Abstract. Understanding what is important to know about harmonics can be challenging for those without extensive electrical engineering backgrounds. In this three part series, this first article will review what a harmonic is, the second will help to clarify what those important facts are, and the third will provide details on what causes harmonic problems and suggested solutions.
Why a Sine Wave
Before defining what a harmonic is, it is useful to review why electrical power is generated in the form of a sine wave, as shown in Figure 1. In much of the world, an AC generator is used to produce power. AC, or alternating current, was chosen back in the 1800s over DC, or direct current, due to its ease of generation and the ability to change amplitude using transformers. [1] The key to understanding a sine wave is in understanding what it is that is “alternating.”
The principle in most AC generators is that by rotating a magnetic field over coils or windings, an alternating electric current will be induced into the windings. The current (or electrical force) is proportional to the magnetic flux (magnetic force), and the voltage (electrical potential) is proportional to the rate of change of the current. If there was no change or alternating of the magnetic flux and hence no change in the current, then there would be no voltage produced.
Figure 1. Sine Wave
A mechanical force, such as water, steam or wind, is used to provide the rotation to produce this changing flux. Figure 2 is a cross-section of a three phase, 2 pole generator. Half of the windings for each phase are located on opposite sides of the stator, or stationary part of the generator. When these coil pairs (A+/A-, B+/B-, C+/C-) are joined together, the current can flow through the circuit of the windings. In the center is the magnet, which has a north and south pole. The magnetic flux gets stronger as the rotating pole gets closer to the coil, and then reduces in intensity as it goes past. The north pole makes the current flow into one coil and the south makes it flow out of the other. In some generators, the magnets are actually electromagnets, not permanent magnets.
Figure 2. Cross section of three phase, two pole generator.
Why the voltage is a sine wave is best illustrated by looking at the phasor diagrams in Figure 3. As the phasor rotates around the circle (like the magnets rotating inside the generator), the position of the end of the phasor in the y axis is shown in Table 1. This is done in 15 degree steps in this example to save space.
Figure 3. Phasors
Table 1. Phase Angle and Magnitude values
Position
Phase Angle
Y axis value
A
0 degrees
0
B
15 degrees
0.259
C
30 degrees
0.5
D
45 degrees
0.707
E
60 degrees
0.866
F
75 degrees
0.966
.
The rotational position (in degrees) is related to an incremental step in time. Plotting the y axis values corresponding to the position steps over a complete 360 degree circle results in an approximation of a sine wave that was shown in Figure 1. This sine wave function occurs in many natural phenomena, such as the speed of a pendulum as it swings back and forth, or the way a string on a guitar vibrates when plucked.
The frequency of the sine wave is proportional to the number of poles (or magnets) and the speed of the rotation, usually expressed in ‘rpm’ (revolutions per minute). The equation is f = ( p/2 )*rpm. This frequency is referred to as the fundamental frequency. In the North America, this frequency is 60Hz, or cycles per second. In European countries and other parts of the world, this frequency is usually 50Hz. Aircraft often use 400Hz as the fundamental frequency. At 60Hz, this means that sixty times a second, the voltage waveform increases to a maximum positive value, then decreases to zero, further decreasing to a maximum negative value, and then back to zero.
What is a Harmonic
The knowledge of harmonics has been around for a long time. In fact, musicians have been aware of such since the invention of the first string or woodwind instrument. Harmonics (called “overtones” in music) are responsible for what makes a trumpet sound like a trumpet, and a clarinet like a clarinet. It can be shown that any complex waveform, whether it is produced by a musical instrument or a power system, can be broken up into harmonic components.
The typical definition for a harmonic is “a sinusoidal component of a periodic wave or quantity having a frequency that is an integral multiple of the fundamental frequency.” [2]. Some references refer to “clean” or “pure” power as those waveforms without any harmonics. Today, such clean waveforms typically only exist in a laboratory.
The harmonic frequencies are integer multiples [2, 3, 4, …] of the fundamental frequency. For example, the 2nd harmonic on a 60Hz system is 2*60 or 120Hz. At 50Hz, the second harmonic is 2*50 or 100Hz. 300Hz is the 5th harmonic in a 60Hz system, or the 6th harmonic in a 50Hz system. Figure 5 shows how a signal with dominant 5th and 7th harmonics would appear on an oscilloscope-type display, which some power quality analyzers provide.
Figure 5. Fundamental with 5th and 7th harmonics
Frequencies that are not integer multiples of the fundamental frequency are called “interharmonics ”. There is also a special category of interharmonics, which are frequency values less than the fundamental frequency, called subharmonics. The presence of sub-harmonics is often observed by the lighting flicker.
One other parameter to be aware of is the phase angle of the harmonic relative to the fundamental. In Figure 6, a third harmonic with an amplitude of 33% of the fundamental is combined with the fundamental. In the left hand picture, the fundamental and the third harmonic are in phase. In the right hand picture, they are 180 degrees out-of-phase with each other. Obviously, the resulting waveform looks quite different.
Figure 6. Effect of Harmonic Phase. [4]
References
[1] Fitzgerald, A.E. et al, Electric Machinery, McGraw-Hill Company, 1971. [2] IEEE 519 Recommended Practices and Requirements for Harmonic Control in Electric Power Systems [3] Kerchner, Russel M. And George F. Corcoran, Alternating-Current Circuits, John Wiley & Sons, NY, 1 943. [4] Powerline Harmonic Problems – Causes and Cures, Dranetz Technologies, December 1994.
About the Author: Richard P. Bingham is currently the Chief Technologist for Dranetz Technologies, Inc., having previously been the Vice-President of Engineering and Strategic Planning. He has been with the company since 1977, following completion of his BSEE at the University of Dayton. Richard also has an MSEE in Computer Architecture and Programming from Rutgers University. He is a member of IEEE Power Engineering Society and Tau Beta Pi, the Engineering Honor Society. Richard is currently working with the NFPA 70B committee on Power Quality and several IEEE committees related to IEEE 1159, and has written and presented numerous papers and seminars in the electric utility and power quality instrumentation fields.
Published by Igor PETROVIĆ1, Zdenko ŠIMIĆ2, Mario VRAŽIĆ2, Technical College in Bjelovar (1), University of Zagreb, Faculty of Electrical Engineering and Computing (2)
Abstract. The object of this research is to compare three of the most popular conventional analytical models used for estimation of electrical energy production of photovoltaic panels. From this analysis a single model will be selected with the best characteristics for implementation of modifications and corrections in order to get better energy production prediction results. Monthly and annual production results and errors will be the main criteria for the selection of a single model. Single prediction results of the selected model should be as accurate as possible in the smallest time periods, which are in this case monthly energy prediction results. This should guarantee that annual results are also rather accurate.
Streszczenie. W artykule porównano trzy modele analityczne umożliwiające analizę energii elektrycznej wytwarzanej przez panele fotowoltaiczne. Analizuje się miesięczną i roczną produkcję energii na podstawie wybranych okresów czasowych. (Porównanie metod przewidywania produkcji enegii przez panele fotowoltaiczne)
Keywords: PV plant, conventional analytical model, electrical energy production. Słowa kluczowe: ogniwa fotowoltaiczne, prognozowanie produkcji energii
Introduction
Accuracy of conventional analytical models used for estimation of electrical energy production of photovoltaic panels and systems is the main characteristic that determines tool expediency. Conventional analytical models are mathematical methods which use theoretical values and estimated relations between energy production and hydrological conditions in the surroundings of the production system ([1], [2]). These assumptions are made on perennial average values for a specific location. In average cases, error estimation from the modelled values and specific annual production can drop over 10%. The main task of this research is to take results of the conventional analytical model from the actual measured input data for a specific location (solar radiation and temperature) and compare them ([3], [4], [5], [6]) with the real measured energy production. One will be able to use the analysis of these results to implement corrections in order to improve conventional analytical model results towards the real measured values ([7], [8]). Conventional analytical model energy production estimations are made for a commercial photovoltaic energy plant, which has measuring data bases for a whole year. The selected tools are three of the most popular software tools: the Homer, the PVSYST and the PVGIS. The same set of data is used for all three production estimations, which is calculated from the measured values in the database of the PV plant.
Approach to PV plant energy generation prediction
It can be assumed that by predicting only radiation and temperature, energy production prediction for a PV plant can be made inside a certain error span ([9], [10]). Errors are determined by a range of conditions that are neglected in the specific analytical model. Other data come from construction characteristics of the PV plant, which in this case cannot be altered since the PV plant is already built and running. Data for determining the subject PV plant and conventional analytical models are presented in the following sections.
The Solvis SE PV plant (Fig.1) is located in Varaždin in the north of Croatia, with geographical coordinates 16.3245° east and 46.3245° north and elevation of 170 m. The climate is temperate continental. The PV plant consists of 96 PV modules with 215 W of electrical power, which are installed in a fixed mode and connected to the commercial electrical energy distribution network. Efficiency of the DC/AC inverter is 96%. The PV plant DC power is 20.64 kWmpp defined for 1000 W/m² irradiance on the PV modules surface and temperature of 25°C. The PV plant orientation is not optimal. The azimuth is set to 0° (south) and inclination to 70°. The albedo is estimated as 0.26.
Fig.1. The Solvis SE Varaždin
The real electrical energy production is measured and the results are written in the PV plant database. The measured values consist of data from the PV system, grid consumption and physical data from the surroundings such as global horizontal irradiance and ambient temperature. The available measurement time period was from 1 March 2011 to 7 March 2012 and represents a whole year. The used data necessary for analysis of conventional analytical model of energy production prediction are presented in Table 1.
The software solutions for calculating energy production are most used tools in PV plant planning. The mathematical model of the PV module in ambient conditions describes the real state of the PV plant which is expected at a selected location. This description consists of various parameters which include some estimated values for defining the PV plant energy production. Ambient influence models affect the PV modules energy production results for average or specific input data. The most common professional software solutions for predicting PV plant energy production are the Homer ([11], [12], [13], [14]), the PVSYST ([15]) and the PVGIS ([3]). The installation mode in this research is set to fixed installation. The input hydrological data for the specific location can be calculated from the PV plant database. An average day cumulative daily irradiances and average monthly temperatures were used as input data in the software tools.
Table 1. Featured measured values of the Solvis SE
.
t – time of data acquisition H – global radiation on horizontal plane T – ambient temperature Ex– cumulative energy production by xthinverter Px– electrical power of xth invert
The used models calculate final energy production by using different algorithms. The Homer calculates energy in two steps based on input data for average irradiation and temperature. In the first step synthetic hourly data are calculated from an average day cumulative daily irradiances and average monthly temperatures. The Liu-Jordan-Klein model is used for transferring the global horizontal irradiance onto the sloped surface. In the second step the PV plant electrical power is modelled from the sloped surface irradiance and ambient temperature. A selection of most common PV modules, inverters and batteries is available in Homer’s equipment catalogue. Produced energy is calculated on the basis of cumulative hourly electrical power. The PVSYST model uses the same input data sets as the Homer model. A transposition model is used for calculating the effective irradiation on the sloped surface from estimated global, diffuse and reflected components of irradiation. The PVSYST offers a selection of two transposition models: the Hay’s model or the Perez model. The Hay’s model is robust and does not require the exact value of diffuse irradiance. The Perez model is more sophisticated, but needs quality data measured on a horizontal surface. Every component is separately calculated with a transposition model. These calculations are made on synthetic hourly data for a clear sky average day of the month. The errors which occur in such calculations are also dependent on azimuth and inclination of the PV modules. Average errors are all in range from 1.1% (maximum for 0° of azimuth and 0° of inclination) to 11 % maximum for ±90° of azimuth and 90° of inclination. The PVGIS is a very empirical model developed for European locations. The input data is irradiance which is developed from the database for Europe. R.sun and s.vol.rst models are used for interpolation. The algorithm consists of estimation for direct, diffuse and reflected irradiation components for the clear sky, and also global real irradiance on a horizontal or sloped surface. Irradiation is calculated by integrating hourly irradiance. Databases have measured data for daily global irradiation for horizontal and sloped surfaces (15°, 25° and 40°). Also, raster maps of 1x1km cell resolution with clear sky irradiation, linke turbidity and ratio of diffuse to global irradiance are computed. The main source of data is presented in the European Solar Radiation Atlas. The albedo used in PVGIS is constant and equal to 0.15. Energy production is estimated from history data of power production measured on PV stations across Europe, installed with inclinations of 15°, 25°, 40° and 90°. Therefore, it should be noted that classic PVGIS model can only generate results based on the measured data, and has a very small modelling contribution.
Analysis of PV plant energy generation prediction for conventional analytical models
The input data for modelling of PV plant energy generation with conventional analytical models are generated from the PV plant database. The input values are presented in Table 2 for each month from March 2011 till February 2012.
Table 2. Input data for the Solvis SE in Homer model
.
A comparison of conventional analytical models and measured results is presented in Fig. 2. The presented measured data were acquired from March 2011 to March 2012, and represent a whole year. For better visual interpretation of results January and February 2012 values are moved in front of the 2011 measured values, although they were recorded in 2012.
It can be seen that most of monthly modelled results have significant errors. In most cases the modelled results are also somewhat different from each other. Monthly errors for conventional analytical model results are presented in Fig. 3. All results were compared with measured energy generation results.
Monthly energy production model errors are presented for each model regarding the measured values. The PVGIS result errors generally underestimate energy production. These results are expected considering the empirical modelling which is affected by the used equipment. The equipment used for the selected PV plant is not used in the PVGIS model. The PVSYST and the Homer monthly errors are rather significant in some months, but they also oscillate around zero during the one year period. The Homer model results have five monthly absolute result errors smaller than 500 kWh, while the PVSYST has only two months in that range. Therefore, it can be concluded that synthetic modelling of hourly data used by the Homer is more accurate than the one used in the PVSYST model. While the PVGIS calculates energy production results from empirical data, the Homer and the PVSYST model use synthetic hourly data from monthly averages. In the synthetic data temperature values are used as a constant for every hour, and have a value of monthly average. Modelling factors are also calculated from average annual data. Errors are partly caused by the measured period which was not close to annual averages.
Fig.2. Monthly energy generation prediction and measured results for the Solvis SE
Fig.3. Monthly energy generation absolute errors for the Solvis SE
Table 3. Annual energy generation for the Solvis SE
.
Cumulative annual energy results for the Homer, the PVSYST, the PVGIS and the measured results are presented in Table 3. Relative energy production errors are also presented in comparison with the measured energy of the PV plant. The greatest annual energy production error is the one made by the PVGIS model. The PVSYST model has annual energy production error under 1% and is the most accurate. The Homer annual result is also rather accurate in comparison with the PVGIS model result. Therefore, it can be concluded that the Homer and the PVSYST models predict annual energy production with the acceptable level of precision.
Conclusion
A comparison of each model with the measured monthly results shows that all models can have significant monthly and/or annual errors in energy production estimation. While the PVSYST and the PVGIS both have multiple monthly errors over 100% of the measured energy production in a given month, the Homer never exceeds that percentage of error for each month in the given year. It can also be seen that all model calculations for warm weather are lower than real energy production, while in cold weather model results are always higher than real energy production. The PVSYST calculated the most accurate annual results, while the Homer and the PVGIS have some errors. When all of these characteristics combine, the Homer proves to be a rather good model with some deficiency which must be considered. The Homer model has been selected for implementation of corrections that will result in better hourly predictions based on its single monthly predictions. These corrections should finally result in better daily, monthly and annual energy production predictions.
REFERENCES
[1] R. Chenni, M. Makhlouf, T. Kerbache, A. Bouzid: A detailed modeling method for photovoltaic cells, Energy 32 (2007), pages 1724–1730 [2] T. Kerekes, E. Koutroulis, S. Eyigün, R. Teodorescu, M. Katsanevakis, D. Sera: A Practical Optimization Method for Designing Large PV Plants, ISIE 2011, 2011 IEEE International Symposium in Industrial Electronics, Poland, 27-30 june 2011, pages 2051 – 2056 [3] André Coelho, Rui Castro: Sun Tracking PV Power Plants: Experimental Validation of Irradiance and Power Output Prediction Models, International journal of Renewable energy research, Vol.2, No.1, 2012 [4] Ahmet Senpinar, Mehmet Cebeci: Evaluation of power output for fixed and two-axis tracking Pvarrays; Energy 92, Elsevier Ltd., 2012, pages 677-685 [5] Steve R. Best, Julie A. Rodiek, Henry W. Brandhorst Jr.: Comparison of solar modeling data to actual pv installations: power predictions and optimal tilt angles, 37th IEEE Photovoltaic Specialists Conference (PVSC), 2011, pages 1994-1999 [6] Ali Naci Celik, Nasır Acikgoz: Modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules using four- and five-parameter models, Applied Energy 84, 2007, pages 1 – 15 [7] E. Kymakis, S. Kalykakis, T. M. Papazoglou: A photovltaic park’s performance on the island of Crete, Energija 57 (2008), Nr. 3, pages 300-311 [8] Francisco Javier Gómez-Gil, Xiaoting Wang, Allen Barnett: Energy production of photovoltaic systems: Fixed, tracking, and concentrating, Renewable and Sustainable Energy Reviews 16, Elsevier Ltd., 2012, pages 306– 313 [9] R. Pašičko, Č. Branković, Z. Šimić: Assessment of Climate Change Impacts on Energy Generation from Renewable Sources in Croatia, Generation from RES Croatia, Renewable Energy. 46 (2012) , October 2012; pages 224-231 [10] Matic Z.: Solar radiation in Republic of Croatia, Croatian Energy Institute ‘‘Hrvoje Pozar’’, Zagreb, 2005 [11] Mohammad Saad Alam, David W. Gao: Modeling and Analysis of a Wind/PV/Fuel Cell Hybrid Power System in HOMER, Industrial Electronics and Applications, 2007. ICIEA 2007, Second IEEE Conference on Industrial Electronics and Applications 2007, pages 1594 – 1599 [12] Nurul Arina bte Abdull Razak, Muhammad Murtadha bin Othman, Ismail Musirin: Optimal Sizing and Operational Strategy of Hybrid Renewable Energy System Using HOMER, The 4th International Power Engineering and Optimization Conf. (PEOCO2010), Shah Alam, Selangor, MALAYSIA: 23-24 June 2010, pages 495 – 501 [13] Kandula Murali Krishna: Optimization Analysis of Microgrid using Homer- A Case Study, India Conference (INDICON), 2011 Annual IEEE 2011, pages 1 – 5 [14] T. Givler and P. Lilienthal: Using HOMER® Software, NREL’s Micropower Optimization Model, to Explore the Role of Gensets in Small Solar Power Systems, Case Study: Sri Lanka, Technical Report, NREL/TP-710-36774, May 2005. [15] Sun Jianping: An optimum layout scheme for photovoltaic cell arrays using PVSYST, International Conference on Mechatronic Science, Electric Engineering and Computer, August 19-22, 2011, Jilin, China, pages 243 – 245
Authors: Igor Petrović, B.Sc., Technical college in Bjelovar, Trg Eugena Kvaternika 4, 43000 Bjelovar, Croatia, E-mail: ygor.petrovic@gmail.com; prof. dr. sc. Zdenko Šimić, University of Zagreb, Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, E-mail: zdenko.simic@fer.hr; doc. dr. sc. Mario Vražić, University of Zagreb, Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, E-mail: mario.vrazic@fer.hr.
Source & Publisher Item Identifier: PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 89 NR 6/2013
Published by Rakesh Kumar, EE Power – Technical Articles: 6 Critical Design Challenges in DC Fast Chargers, February 09, 2023.
DC fast chargers pose significant challenges to a power grid. Know the six challenges while designing a DC fast charger to overcome the various power quality challenges.
The design of a DC fast charger is based on several internally running and externally connected control loops of a power grid. Some of the controller loops are the phase-locked loop (PLL), current control (CC) loop, and direct voltage control (DVC) loop. Additionally, the design of an EMI filter and the modulator that controls the PWM signals is also important.
Figure 1 shows a comprehensive view of the interconnection of different controller loops of a DC fast charger. Each of these loops is further discussed in this article with their design challenges that must be taken care of to address the power quality issues of a power grid.
DC Fast Charger Startup Scheme
A DC fast charger handles a large amount of power to charge an electric vehicle. This means that an abrupt charging or discharging of an electric vehicle can suddenly disrupt a power system. The case is particularly severe when multiple such electric vehicles are connected. In such a scenario, the power handled is quite large, which can lead to flickering. Therefore, a good startup scheme is necessary for smoothly handling the large power of a fleet of electric vehicles.
A possible solution is to follow a ramp-type start-up of an electric vehicle charging. A ramp-type approach refers to a linearly charging way of electric vehicles, and it has many benefits compared to a step-type charging of the electric vehicle. An energy storage system such as a battery can help alleviate power quality issues for such a smooth power-building. Using an energy storage system offers the necessary bandwidth of the controller to achieve ramp-type charging of the electric vehicle. The power rate at which an electric vehicle is charged also depends on the command issued by a distribution system operator.
Phase Lock Loop
A feedback control system known as a PLL block is responsible for automatically adjusting the phase of a locally generated signal to match the phase of an input signal. A converter’s impedance is impacted due to the PLL, mainly when the frequency range is low. Negative damping may be injected into a power system when the PLL leads to negative resistance at some frequencies. The negative resistance also causes harmonics and inter-harmonics in the power grid to increase. This is because of the weakening damping of frequencies that are dependent on the negative resistance. Such a situation, when unchecked, can potentially lead to complete harmonic instability.
A possible solution to this phenomenon is to check for PLL’s bandwidth. It is suggested that the PLL’s bandwidth be kept at low frequencies in the range of a few Hz. Therefore, the negative resistance offered by a PLL can be taken care of. Flickering can also occur due to PLL issues if inter-harmonics have less than double the fundamental frequency. Therefore, PLL dynamics and frequency bandwidth are important to DC fast chargers.
Direct Voltage Control
The DVC loop takes in the dc voltage and a reference dc voltage to generate a reference current signal. The signal forms a basis for the following current control block. The bandwidth of the DVC loop is also narrow, and it resembles the bandwidth of PLL. When the grid conditions are weaker, it decreases the stability of the DVC loop. The stability of the DVC loop is also dependent on other factors, such as the input power of the voltage source converter. The DC fast charger is another factor that determines the stability of the DVC loop.
As discussed with the PLL, negative damping is also introduced by the DVC loop for the low-frequency range. Hence, negative damping leads to issues such as flicker and harmonics. A good DC fast charger should take into account the design of DVC to mitigate the power quality challenges arising. The DVC loop should respond well to weaker grid conditions, and it is important that it can synchronize with other control loops in the system.
Current Control
The CC loop is at the heart of the controller design because its inputs its signals from the DVC and PLL loops. Unlike the previous two cases, the CC loop deals with higher frequencies. If the interaction between the PLL and CC loop is not synchronized with each other, it again leads to the system’s instability. This problem can be further addressed by keeping a check on the bandwidth of PLL and keeping it to a low range.
Resonant controllers are also a good solution to operate with the CC loop. When the need arises to eliminate particular harmonics, resonant controllers can achieve the same. Another way a power grid can slip into instability is the effect of multiple CC loops operating together. When multiple electric vehicles are charged together in a DC fast charger station, the parallel operation of multiple such converters can cause instability of the power system.
Input Filter
The ripple injected into the grid can be attenuated with the help of input filters. The switching frequencies of such ripple can vary from 2 kHz to 150 kHz. The input filters are usually in the form of an L-type or LCL-type filter. When the inductances used in both filters are the same, it is observed that the LCL-type filter tends to perform better. But an LCL-type filter poses additional zeros and poles, which becomes a cause of concern from the system stability point of view. But the LCL-type filter is still considered the optimal solution because of its matured technology.
The grid impedance condition is unique to each type of grid; therefore, the design of a DC fast charger is also unique to the specific grid it is built upon. When a DC fast charger is connected to a grid with a different grid impedance, it will change the resonance peak of the LC filter employed. If the CC loop is designed, so the bandwidth is high, it can still lead to system stability because of the change in grid impedance. One way to minimize instability risk is to employ active damping methods.
Modulator and EMI Filter
The modulator is an essential component of a DC fast charger responsible for managing the charging current and voltage provided to the battery. This is accomplished by modulating the signal sent from the charger to the battery. The modulator will normally use a DC-DC converter to adjust the voltage of the charging current according to the system’s requirements. Additionally, it may use pulse width modulation (PWM) or other control methods to regulate the charging current. Sideband frequency oscillations in the range of 2 to 150 kHz can be induced if the proper PWM synchronization design is not properly taken care of.
EMI filters perform their function by obstructing or dampening the transmission of high-frequency signals produced by the charger. These signals are often created by switching power transistors or switching the DC-DC converter that is used to step up or down the voltage of the charging current. Both of these processes are employed to step the charging current voltage up or down. EMI filters are normally located either at the input or output of the charger, and they can either be passive or active.
Figure 2 summarizes and illustrates the different design challenges, their related issues, and the frequency range for which the design challenges are relevant.
• When it comes to recharging an electric vehicle, a DC fast charger is capable of handling significant amounts of power. Using a system for energy storage provides the controller with the necessary bandwidth to accomplish ramp-type charging of the electric vehicle.
• Another element that plays a role in determining the DVC loop’s degree of stability is the DC fast charger. A suitable DC fast charger should consider the DVC design to help reduce the power quality difficulties that may arise.
• With input filters’ assistance, the ripple pumped into the grid can reduce its volume. Typically, the input filters take the shape of an L-type or LCL-type filter. The resonance peak of the LC filter used will shift if a DC fast charger is connected to a grid with a varied grid impedance.
• The transmission of high-frequency signals produced by the charger is impeded or dampened by EMI filters, which allows the filters to fulfill their intended purpose. Either the input or output of the charger is the typical location for EMI filters, and these filters can either be passive or active.
Author: Rakesh Kumar holds a Ph.D. in Electrical Engineering with a specialization in Power Electronics from Vellore Institute of Technology, India. He is a Senior Member of IEEE, Class of 2021, and a member of the IEEE Power Electronics Society (PELS). Rakesh is a committee member of the IEEE PELS Education Steering Committee. He is passionate about writing high-quality technical articles of high interest to readers of the EE Power Community. You can email him at rakesh.a@ieee.org.
Published by Andrzej ERD1, Józef STOKLOSA2, University of Technology and Humanities In Radom(1), University of Economics and Innovation in Lublin(2)
Abstract. The purpose of the publication is to indicate activities aimed at improving the reliability of electric vehicles. The starting point is the analysis of the most common failures of both assemblies and their components. On this basis, the most common reasons for their appearance have been identified for each group. The work indicates suggested steps aimed at reducing the intensity of damage to the systems that are part of electric vehicles.
Streszczenie. Celem publikacji jest wskazanie działań mających na celu poprawę niezawodności pojazdów elektrycznych. Punktem wyjścia jest analiza najczęściej występujących uszkodzeń zarówno zespołów jak i ich elementów. Na tej podstawie zostały wyodrębnione dla każdej z grup najczęstsze przyczyny ich pojawiania się. W pracy wskazano sugerowane kroki zmierzające od zmniejszenia intensywności uszkodzeń systemów wchodzących w skład pojazdów elektrycznych. (Czynniki wpływające na powstawanie uszkodzeń elementów i układów elektronicznych w pojazdach elektrycznych oraz działania mające na celu zmniejszenie ich znaczenia)
Keywords: failures of electronic circuits, electronic components, electric vehicle, improvement of quality electronic systems Słowa kluczowe: uszkodzenia układów elektronicznych, elementy elektroniczne, pojazdy elektryczne, poprawa jakości systemów elektronic
Introduction
The intensive development of electric cars started in the 20th century caused a dynamic increase in the number of electronic components and systems installed in the vehicle.
While in the initial period the number of electronic components was relatively small and mainly electromechanical components dominated, at the moment the majority of control functions are supported by electronic systems. Unfortunately, most systems are unreliable and damage occurs, sometimes even resulting in tragic events. With time, the quality of the components has improved, as a result of which both the span of time until the first damage and the span of time between failures in reference to both individual elements and systems have increased.
The degree of complexity of the systems has increased enormously over time. New layout functions have been created, including support and optimization of mechanical components, initially such as ABS and electronic ignition. Slightly later, ASR or ESP traction control systems appeared.
Active and passive safety systems such as LDW (Lane Departure Warning), PD (Pedestrian Detection), or PCAM (Pedestrian Crash Avoidance / Mitigation), RSR (Road Sign Recognition) and FCW (Forward Collision Warning). The peak achievements at the present time include navigation systems or autonomous pilot systems being tested. The emergence of hybrid (HV) and fully electric (EV) cars forced the introduction of high-power and high-voltage electric motor control systems into vehicles. The occurrence of high voltage on the car carries the risk of electrical shock [1].
Damage to Electronic Systems
Damage to electronic systems can be divided into catastrophic and non-catastrophic. Catastrophic are those in which the device stops working completely. Non-catastrophic damage [2,3] occurs when the device is still electrically functioning but the parameters change and the functionality is reduced. Depending on the duration, permanent damage is distinguished, ie. the device degrades permanently. The second group is transient damage, ie. the change of the leading parameter characterizing the error of operation occurs in a random manner in time. As stated in [3] causes of transient failures can be divided into:
• Design errors • Manufacturing errors in the production phase • Temporary short circuits • Disappearing connections • Interference with other systems • Wear or corrosion on connections • Temporary short circuits
The above-mentioned causes of transient failures, in the paper [3] refer to the connections of electronic circuits.
System connectors are one of the most sensitive elements of vehicles. Other combinations of cover materials should also be explored. A compromise is therefore needed between good electrical and mechanical properties on the one hand, and reasonable prices on the other.
While examining complete systems, this list of causes should be complemented with:
• Defects and damage to components • Changes in the parameters of internal components beyond the limits provided for in the construction. • Occurrence of working conditions not provided for in the construction.
The first two factors mentioned above may be caused either by an internal defect not revealed during the final quality control of the elements, or by aging and change of the internal structure. The last of them is related to the construction assumptions, in some cases enforced by international standards, in others to the emergence of an unlikely external situation.
Damage detection is a separate and widely studied issue [4,5,6,7,8]. It is easier in the case of devices with many internal signals, and based on their measurements it is possible to observe changes in the technical condition up to the degradation of the device, inclusive The general practice with regard to car systems is to register the occurrence of operating errors reported by their controllers to the Powertrain Control Module(PCM) of the vehicle. The operation of the PCM module is complemented by Body Control Module (BCM) whose task is to control the windows, wipers, air conditioning, seat settings, central locking, internal and external lighting. The division due to the place of damage to electric vehicles shows that they most often appear in the systems indicated in Table below.
Table 1. Percentage structure of damages in vehicles electronics systems [6]
.
It is worth noting that critical damages in the engine group and steering for vehicle movement are relatively less frequent than eg. in the Audio group.
Knowledge of the system responsible for damage is important from the point of view of the service because it usually exchanges entire modules for repair without repairing them. However, the knowledge of how the single components of modules are damaged, allows to draw conclusions about the methods of improving the reliability of systems and vehicles.
Damages to Electronics Components
The range of elements used in car electronics is very wide. In the paper [8] an example is given that in a premium car there are more than 800 integrated circuits. Thus, a detailed analysis, broken down into individual groups of elements, will be omitted due to the size of the issue, only important conclusions will be cited. The factors influencing the change of the technical condition of the elements are particularly important.
Passive elements are generally durable, however, as the authors of the paper [9] indicate, an increase in the resistor temperature of 35°C causes a double increase in the intensity of damage (ID). Similarly for capacitors, the temperature increase of 15°C results in the ID also being doubled. In addition, an increase in temperature, especially in electrolytic capacitors. Particularly dangerous are the changes in capacitance of capacitors in drive controllers for EV drive motors, then the switching conditions of the IGBT transistor change.
Low-voltage inductive components are not very susceptible to damage due to the increase in temperature, in contrast to high-voltage induction components found, for example, in battery charging modules. For glass-epoxy laminates, an increase in temperature from 25 to 70 °C results in a drop in vertical resistance of over 14 times.
Damages to Semiconductors
Semiconductor components are also very susceptible to temperature changes. Depending on the temperature range conduction phenomena have different character. In the lower temperature range, the ionisation of the dopants is exponentially dependent on the temperature, hence the conductivity increases exponentially, in the middle range most of the dopants are ionized and the changes depend only on the mobility of the carriers. In the upper temperature range, an intense generation of an electron-hole pair occurs, depending on the temperature exponentially. This causes a rapid increase in the thermal leakage current.
In addition to changes in conductivity of the semiconductor, there are also changes in the voltage of the conductivity of the PN junction and the amount of backbreaking voltage. The critical temperature values depend on the material. In the paper [10], it was indicated that in semiconductor devices the deviations from the norm may appear suddenly or be predicted. Sudden changes in the technical condition are caused by overvoltages, mechanical damage or a puncture of the insulation inside the system. Progressive degradation is the result of drift of electrical parameters, electromagnetic interaction. Detection of systems with deteriorated properties takes place during testing. Finding the right boundary value that classifies systems as fit / unfit is a matter of a compromise between high parameters and high losses in the production process.
Damages to MEMS
A relatively new group of elements are MEMS (Micro Electro Mechanical System). They are used as sensors (accelerometers, gyroscopes, vibration sensors, pressure meters), and actuators. Due to their structure, they have some characteristic types of damage, caused by:
Exceeding the limit value of static friction force – Static friction (in English literature term Stiction, derived from Static Friction) is the mutual attraction force that occurs between two very close bodies when they do not move relative to each other. As long as the static friction force balances the external force affecting the body, the body remains motionless. The static friction force increases as the value of the external force increases until it reaches its maximum value.
As a result of changes in the internal structure of the system, the static friction force may increase and the device is not able to start correct operation. To prevent this, designers use elements that prevent excessive approach.
Mechanical shocks – they are an element accompanying the operation of vehicles and their occurrence is inevitable, however, the impact on the elements of electronic equipment must be minimized. With respect to MEMS elements, temporary acceleration of the delay exceeding the permissible value may be destructive for them and cause the structure to detach from the ground, jamming of moving elements. This is particularly important due to the fact that MEMS elements are often components found in both passive and active safety systems.
Electrostatic discharges – (ESD – Electrostatic Discharge) The presence of moving parts is associated with jumps of electric charge and this is part of the normal operation of the system, however, their repeated occurrence can lead to local melting of the contacts, or in the event of additional external voltages to breaking through the structures of semiconductor systems or internal insulation e.g. of capacitors.
Micro-Contamination – This is a phenomenon related to the occurrence of undesirable particles inside the housing. Although the production of MEMS elements takes place in a clean atmosphere, and after its completion, these elements are tested, however, in particular cases individual particles may not be detected.
It is also possible for gas particles to get inside the MEMS housing, This can lead to a change in the surface properties of internal structures.
Reduction of internal friction and contamination is only possible during the production phase. The impact of vibrations during operation should be kept to a minimum and the condition of their use in the vehicle is to place them in places that allow them to survive in the event of a collision.
The protection of MEMS systems against ESD damage is not significantly different from other electronic systems. It is necessary to take care of proper storage conditions before and during assembly, as well as to maintain at the design stage, appropriate track spacing and appropriate width of high-current tracks.
Directions of Improvement of Reliability Parameters
Elimination of most external exposures occurring in vehicles such as temperature, vibrations, strong electromagnetic fields is impossible. However, it is possible to limit their influence by design measures/constructional treatments.
Analysis of transient failures indicates that the occurrence of many of them is the result of malfunctioning joints. Among the design measures that can bring a definite improvement, we should mention: anti-vibration designs of electronic boards, making connectors, in particular those in contact with the environment in a hermetic manner. The use of cables with increased insulation; the use of double insulation cables in high voltage systems; shielding electrical machines and their power cords to limit electromagnetic interference; proper selection of components.
Batteries and Battery Management Systems (BMS)
The electric cells used in batteries even though they were invented more than 100 years ago are far from perfect. Disadvantages of the cells, such as a long charging time, a large mass necessary to accumulate enough energy to move, the content of heavy metals can hardly be considered damage. However, the cells age with time which causes, inter alia, a decrease in the capacity and increase in series resistance.
At present, in electric vehicles the main power source is composed of modules in which individual cells are connected in parallel, and these in turn are combined into packages. (Battery Pack). A dozen of these types of elements are usually connected in series into a complete power supply system. Single cells have a large number of limit parameters, the exceeding of which may affect [12]
• Design errors • Manufacturing errors in the production phase • Exceeding the permissible operating temperature. • Reduction of cell life. • Destruction of the cell • Self-ignition and threat to the safety of entire vehicle.
The basic parameters that cannot be exceeded are presented in [12]. In view of such a limited area of correct work, it is necessary to carry out supervision over loading and unloading, which is the role of Battery Management Systems (BMS). Each Battery pack has its own BMS called slave here, and these in turn communicate with the master BMS related directly to the MCU (Master Control Unit). BMS systems, in addition to the current supervision over the control flowing through the cells, still have several prognostic tasks. In particular, they must provide the main control unit of the vehicle with information on the stored and possible to consume quantities of energy and the forecasted lifetime of the battery’s.
The algorithms for this purpose are based on the determination of SOC – State of Charge and SoH-State of Health indicators, which are currently determined by BMS.. SoH is of a diagnostic nature and is used to determine if the batteries are fit for further use, and what their degree of wear is. SoH strongly depends on the number of charging cycles and also on the SoC value at which recharging was initiated [14]. The method of determining the size of SoH is not standardized [13].
It should be added that SoC significantly depends on the temperature of the cell [18] and on the way of loading. The state of battery consumption is understood as the level of degradation of the battery, which allows you to recover at full charge up to 80% of the energy that was recovered in the initial state. It follows that the battery is not completely useless but still it has deteriorated parameters.
This kind of approach on the one hand allows for more reliable short-term operation, but on the other hand indicates the need to replace the battery, despite the fact that it still has a significant operational potential. As shown in [13], the consumption of individual cells in the module may be uneven and over time the discrepancy of parameters becomes deeper.
Batteries are usually made as non-removable not only by the user but also by car services. However, after dismantling, it turns out that the cells are connected in modules in a parallel manner without elements that align the currents between them. BMS controls the operation of the entire module supposedly of identical cells connected in such a way. So differences in the properties of cells that are immeasurable in the initial period can become visible over time and result in:
Reduced ability to load the module, by prior signalising of reaching the final charging voltage by the less capacious cells.
Crossing the discharge current for less used cells with lower internal resistance (with higher capacity) during loading.
Improvement of Functional Battery
Parameters In connection with this, the following postulates are suggested that may significantly affect the battery life extension.
• Constructing cells with the largest unit capacity to reduce their total number in the power supply system. • BMS supervision of each individual cell, not just cell assemblies connected in parallel. • Making batteries demountable with the possibility of exchanging individual cells or their groups in the service. • Building algorithms for battery charging in a differentiated way for individual component cells. • Strengthening and stiffening the structure of the floor panel covering the batteries.
The first postulate is related to the general progress in the construction and production of cells, but it allows to reduce the number of BMS in the vehicle supply system, and thus reduces the amount of information collected and processed, and further reduces the cost of the supply system, with the same total capacity.
The second postulate reduces the possibility of uncontrolled uneven operation of the cells and prevents, above all, overcurrent. When constructing a power supply system as being composed of several thousand elementary cells, it is difficult for practical and economic reasons to include each of them with the supervision of local BMS and separate balancing. A compromise is necessary and is currently achieved by selecting the number of cells connected in parallel and jointly supervised by one BMS. The number of cells connected in parallel should be as low as possible according to the postulate 1. If necessary, it is possible for the BMS to turn off a heavily used cell and work in a parallel module with a reduced number but in a wider range of capacity cells. Then the work will not be limited by a damaged cell.
Knowledge of the degradation status of each individual cell in the package would allow for the removal of the most worn out cells and inserting in their place cells with characteristics similar to the others, in this way the module would regain a significant potential for exploitation, but this is possible when fulfilling postulate 3.
BMS systems have the ability to supervise the temperature, therefore the central BMS system would have the possibility to load cells with lower temperature more (at high ambient temperatures) and the ability to load coldest cells less at low temperatures, and this approach would result in extended life of individual cells.
A series of vehicle fires following a fairly long period of time after severe damage prompted car manufacturers to recommend discharging lithium-ion batteries after serious failures. However, completely discharging the vehicle’s battery for safety reasons permanently damages the battery and makes it worthless. Self-discharge and parasitic electronic load on the battery management system can also irreversibly discharge the battery[19].
The production of elements and assemblies of electric vehicles is subject to many international arrangements, including those conducted under the direction of the Automotive Electronics Council. First of all, the endurance tests were standardized. Depending on the element groups, additional aging tests are being carried out. The list of tests includes [20].
In addition, there are many standards by the International Organization for Standardization. It would be advisable to introduce a normative requirement for the manufacturer to explicitly provide electronic components and systems indicators of damage reported as warranty and as replacement parts.
REFERENCES
[1] Fres chi F,Mi tolo M. , Tommasini R. Electrical Safety of electric vehicles 2017 IEEE/IAS 53rd Industrial and Commercial Power Systems Technical Conference (I&CPS) Niagara Falls ON, 2017, pp. 1-5. [2] Ahmad W.,Perrinpanayagam S.,Jennions I.,Khan S. Study on Intermittent Faults and Electrical Continuity. 3rd International Conference on Through-life Engineering Services Nov 2014, pp 71- 75. [3] Correcher E., Garcia E., Morant F., E. Quiles, L. Rodriguez , Diagnosis of Intermittent Faults and its dynamics. First Publication: 2008 IEEE International Conference on Emerging Technologies and Factory Automation. [4] Gandoman F., Ahmadi A, Van den Bossche P, Van Mierlo J. Omar J., Nezhad A, Mavalizadeh H, Mayet C. , Status and future perspectives of reliability assessment for electric vehicles. Reliability Engineering & System Safety Volume 183, 2019, pp. 1-16. [5] Cui J . , Faults Classification of Power Electronic Circuits based on a Support Vector Data Description Method Metrol. Measurement. Systems., Vol. XXII (2015), No. 2, pp. 205–220. [6] Leeman S., Joris K, Latent Reliability Defects in Automotive Chip Packages Automotive Electronics Council Reliability Workshop 2018. [7] Lewitsching H, Electrical Drift of Electronic Devices. 20th Automotive Electronic Consuil Reliability Workshop. Detroit 2018. [8] Drobnik J., Praveen J. Electric and Hybrid Vehicle Power Electronics Efficiency, Testing and Reliability. International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium EVS27 Barcelona 2013, pp. 1-12. [9] Ćwirko J, Ćwi r ko R. , „Badania temperaturowe modułów elektronicznych”. Biuletyn WAT Vol. LVII, NR 2, 2008, pp. 134-142. [10] Lewitsching H. Electrical Drift of Electronic Devices. 20th Automotive Electronic Concuil Reliability Workshop. Detroit 2018. [11] Erd A. ,Stoklosa J . , “Main Design Guidelines for Battery Management Systems for Traction Purposes”. 2018 XI International Science-Technical Conference Automotive Safety, pp. 4. [12] Andrea D. , “Battery Management Systems for Large Lithium-Ion Battery Packs” Artech House 2010 ISBN:9781608071043. [13] Nuhic P,, Bergdolt J, Spier P.,Buchholz M., Dietmayer K. , “Battery Health Monitoring and Degradation Prognosis in Fleet Management Systems” World Electric Vehicle Journal. Vol 2018 (9), pp. 39. [14] Remmlinger J., Buchholz M., Mei ler M., Bernreuter P. , Dietmayer K. , “ State-of-health monitoring of lithium-ion batteries in electric vehicles by onboard internal resistance estimation” J. Power Source 2011, 196, pp. 5357–5363. [15] Tippmann S., Walper D.1. , Balboa L. , Spier B. , Bes sler W. , “Low-temperature charging of lithium-ion cells part I: Electrochemical modeling and experimental investigation of degradation behavior”. J. Power Source 2014, pp. 252, 305–316. [16] S.J. Moura, N.A. Chaturvedi, M. Krstic , “Adaptive PDE Observer for Battery SOC/SOH Estimation via an Electrochemical Model”. ASME J. Dyn. Syst. Meas. Control 2013, 136, pp. 101–110. [17] Nuhic, T. Terzimehic, T. Soczka-Guth, M. Buchholz , K. Dietmayer , “ Health Diagnosis and Remaining Useful Life Prognostics of Lithium-Ion Batteries Using Data-Driven Methods” . J. Power Source 2013, 239, pp.680–688. [18] Remmlinger J . , Tippmann S., Buchholz M. , Dietmayer K. , “Low-temperature charging of lithium-ion cells Part II: Model reduction and application” J. Power Source 2014, 254, pp. 268–276. [19] Erd A., Stoklosa J.,”Failures of electronic systems and elements in electric vehicles and guidelines for reducing their intensity”. 2019 Applications of Electromagnetics in Modern Engineering and Medicine, PTZE 2019. [20] http: //www.aecouncil.com/AEC/Documents.html
Authors: Dr inż. Andrzej Erd University of Technology and Humanities in Radom. Faculty of Transport and Electrical Engineering Radom Poland a.erd@uthrad.pl; Józef Stokłosa University of Economics and Innovation in Lublin Faculty of Transport and Computer Science. Lublin, Poland jozef.stoklosa@wsei.lublin.pl
Source & Publisher Item Identifier: PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 95 NR 12/2019. doi:10.15199/48.2019.12.23
Published by Ali BAGHERI, Mohsen ALIZADEH, Department of Electrical Engineering, Yadegar -e- Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran
Abstract. One of the most important indices of power quality in distribution grids is the harmonic distortion, which can affect the reliability of the micro-grid. On the other hand, the growth of power electronic devices and the emergence of modern industries in distribution grid can led to harmonic distortion emission in the distribution networks. Hence, harmonic analysis of distribution networks has particular importance. Therefore, in the current paper a new method for designing a passive filter is proposed to reduce the harmonics emitted by power electronic devices in a hybrid micro-grid network including nonlinear load, energy storage, wind turbine and solar cell. To do this, a sample harmonic micro-grid is provided in ETAP software which the information needed to design the passive filter is extracted. Then, according to the micro-grid structure and obtained results, the parameters values of the passive filter are determined. Finally, simulation results provided by ETAP software confirm the efficiency of the passive filters as well as the improvement of power quality indices in the micro-grid.
Streszczenie. W artykule zaprezentowano nową metodę projektowania pasywnych filtrów stosowanych do redukcji harmonicznych w sieciach typu micro-grid. Sieć może być obciążona nieliniowym odbiornikiem, zasobnikiem energii I w jej skład może wchodzić turbina wiatrowa oraz panbel fotowoltaiczny. Projektowanie filtrów pasywnych do redukcji harmonicznych w hybrydowej sieci typu micro-grid zawierającej turbinę wiatrową, panel fotowoltaiczny i nieliniowy odbiornik
Keywords: Micro-grid, Power quality, Renewable energy, Passive filters Słowa kluczowee: micro-grid, redukcja harmonicznych, filtyr pasywny
Introduction
Given the current environmental problems caused by the fossil-fuel power plants all over the world [1], the governments is obliged to devote a specific percent of development for its plants to renewable energies such as wind turbines and solar cells [2]. Accordingly, since these plants usually use an inverter in order to connect to the network as well as existing the nonlinear loads current, the injected power by these units are non-sinusoid [3]. For instance, electric arc furnace and photovoltaic are now producing a high amount of harmonic loads [4], [5]. This should be taken into account more seriously when talking about a micro-grid which is a weak electrical system with low short-circuit power. Because the micro-grid cannot bear mentioned issue in contrast to the large power systems. The power quality indices in power systems are:
1) steady state voltage (under- or over-voltage), 2) steady state voltage unbalance, 3) Harmonics, 4) Voltage fluctuations, 5) short-term interruption (sag or swell), 6) Transient events (impulsive or oscillatory).
However, the most important issue among the mentioned power quality phenomenon for the hybrid micro-grid based on renewable energies is harmonic distortion [6].
Factors and methods of improving power quality in micro-grids have been published in some of the previous studies [7]. However, a few studies have proposed an efficient and practical method to mitigate harmonics at micro-grids. The authors in [8] considered application of passive filters for the power system. However, the mentioned paper is not appropriate for the microgrid. Moreover, in the last decade, authors have proposed to optimize the sizing of harmonic filters using many factors such as cost of filter, power factor, THD, and energy savings [9]. Additionally, although advanced controller such as selective harmonic compensation techniques and the cascaded PR controllers can be used to mitigate the voltage distortion in micro-grids, the main drawback for practical application is complexity of controllers [10]. Harmonic distortions cause serious problems in power micro-grids including lack of proper performance in equipment and also reduced life and efficiency of devices [11]. However, it can be concluded from the aforementioned survey that there have been a few studies published on the harmonic distortion improvement of micro-grid with the presence of high percentage of renewable energies [12].
The major contributions of the current article are:
1- harmonic analysis of a hybrid micro-grid including nonlinear load, energy storage, wind turbine and solar cell, 2- design of a shunt passive filter to comply the requirements of the related standards, 3- the best installation location of passive filter is investigated.
Therefore, the rest of the paper is presented in the following order. Harmonic sources in the micro-grids and the different passive filter are considered in Section 2 and Section 3, respectively. In Section 3, also, harmonic analysis of a hybrid micro-grid is presented. Section 4 considers some scenarios to analysis passive filter application to reduce the harmonics in the micro-grid. In this section, simulation results approve the improvement in power quality parameters related to the harmonics. Finally, discussion and suggestion for future studies are presented.
Harmonics in Micro-grid
The main reason for generating harmonic in the power grids is non-linear loads such as power electronic devices. There are some numerical criteria for showing the amount of harmonics in a signal. THD is one of the most well-known indices for power quality requirement. The permitted limit of current harmonic changes based on the short-circuit level at the point of common coupling (PCC) of the load and maximum load demand, so that the voltage harmonic at distribution grid for PCC must not exceed a maximum value; for instance, as determined in, maximum value of individual harmonic and THD for low voltage levels are 3 and 8%, respectively [13]. On the other hand, the percentage of total harmonic distortion (THD) can be written in two forms; first, as a percentage of the fundamental component (defined by IEEE standard), and second, as a percentage of rms (defined by IEC standard). THD for current and voltage are defined based on Eq. (1)- (2), respectively:
.
.
where, I1 and U1 are the first component of non-sinusoid current and voltage, respectively. Also, Ih and Uh are the hth component of non-sinusoid current and voltage, respectively.
Micro-grids
Micro-grid is a small power system which can act either grid connection or island mode. Also, a micro-grid technology makes it possible to use the power system at decentralized control mode. Hybrid AC/DC micro-grids, which AC and DC buses are connected with power electronic converters, are increasingly used in recent years. On one hand, advantages of AC and DC are independently preserved and, on the other hand, simultaneous quick access to AC and DC is possible, which improves the efficiency and power loss with higher economic advantages [14].
Fig. 1: AC/DC hybrid micro-grids
Diagram of an AC/DC micro-grid is shown in Figure 1. As it is observed, DC loads, including solar and battery cells, are connected to the DC buses, and Ac loads, including wind and gas turbines are connected to AC bus. These two buses are connected by the power electronic converter.
Solutions to restrain harmonic at the micro-grid
Electric filters are used in a micro-grid to restrain harmonics with a specific frequencies, which are normally composed of three elements including resistor, inductor and capacitator. There are various types of filters such as active filter, passive filter, and hybrid filter.
Fig. 2: Power quality analyzer
As it was mentioned before, one simple and costeffective way to eliminate harmonics in the micro-grid is to use passive filters. The primary purpose of a passive filter is to reduce the amplitude of one or multiple voltage harmonic components or current in the grid. If this filter is designed to remove a certain frequency component of the micro-grid, one can use a series passive single-tuned passive filter (STPF). Series filter is formed of a capacitor and inductor which provides high impedance path at frequency of the harmonic. However, a parallel passive filter can create shunt path, and also at the same time harmonics are led to the path with low impedance for related frequencies, to prevent harmonic current flowing into the grid. All passive filter types which available in library of ETAP are as following.
1) single-element filter 2) High-pass filter 3) High-pass type C filter 4) 3rd degree damped filter
Fig. 3: Various types of passive filters, (a) single tuned, (b) high pass, (c) high pass c-type, (d) 3rd order damped [15].
Fig. 4: Single line diagram of hybrid AC-DC micro-grid
Single-tuned passive filter
One type of passive filters is the STPF, which has many applications in the power grid. As shown in Figure 2, a series of resistors, inductors, and capacitors can be connected parallel to the grid. Reactance of the capacitor is equal to the inductor for a STPF at a specified frequency (ωn), so the impedance of the filter will be purely resistive as follows:
.
where Z, R, L, and C impedance, resistance, inductance, and capacitance of the filter, respectively. Also, ωn is the resonant frequency of the filter [15], [16].
.
where ω0 is fundamental frequency of the system. XLn and XCn are the inductor and capacitator reactance of the filter in the intensified frequency. Furthermore, n is the harmonic order.
For the optimal performance of the passive filter in the micro-grid, the exact computation of the resistance, inductor, and capacitor are required to operate at a certain harmonic frequency for reducing the corresponding harmonic. In this paper, to eliminate each order of the harmonic in the circuit, a STPF with the equal resonant frequency with the harmonic frequency that is intended to be eliminated. In a single-element passive filter at the desired harmonic frequency, the filter circuit is resonated, resulting in its impedance being very small and guiding the desired harmonic frequency to the ground [17].
Quality factor of Single-element passive filter
Quality factor (QF) of a STPF is a parameter that determines the frequency-impedance characteristics of the filter. Filters with high QF are only designed for elimination of a specific harmonic. On the other hand, if filter has a low QF coefficient, it can weaken the neighbor harmonic components in addition to set frequencies. The QF of STPF is defined as the ratio of the inductor impedance of the filter to the resistance in intensified frequency. However, since the inductor and capacitor reactance of the filter in harmonic frequency are equal, QF can be defined as capacitator impedance to the filter resistance [18], [19]. The QF coefficient is obtained from Eq. (5):
.
where Q is QF. It should be noted that STPFs have higher quality coefficient compared to low-pass filters [19].
Injection reactive power by single-tuned passive filter
The most important parameter of a passive filter, which determines the filter size, is the injected reactive power amount at the fundamental frequency. Given the filter impedance feature, it can be concluded that the capacitor impedance of the main frequency will be greater than inductor impedance. That is, the filter can act as a capacitor bank at the fundamental frequency. Generating reactive power of filter at the main frequency are obtained based on Eqs. (7) – (8) [20].
.
where QC is the reactive capacitor generating power, Qfilter is the filter generating reactive power, and V is voltage of the filter.
Simulation
Electrical Power System Analysis (ETAP) is a software for analysis of power system with a perfect graphics connector. This program includes many functions and a comprehensive library. Harmonic analysis at ETAP software is done at two parts; harmonic load and frequency response. This study uses ETAP to analysis a harmonic grid and its frequency response. Figure 4 is the single line diagram of under study micro-grid. This hybrid micro-grid includes AC and DC buses, wind turbine, solar cell, battery, linear loads, and non-linear loads. As shown in Figure 4, three non-linear loads, two solar cell and two wind turbines are considered in buses 2, 3, and 4, respectively. The parameters of these harmonic resources are manually entered into the software, as shown in Figure 5 [21]. Moreover, four scenarios are considered for analysis of harmonic load effect on THD index of micro-grid.
Scenario 1: Using of two solar cells and a non-linear load at bus 2 (measurement at bus 2). Scenario 2: Using of wind turbine and nonlinear loads at bus 3 with a line length of 1 km for grid connection (measurement at bus 3). Scenario 3: Using of wind turbine and non-linear load at bus 4 with a line length of 6 km for grid connection (measurement at bus 4). Scenario 4: Using all the devices as shown in Figure 4.
Fig. 5: Harmonic source in ETAP software editor
Results of voltage waveform of the micro-grid at buses 1, 2, 3, and 4 is shown in figure 6.
Fig. 6: Voltage waveform at different buses of the micro-grid without installing STPF
Table 1. Different buses THD Value of the micro-grid
.
Fig. 7: STPF parameters in ETAP
Fig. 8: Voltage waveform at different buses of the micro-grid with installing STPF
Different buses THD value of the micro-grid are listed in Table 1. As listed in Table 1, bus 2 has the highest THD value due to the presence of solar cell and power electronic device, which leads to changes in waveform as shown in Figure 6. Moreover, as it is obvious, bus 4 has higher harmonic value due to the presence of longer line length comparing to bus 3. It should also be noted that bus DC has been considered ideally in simulations. That is, voltage oscillation is ignored in case of installing a good controller. Now, with designing a STPF, as described in Section 3, it is aimed to reduce the harmonics of different busses within limitations defined in corresponding standards [13]. Designed parameters value of the STPF are shown in Figure 7. Wave form of micro-grids is voltage shown in Figure 8.
Figure 7 shows the designed passive filter editor in ETAP software. As it is shown in Table 1 and figure 6, the voltage quality of micro-grids have been mostly improved. Furthermore, prior to filter installation, THD at buses 1, 2, 3, and 4 are 5.43, 12.76, 7.61 and 8.37, respectively, which have been reduced to 2.54, 0.81, 1.18 and 1.29, respectively, after the filter installation. It should also be noted that installation of filters at micro-grid is costly and economic concerns should be taken into account in studying the filter installation, which is out of the scope of this study.
Table 2. THD values of different buses in the harmonic micro-grid
.
Conclusion
This paper studies a harmonic micro-grid including wind turbine, solar cell and nonlinear load using ETAP software. Given under study micro-grid, a passive filter was designed and installed for meeting the power quality requirement of the standard. Furthermore, four scenarios were examined to install the filter at micro-grids. The following conclusions can be drawn.
1) Different designs were considered in micro-grid harmonic analysis, in which harmonic increased based on the capacity of solar cell, wind turbine, transformer, cable length and nonlinear load.
2) It was also observed that total harmonic distortion (THD) differed on various buses of the micro-grid using different generators (solar cell or wind turbine).
3) The STPF was designed and used to eliminate the harmonic distortions by the solar cell. The frequency response of the micro-grid approved the improvements.
4) It was observed that the destructive effect of harmonics on voltage appears at the grid which can destruct the insulation of devices.
5) Grid connection line length for the wind turbine can be an essential role in THD, so that by increasing the line length, the THD increases.
Finally, it is suggested to have further studies on economic analysis and also using higher-order filters at distribution grids.
REFERENCES
[1] M. A. Bidgoli, H. A. Mohammadpour, and S. M. T. Bathaee, “Advanced vector control design for DFIM-based hydro power storage for fault ride-through enhancement,” IEEE Trans. Energy Convers., vol. 30, no. 4, pp. 1449–1459, 2015. [2] P. Mazurek, “Selected aspects of electrical equipment operation with respect to power quality and EMC,” Prz. Elektrotechniczny, vol. 93, no. 1, pp. 21–24, 2017. [3] N. Eghtedarpour and E. Farjah, “Power control and management in a hybrid AC/DC microgrid,” IEEE Trans. Smart Grid, vol. 5, no. 3, pp. 1494–1505, 2014. [4] M. Gała and A. J\kaderko, “Assessment of the impact of photovoltaic system on the power quality in the distribution network,” Prz. Elektrotechniczny, vol. 94, 2018. [5] R. Belaidi and A. Haddouche, “A multi-function grid-connected PV system based on fuzzy logic controller for power quality improvement,” Prz. Elektrotechniczny, vol. 93, 2017. [6] A. Saim, A. Houari, J. M. Guerrero, A. Djerioui, M. Machmoum, and M. Ait-Ahmed, “Stability Analysis and Robust Damping of Multi-Resonances in Distributed Generation based Islanded Microgrids,” IEEE Trans. Ind. Electron., 2019. [7] M. Azizi, A. Fatemi, M. Mohamadian, and A. Y. Varjani, “Integrated solution for microgrid power quality assurance,” IEEE Trans. Energy Convers., vol. 27, no. 4, pp. 992–1001, 2012. [8] J. C. Das, “Passive filters-potentialities and limitations,” IEEE Trans. Ind. Appl., vol. 40, no. 1, pp. 232–241, 2004. [9] M. M. Elkholy, M. A. El-Hameed, and A. A. El-Fergany, “Harmonic analysis of hybrid renewable microgrids comprising optimal design of passive filters and uncertainties,” Electr. Power Syst. Res., vol. 163, pp. 491–501, 2018. [10] S. Yang, P. Wang, Y. Tang, and L. Zhang, “Explicit phase lead filter design in repetitive control for voltage harmonic mitigation of VSI-based islanded microgrids,” IEEE Trans. Ind. Electron., vol. 64, no. 1, pp. 817–826, 2017. [11] J. Arrillaga and N. R. Watson, Power system harmonics. John Wiley & Sons, 2004. [12] Y. Liu, H. A. Mantooth, J. C. Balda, and C. Farnell, “A Variable Inductor BasedLCLFilter for Large-Scale Microgrid Application,” IEEE Trans. Power Electron., vol. 33, no. 9, pp. 7338–7348, 2018. [13] R. Langella, A. Testa, and E. Alii, “Ieee recommended practice and requirements for harmonic control in electric power systems,” 2014. [14] X. Wu, L. Chen, C. Shen, Y. Xu, J. He, and C. Fang, “Distributed optimal operation of hierarchically controlled microgrids,” IET Gener. Transm. Distrib., vol. 12, no. 18, pp. 4142–4152, 2018. [15] S. P. Diwan, H. P. Inamdar, and A. P. Vaidya, “Simulation Studies of Shunt Passive Harmonic Filters: Six Pulse Rectifier Load-Power Factor Improvement and Harmonic Control,” ACEEE Int. J. Electr. Power Eng., vol. 2, no. 1, pp. 1–6, 2011. [16] C. L. Anooja and N. Leena, “Passive Filter For Harmonic Mitigation Of Power Diode Rectifier And SCR Rectifier Fed Loads,” Int. J. Sci. Eng. Res., vol. 4, no. 6, 2013. [17] O. Anaya-Lara and E. Acha, “Modeling and analysis of custom power systems by PSCAD/EMTDC,” IEEE Trans. power Deliv., vol. 17, no. 1, pp. 266–272, 2002. [18] K. K. Srivastava, S. Shakil, and A. V. Pandey, “Harmonics & its mitigation technique by passive shunt filter,” Int. J. Soft Comput. Eng. ISSN, pp. 2231–2307, 2013. [19] Y.-S. Cho and H. Cha, “Single-tuned passive harmonic filter design considering variances of tuning and quality factor,” J. Int. Counc. Electr. Eng., vol. 1, no. 1, pp. 7–13, 2011. [20] T. M. Bloom and D. J. Carnovale, “Harmonic convergence,” IEEE Ind. Appl. Mag., vol. 13, no. 1, pp. 21–27, 2007. [21] X. Chen and G. Zhang, “Harmonic analysis of AC-DC hybrid microgrid based on ETAP,” in 2016 IEEE 8th International Power Electronics and Motion Control Conference (IPEMCECCE Asia), 2016, pp. 685–689.
Authors: Mohsen Alizadeh Bidgoli as corresponding author (Ph.D.), Email: m.alizadeh.b@gmail.com, Ali Bagheri (M.Sc.) Email: bagheriiali1374@gmail.com, Department of Electrical Engineering, Yadegar -e- Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran *Corresponding author: M.Alizadeh Bidgoli
Source & Publisher Item Identifier: PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 95 NR 12/2019. doi:10.15199/48.2019.12.02
Published by Rakesh Kumar, EE Power – Technical Articles: Applications of Grid-connected Battery Energy Storage Systems, February 17, 2023.
Grid operators, distributed generator plant owners, energy retailers, and consumers may receive various services from grid-connected battery energy storage systems. Learn more about the applications here.
Battery energy storage systems (BESSes) act as reserve energy that can complement the existing grid to serve several different purposes. Potential grid applications are listed in Figure 1 and categorized as either power or energy-intensive, i.e., requiring a large energy reserve or high power capability. They can also be classified according to the deployment time scale, which ranges from milliseconds to hours. A general understanding of the services is helpful before analyzing how storage has been used for delivery.
Power quality indices are used to measure how much the voltage and current waveforms differ from a perfect sinusoidal waveform. Distortions can be temporary, like when loads or generators are turned on or off, or they can be constant at steady states, like when non-linear loads or power electronic interfaced generators are running. Energy storage has been looked into for this purpose and has been shown to be a good answer.
Power fluctuation
The rise of intermittent power sources has also brought up the problem of power fluctuations in the network. Solar irradiation and changes in wind speed can cause distributed generation (DG) plants to change power quickly and in large amounts, which can hurt the network. In this situation, energy storage can be added to the DG plant to help smooth out the short-term changes in power. When BESS is used this way, it adds an extra cost to the RES plant, lowering the system’s income. In this case, one way to compensate for the lost money could be to give the plant owners financial incentives to reduce power fluctuations.
Battery energy storage system. Image used courtesy of Adobe Stock
Continuity of Service
In addition to measuring the voltage waveform and the changes in output power, the continuity of service is also kept an eye on. System average interruption frequency index (SAIFI) and system average interruption duration index (SAIDI) are used to figure out the Distribution System Operator’s (DSO) bonus pay. Also, national grid codes can require DSOs to pay fines or make payments to users if service is interrupted. To improve service reliability on distribution grids, energy storage systems can be put in place to make black start procedures easier and let the distribution feeder work on its own.
Both of these problems happen when one or more faults cause a part of a distribution network to stop working with the main transmission grid. In the case of blackouts, storage systems could be added to plans for fixing the grid, making the process of getting power back on faster. Also, a lot of DG and storage systems could make it possible to run safely even when the islanding isn’t planned. In a hypothetical islanding procedure, BESSes will be needed to monitor and reduce the fault-caused transient and sudden load-generation imbalance so that the switch from being connected to the grid to being off the grid is smooth.
Voltage Control
Several devices, such as tap changers, capacitor banks, voltage regulators, and static VAR compensators, can change the voltage in distribution grids. BESSes can help shape the future of voltage management by adding flexibility to distribution grid management. The use of storage units in the voltage control scheme has been shown to work well from a technical point of view.
Figure 2 shows the voltage profiles of one of the two main feeders of the IEEE European test network. These profiles are evaluated and plotted in Figure 3(a), showing the voltage profiles before and after the addition of PV generators. This lets anyone see how the PV generators can cause overvoltages at the network’s end. Figure 3(b) shows how BESS could help reduce overvoltages. The colored lines show the voltage profiles when the BESS system is turned on to reduce the overvoltage. The different colors show where the energy storage is located in the network. In each case, a star is placed on the node where the BESS is located.
Figure 2. IEEE European Test Feeder schematic—highlighted with a star the three nodes considered for locating the energy storage units in the analysis of Figure 3. Image used courtesy of IEEE Open Journal of the Industrial Electronics Society
Figure 3. Voltage Profiles along the network: (a) with and without PV generation and (b) with PV generation and with storage units used to reduce the overvoltage; the storage units are located in the node marked with a star—the nodes numbers are referred to the numeration of Figure 2. Image used courtesy of IEEE Open Journal of the Industrial Electronics Society
To limit how much DGs affect the grid voltage, national and international energy regulators have made it a requirement for DGs connected to distribution grids to follow Q(V) or cos(V) droop curves. In the most recent versions of the national technical standards, such as the Italian standards CEI 0-16 and CEI 0-21 and the German standards VDE-AR-N 4110 and VDE-AR-N 4105, these requirements have also been added for energy storage systems. This service must be done automatically and simultaneously as the main function. It helps to balance out the overvoltage in the distribution network feeders by taking in reactive power and balancing out the undervoltage by putting out reactive power.
Peak Shaving and Load Smoothing
Peak shaving and load smoothing involve making the generation and load profiles flatter so that the grid sees less power at its highest point. In real-time, this scheme can help solve network congestion by preventing the conductors from being overloaded by the peak power of both the generator and the load. Also, in a planning horizon, network improvements like rewiring a feeder or replacing a transformer could be avoided or put off by installing energy storage systems.
In this case, energy storage could be a good idea because DSOs are required to ensure that the network infrastructure is good enough to handle both the load’s nominal power and the connected generators’ nominal power. Along with putting off the upgrade, peak shaving and load smoothing may also help reduce network losses. In this way, BESS operation can further reduce system losses by making the load and local generation more similar in shape.
Frequency Control
In the ancillary service market, generators connected to the transmission networks offer frequency control as a paid service. In recent years, this service has also been offered by generators and energy storage systems connected to the distribution network. Generators and BESS use a droop control that watches for frequency imbalances and responds to them by changing the power output. Table 1 shows the main parameters for some European countries’ primary frequency control logic.
Figure 4 demonstrates how the droop control logic works. Frequency control is a valuable feature of energy storage systems. Energy storage systems might be limited by their maximum and minimum state of charge (SoC). Several ways to control the SoC have been suggested to solve this problem. Depending on the country, the droop logic is set up with different parameters that define or don’t define the deadband and change the amount of droop. The way people get paid is often through tenders, where they bid on regulating power and the price that is needed.
Table 1. Primary Control Parameters in Some European Countries
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Figure 4. Example of P-f curves for primary frequency control—the curves are made according to the data in Table 1. Image used courtesy of IEEE Open Journal of the Industrial Electronics Society
Energy Arbitrage
Energy arbitrage is buying and selling energy on the spot energy market. Since the electricity sector is separated in most countries, energy arbitrage can only be done by a business user. This can be done with a BESS+DG or BESS+load system, where the storage unit moves the energy production or generation to make the most of price changes in the energy market. Energy arbitrage could be used to create a business case, but the prices on the central European spot market may not be high enough if that is the only source of income.
Considering the day-ahead-market price data for Italy and the UK in 2018, the lowest and highest daily prices are found. These are shown in Figure 5, along with the biggest daily price difference and the average difference over the year. The average daily price difference can be less than 50 €/MWh.
Figure 5. Analysis of day-ahead market prices of the year 2018 for Italy (a) and the UK (b). Image used courtesy of IEEE Open Journal of the Industrial Electronics Society
Key Takeaways of Grid-connected BESS
This article has discussed the various applications of grid-connected battery energy storage systems. Some of the takeaways follow.
• Grid applications of BESS can be categorized by energy use and implementation speed. Energy storage in the DG plant can also reduce power fluctuations.
• Energy storage systems can simplify black start procedures and let the distribution feeder function independently, improving distribution grid reliability.
• BESSes can shape voltage management by adding flexibility to distribution grid management, which has been shown to work technically.
• Technical, economic, and regulatory research may examine how to combine multiple services effectively. Research should focus on optimizing battery features and providing complementary services.
• Flattening generation and load profiles reduces network congestion. Energy storage systems avoid feeder rewiring and transformer replacement.
• Generators and energy storage systems connected to the distribution network can ignore paid frequency control.
• Energy arbitrage—buying and selling energy on the spot energy market and moving energy production or generation to take advantage of price fluctuations—can be done with a BESS+DG or BESS+load system.
• Research should demonstrate how to best use the battery’s characteristics by creating a comprehensive service delivery plan.
• Combining multiple services may be studied in the future. Multiple stakeholders also improve business case success.
Author: Rakesh Kumar holds a Ph.D. in Electrical Engineering with a specialization in Power Electronics from Vellore Institute of Technology, India. He is a Senior Member of IEEE, Class of 2021, and a member of the IEEE Power Electronics Society (PELS). Rakesh is a committee member of the IEEE PELS Education Steering Committee. He is passionate about writing high-quality technical articles of high interest to readers of the EE Power Community. You can email him at rakesh.a@ieee.org.
Published by Wojciech MATELSKI1, Electrotechnical Institute, Warsaw
Abstract. The article describes a practical implementation of the induction motor (IM) drive system powered from photovoltaic (PV) panels. The system incorporates an energy storage device, in form of a supercapacitor bank, and enables an AC grid connection. Two system concepts are considered, thus a discussion about the favorable solution is given. A model installation was developed, and the chosen system components are described. Laboratory tests have been conducted and the results are presented.
Streszczenie. W artykule opisano praktyczną implementację napędu z silnikiem indukcyjnym (IM) zasilanym z paneli fotowoltaicznych (PV). System zawiera zasobnik energii, w formie baterii superkondensatorów, oraz posiada możliwość podłączenia do sieci AC. W pracy rozpatrzono dwie koncepcje realizacji systemu oraz przedstawiono dyskusję dotyczącą korzystniejszego rozwiązania. Opracowano instalację modelową oraz opisano wybrane komponenty systemu. Praca zawiera wyniki wstępnych badań laboratoryjnych (Układ zasilania napędu indukcyjnego z baterii fotowoltaicznej z magazynem energii – model eksperymentalny).
Keywords: induction motors; photovoltaic systems; supercapacitors; variable speed drives. Słowa kluczowe: silniki indukcyjne, systemy fotowoltaiczne, superkondensatory, napędy z regulowaną prędkością.
Introduction
Solar energy is considered as the most environment friendly renewable energy source. Comparing to other renewables, converting solar radiation into electricity pollutes the environment to the smallest extent. As technology advances, the efficiency of solar panel systems is increasing, and in recent years, the price per kilowatt peak power is dropping with a steady rate. Despite this fact, solar panel systems are still costly, and large solar power plants are economically justified in areas of appropriate insolation. Even though, there are several applications, where due to other considerations, solar power appears to be a reasonable solution.
Very often water pumps, used in agriculture for field irrigation, are installed in remote or rural areas, where the industrial power grid is not available. In literature several concepts of solar energy-based water pumping are described [1 – 8]. Water flow is forced by the work of pumps driven by DC [2] or AC motors [1, 5 – 9]. Thanks to their robustness, simple structure and low cost, induction motors are a common solution [1, 5 – 7].
Power generation from solar panels strongly depends on the current weather conditions. In a straight forward approach, when the solar radiation is sufficient, the pumping system can operate, providing fresh water on the field. The drive works intermittently. To make the system more cost effective, electrical energy storage devices are avoided [1, 5 – 8]. In this way, the system level of complexity is reduced. In reference [1] a small power, mobile pump system for water filtration purposes is presented. In exchange for the lack of electrical energy storage, water tanks can be applied, so that water is gathered for later use. Even though, the power rating of the solar panel has to be large enough, to enable motor start and satisfy the load demand. In such systems motor stall is possible.
On the other hand, incorporation of a battery pack [2 – 4] makes the installation independent from solar radiation and expands the functionality of the solar water pump system. For example, remote control through GSM network can be implemented [2 – 4]. Reference [3] presents an automated irrigation system for optimization of water use for agricultural crops. Field tests were carried out and water savings up to 90% were achieved in comparison with traditional irrigation practices on the test agricultural zone. What is more, thanks to energy storage devices, the power rating of the solar panels can be lowered, and the risk of motor stall is reduced. Even under low insolation conditions, the energy can be cumulated in order to enable short-time higher power values, which is perfect for intermittent mode of motor operation.
Solar panels operate with the best efficiency at a specific load current and output voltage. This is achieved through implementation of a maximum power point tracking algorithm MPPT. In batteryless systems the load demand is regulated. Considering solar pump drives, the speed of the motor can be adapted according to insolation sensor readings [6] or various perturb and observe algorithms [1, 5, 7]. The command speed depends on load conditions. Thus the control possibilities of the drive are reduced. A battery pack enables independent speed control (when the stored energy is sufficient) of the motor. MPPT is usually provided by an additional DC/DC battery charging converter.
Reference [9] describes a batteryless AC drive system, powered from solar panels, supported from the electrical grid. The power rating of the photovoltaic battery can be lower, and thus the system costs can be reduced. This solution is completely independent from current weather conditions.
Solar panel generation units are also a reasonable solution in elevator applications. The presence of an auxiliary source of power, supporting the AC grid, improves the reliability of the drive system. Together with an energy storage device, the system can provide some additional safety features, which is crucial in elevator applications. For example, in case of power failure, the stored energy is used to supply the traction motor in order to move the cabin to the nearest floor, open the door and enable the evacuation of the passengers. In [10 – 12] elevator systems, powered from a DC microgrid with solar panels and energy storage units and an AC grid connection, are presented. The main focus of the research was the elaboration of energy management algorithms to optimize the operation of the elevator drive system, and reduce the power consumption from the AC grid.
This article describes a practical implementation of the induction motor IM drive system supplied from photovoltaic PV panels. The system incorporates an energy storage device, in form of a supercapacitor bank, and enables an optional AC grid connection. Two system concepts are considered, thus a discussion about the favorable solution is given. A model installation was developed, and the chosen system components are described. Laboratory tests have been conducted and the results in form of selected waveforms are presented.
Solar powered drive with energy storage
The presented in this article drive system consists of an induction motor, powered from a solar panel array. The general concept of the system, in form of a block diagram, is depicted in Figure 1. Considering the moderate irradiation conditions in Poland, the solution incorporating an energy storage device has been adopted. The system can be used in applications characterized by intermittent operation, like water pumps for field irrigation or elevator systems.
Fig.1. IM drive system supplied from PV panels block diagram
For energy storage purposes supercapacitors SC were chosen. Even if their energy density is not comparable to that of conventional electrochemical accumulators, like the commonly used lead-acid batteries, their high power density, large number of charge and discharge cycles (approximately 1 million), shorter charging times and the possible amount of stored energy, makes them compatible with many industrial applications [13], like for example elevator systems [9 – 13]. What is more, lead-acid battery banks are heavy and expensive and their lifetime is estimated to be one fifth of the lifetime of a solar panel [5].
As can be seen from Figure 1, the system does include an optional industrial low voltage three phase grid connection. In this way, the system becomes a more reliable power source, so the operation of the load can be ensured for example in emergency situations. In cases of poor weather conditions, energy can be drawn from the grid in order, if necessary, to fed the load, or it can be stored in the SC, when the prices are low, for example at night.
The power flow is controlled by the means of power electronic converters, and in the block scheme from Figure 1, they are contained in the block denoted as PES. This structure will be discussed in detail in the next section.
For the purpose of this research, an existing 690 W solar panel system, installed at the parking lot of the Electrotechnical Institute, was used. The solar panels are presented in Figure 2, and their parameters are listed in Table 1.
Table 1. Photovoltaic panels system parameters
.
Having regard to the intermittent operation of the drive system, thanks to the presence of the supercapacitor as an integral part of the installation, the power rating of the motor can be larger than the nominal power generated by the solar panels. In situations of cloudy weather, a small power level can be achieved, but with the energy stored over a longer period of time, the supercapacitor can deliver power sufficient to start even a larger motor.
System structure considerations
The type and configuration of the power electronic converters in the solar drive system determine its performance and capabilities. For further research two system structures were analyzed. The configurations are presented in Figure 3 and Figure 4, and were denoted as CONFIG 1 and CONFIG 2 respectively.
The IM is powered through a variable frequency converter, in Figure 3 and Figure 4 denoted as P3, and controlled by its inverter DC/AC part. The electrical energy from the solar panels can be supplied via the DC link terminals DC+, DC- of converter P3.
Fig.2. Solar panels installation used in research
The main aspect distinguishing CONFIG 1 from CONFIG 2 is the location of the SC in the structure, which in consequence strongly affects the function and requirements for converter P2 in the system. This feature also has influence on the principle of operation of converter P1.
Fig.3. IM drive system supplied from PV panels with energy storage – CONFIG 1
The adopted motor is a 400 V (phase to phase RMS) machine. To ensure proper operation, the DC link voltage udcof converter P3 needs to be sustained on a high enough level. In both configurations an optional connection to the industrial electricity grid is assumed. The drive can be powered solely by the solar panels when udc is kept higher than the rectified three phase grid voltage provided by the AC/DC part (three phase bridge diode rectifier) of converter P3. In this way, the energy consumption from the grid is disabled. This principle of operation, together with the relatively low voltage output level generated by the solar panels (cf. Tab. 2), enforces the boost character of converters P1 and P2 regarding CONFIG 1, and converter P2 as for CONFIG 2.
Fig.4. IM drive system supplied from PV panels with energy storage – CONFIG 2
The level of insolation, at which photovoltaic cells are exposed, determines the power they produce. The U-I characteristics of solar panels are non-linear. The efficiency of the conversion of sunlight into electricity reaches its best performance at a specific output voltage. Several techniques are described and applied in order to keep this voltage on a desired level. In this way, maximum power point tracking MPPT is achieved. Considering CONFIG 1, in order to perform MPPT, converter P1 has to cooperate either with converter P2, or with the inverter part of P3, or together with both converters. In the first case, the drive has to be stopped, and the energy is stored in the SC. In situations when the IM needs to operate, but the SC is fully charged, MPPT can be realized by adapting the output voltage frequency command of converter P3, so that the power consumption of the IM is altered. At last, when uSC doesn’t exceed its maximum value, the surplus energy is used to charge the SC, thus enabling independent control of the drive. As for CONFIG 2, if the SC is not fully charged, MPPT can be realized solely by P1.
Comparing CONFIG 1 and CONFIG 2 it can be seen, that for the same modes of operation, the power flows through a different number of conversion stages, i.e. converters. Each conversion stage is associated with power losses. The main modes of operation, together with the number of conversion stages, are listed in Table 2.
From Table 2, in modes 2, 3 and 4 the number of converters taking part in the operation of the system for CONFIG 1 and CONFIG 2 is equal.
Considering mode 5, when powering the drive only by the solar panels (the SC is fully charged or discharged) CONFIG 1 has the advantage, because the operation is associated with a smaller number of converters, than for CONFIG 2. CONFIG 1 is more suitable for systems where the nominal power of the solar panels is equal to the power demand of the drive.
Mode 1 has to be analyzed together with mode 3. In this case CONFIG 2 is more efficient. This solution is applicable for systems characterized by intermittent operation, where the motor power exceeds the power generation of the solar panels, and the lack is covered by the energy stored in the supercapacitor.
Table 2. Main modes of system operation and the corresponding power conversion levels
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Experimental setup
In order to perform laboratory tests, the system presented in Figure 1 has been built. In the establishment, components from former projects have been utilized and are listed in Table 3.
The power of the adopted induction motor IM (cf. Tab. 3) equals 3 kW and is greater than the 690 W of the solar panels (cf. Tab. 1). Therefore CONFIG 2, presented in Figure 4, has been chosen for further laboratory tests.
Table 3. Laboratory model system description
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Converter P1 is used to charge the SC and ensure MPPT of solar panels. The scheme of converter P1 is presented in Figure 5a. Converter P2 serves to boost the voltage of the SC to the requirements of the DC link of converter P3, which equals approximately 600 V. The converter scheme is presented in Figure 5b. The structure of P2 enables bidirectional power flow. In this way, when the drive operates in generator mode, energy recuperation is possible. Converters P1 and P2 have been realized in interleaved technology. This solution consists of a parallel connection of identical converters (legs), which can give a number of advantages. The effective output voltage frequency is higher. As a result the load current pulsations are decreased. What is more, converter reliability is improved, and the power ratings of necessary components can be reduced [14, 15]. P3 is a commercial LG LS 5,5 kW variable frequency drive and its structure is presented in Figure 5c. The DC/AC part is a two-level three phase bridge inverter operated with the volt/hertz control principle. The AC/DC part is a three phase diode bridge rectifier. The terminals U, V, W are used to connect the IM. The motor is directly coupled with a 3 kVA permanent magnet synchronous generator PMSG. The drive test bench includes an incremental encoder for speed measurements and a torque sensor and is described in [16]. The PMSG is connected to a variable resistance load RL. The built solar drive laboratory model installation is presented in Figure 6 and Figure 7.
Fig.5. Converter structures: a) 2-leg interleaved buck converter (P1); b) 3-leg interleaved bidirectional buck-boost converter (P2); c) variable frequency converter (P3)
Fig.6. Solar powered drive test bench (including: converter P3 and its DSP control board, motor IM, generator PMSG)
The converters P1 and P2 are run by a control board, incorporating a DSP TI TMS320F28335 microcontroller together with a FPGA ALTERA CYCLONE III unit. The P3 converter is controlled by DSP TI TMS320F2812 unit.
Fig.7. Solar powered drive test bench (including: converters P1, P2 and their DSP control board, supercapacitor SC, variable resistance load RL)
Laboratory results
The proposed system is still under development. Some first laboratory tests have been conducted, including a part of the model installation. The results in form of selected waveforms are depicted in Figure 8 and Figure 9.
During the test, the grid was disconnected. The solar panels and converter P1 also did not take part at this stage of research. The SC has been precharged before the experiment started (cf. Fig. 8a). The waveforms have been registered by RejDiag application, developed in the Electrotechnical Institute. This program is operated from a PC and it enables the communications with the DSP control boards. The measuring points were as presented in Figure 4. The PMSG was loaded with a constant resistance RL of 67 Ω per phase.
In the experiment the command output voltage frequency signal fe of converter P3 was changed, to adjust the rotational speed nR of the IM (cf. Fig. 9b), so that it should resemble an elevator moving from one floor to another. After about 1s fe was increased from 0 Hz to 30 Hz, during a 1s interval, so the motor started and accelerated with a steady rate. The DC link voltage udc, presented in Figure 8c, decreased and when it dropped under the trigger value of 530 V, converter P2 turned on, in order to discharge the SC and maintain udc on the reference value. After fe reached 30 Hz (t = 2s) the IM started constant speed operation. The developed torque Tm also remains constant (cf. Fig. 9c). In this way, the mechanical power demand Pm (cf. Fig. 9d), calculated as:
.
also remains constant. Converter P2 delivers power Pdc, presented in Figure 9a, calculated as:
.
where: idc – output current of converter P2 [A],
to the DC link of converter P3. The SC is being discharged, so that the supercapacitor voltage usc, presented in Figure 8a, decreases. In order to deliver constant power Psc, presented in Figure 9a, calculated as:
.
the input current for converter P2 isc drawn from the SC has to increase, which can be seen in Figure 8b. At t = 11s, fe starts decreasing to reach 0 Hz, after another 1s. The motor decelerates operating in generator mode, thus udc increases exceeding its reference value. Therefore converter P2 regulates the current drawn from the SC to 0 A.
From the beginning of the test, the supercapacitor voltage was deliberately set below the half of its nominal value (cf. Tab. 3). Converter P2 needs to boost this voltage to the level of the DC link voltage udc, so the boost factor varies from about 10 at the start of the test, to almost 17 at the end of constant speed operation. Converter P2 has difficulties in maintaining udc on the reference value 540 V. But despite those harsh conditions, the motor is still operational, and the efficiency of converter P2 is satisfactory: at t = 2s it equals 92%, and at t = 11s drops to 88% (cf. Fig. 9a). The voltage of the SC decreases, so in order to deliver constant power to the drive, the current drawn from the SC is increasing, thus the power losses in the system also increase. This can be observed in Figure 9a, where Pdc is on a steady level (constant speed region), while Psc is increasing. The supercapacitor has to cover for rising power losses due to the increasing discharge current.
Fig.8. Solar powered drive test results: a) supercapacitor voltage uSC, b) supercapacitor current iSC, c) converter P3 DC link voltage udc, d) converter P2 output current idc
Fig.9. Solar powered drive test results: a) power drawn from the SC Psc, converter P2 output power Pdc, b) IM shaft rotational speed nR, c) mechanical load torque Tm, d) mechanical power at shaft Pm
Conclusion
The solar powered induction motor drive system, with energy storage in form of a supercapacitor and possible grid connection, has been presented. The system is meant for application with field irrigation water pumps and especially elevator installations.
In the article two system structures have been described and compared. The relationship between the power of the solar panels and the power of the drive determines the more favorable solution. A model for laboratory tests, including a 690 W solar installation and a 3 kW induction motor drive system has been developed. Some first laboratory tests including a part of the model installation were conducted. The supercapacitor supplies the drive with sufficient power, even in states of high depth of discharge. The applied converter is able to boost the voltage according to the requirements of the DC link of the inverter. Although due to the high current drawn from the SC, the efficiency of the boost converter gets affected. Therefore it is wise not to let the SC get discharged under the half of its nominal voltage. Besides, at that point there is only 25% energy left.
The system is still under research. Further tests, including all components of the model, such as power and efficiency measurements on each stage of power conversion, need to be carried out.
Projekt finansowany ze środków NCBiR w ramach programu INNOMOTO: “Wielofunkcyjny system ‘inteligentnych’ sprzężeń multilateralnych (WSISM) pojazdów elektrycznych z siecią dystrybucyjną, zasobnikami i odnawialnymi źródłami energii”. Aplikacja no.: POIR.01.02.00-00-0277/16.
REFERENCES
[1] Chualin J., Wei J., Design of a digital controlled solar water pump drive system for a nano-filtration system, Ninth International IEEE Conference on Power Electronics and Drive Systems (PEDS), (2011), 982-986 [2] Ganesh K., Girisha S., Embedded controller in farmers pump by solar energy (automation of solarised water pump), IEEE International Conference on Recent Advancements in Electrical, Electronics and Control Engineering (2011), 226-229 [3] Gutierrez J., Villa-Medina J.F., Nieto-Garibay A. Porta-Gandara M.A., Automated irrigation system using a wireless sensor network and GPRS module, IEEE Transactions on Instrumentation and Measurement, Vol. 63 (2014), No. 1, 166-176 [4] Alex G., Janakiranimanthi M., Solar based plant irrigation system, International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB16), IEEE, (2016) [5] Vitorino M.A., de Rossiter Correa M.B, Jacobina C.B., Lima A.M.N, An Effective Induction Motor Control for Photovoltaic Pumping, IEEE Transactions on Industrial Electronics, Vol. 58 (2011), No. 4, 1162-1170 [6] Kolano K., Kolano J., Praktyczna realizacja układów napędowych z trójfazowym silnikiem indukcyjnym zasilanym z baterii ogniw fotowoltaicznych, Zeszyty Problemowe – Maszyny Elektryczne, Nr 77 (2007), 5-10 [7] Kusio M., Maksymalizacja mocy układu napędowego klimatyzacji zasilanego z generator PV, Prace Instytutu Elektrotechniki, Zeszyt 236 (2008), 76-86 [8] Singh B., Mishra A.K., Kumar R., Solar powered water pumping system employing switched reluctance motor drive, IEEE Transactions on Industry Applications, Vol. 52 (2016), No.5, 3949-3957 [9] Kolano K., Układ napędowy zasilany z baterii ogniw fotowoltaicznych współpracujący z siecią elektroenergetyczną, Napędy i Sterowanie, Nr 2 (2011), 80-84 [10] Lin Y., Liu Y., Simulation and experiment research on a new elevator system with solar energy and super capacitor, Journal of Software Engineering, 9 (3), (2015), 534-547 [11] Pham T.H., Prodan I., Genon-Catalot D., Lefevre L., Efficient energy management for an elevator system under a constrained optimization framework, 19th International Conference on System Theory, Control and Computing (ICSTCC), October 14-16, Cheile Gradistei, Romania, IEEE (2015), 613-618 [12] Nikolić T.R., Nikolić G.S., Petrović B.D., Sojcev M.K., Elevator system with dual power supply, Facta Universitatis, Automatic Control and Robotics, Vol. 14 (2015), No. 3, 159-172 [13] Rufer A., Barrade P., A supercapacitor-based energy storage system for elevators with soft commutated interface, IEEE Transactions on Industry Applications, Vol. 38 (2002), No. 5, 1151-1159 [14] Matelski W., Low power DC/DC converter from 3 kV to 300 V: simulation analysis, IAPGOŚ, Vol. 6 (2016), No. 2, 44-47 [15] Matelski W., Wolski L., Abramik S., Dwukierunkowa przetwornica DC/DC z wykorzystaniem elementów SiC, IAPGOŚ, Vol. 6 (2016), No. 3, 64-69 [16] Matelski W., Łowiec E., Abramik S., Symulator małej turbiny wiatrowej, Prace Instytutu Elektrotechniki, Zeszyt 273 (2016), 63-77
Authors: mgr inż. Wojciech Matelski, Instytut Elektrotechniki, Bałtycka Pracownia Technologii Energoelektronicznych (BaPTE), ul. Chechosłowacka 3, Park Konstruktorów, 81-336 Gdynia, E-mail: wojciech.matelski@iel.pl.
Source & Publisher Item Identifier: PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 94 NR 2/2018. doi:10.15199/48.2018.02.42
Published by Marek P. MICHALAK1, Monika E. SZAFRAŃSKA2, National Institute of Telecommunications (1), Wroclaw University of Science and Technology (2)
Abstract. The Internet of Things (IoT) is fast growing part of the market that becomes one of the biggest challenges of contemporary and future electromagnetic compatibility researches. The paper presents discussion on the subject of EMC testing of IoT related equipment. The authors considered different, alternative to classical, approaches to equipment testing for electromagnetic compatibility including those using new technical developments of FFT use in EMC testing. PCB-like testing approach and system approach are also discussed and taken into consideration.
Streszczenie. W artykule przedstawiono dyskusję nad tematyka związaną z badaniami EMC urządzeń tzw. Interrnetu Rzeczy (Internet of Things – IoT), stanowiącego szybko rozwijającą się gałąź rynku teleinformatycznego. Autorzy uwzględnili różne podejścia do zagadnienia badań kompatybilności urządzeń w tym wykorzystujące nowe zdobycze techniki w zakresie badań z wykorzystaniem FFT. Pod dyskusję poddane zostały również podejścia badawcze zbliżone do rozwiązań stosowanych w przypadku PCB oraz podejście systemowe. (EMC w świecie IoT).
Keywords: IoT, EMC, test setup Słowa kluczowe: Internet Rzeczy, kompatybilność elektromagnetyczna, stanowisko badawcze
Introduction
In the last years the number of smartphones, tablets and “wearable” equipment significantly increased. In 2011 Cisco [1] provided estimation that in 2020 there will be 50 billion Internet of Things (IoT) related equipment in the world. This means a huge number of equipment that must coexist.
Together with the increase of transfer rates the use of wireless communication also increased. That brings questions if the increasing number of wireless devices will influence the rise of problems with Electromagnetic Compatibility (EMC) and if today’s industry is ready for EMC problems connected with IoT development. The statistics alone show that the more equipment influence and interact with each other (what is a basic presumption of IoT), the more electromagnetic compatibility problems can be expected, especially when EMC parameters of the equipment will be left on today’s level.
The problems mentioned above are being razed on international level, the subject is very up to date and in coming years it will undoubtedly be major EMC issue.
How to test the IoT equipment against the EMC?
The dynamic development of IoT equipment contributes to the fact that this equipment is quickly introduced to the market and mass production, what in turn generates the need for testing of this equipment. In most cases the EMC standards fall behind as far as new solutions are concerned. And even if that can be achieved the new problem arises. Most of IoT intended equipment is meant to work in “crowds” – we face the accumulation of equipment in direct proximity. And here another issue arises – very often that equipment was tested according to different standards, therefore in some cases even if on the basis of performed tests single equipment meets the appropriate requirements, the coexistence of the same equipment in specific conditions becomes impossible or generates interactions that influence the integrity and correctness of data transferred by that equipment. Furthermore, if we take into account that IoT equipment works in very close proximity the other issues arise that were not so much important before as far as EMC testing is concerned. Even if since lately it is taken into consideration that electronics is being smaller all the time, in case of IoT equipment we face it in every single issue. For typical equipment for which the influence of devices working in close proximity was observed in the last years, not long ago the IEC 61000-4-39 [2] standard was positively voted for introduction. The solutions proposed in above mentioned standard may introduce some kind of transposition of IoT equipment coexistence into area of the tests. However, if we take into account the time usually needed for introduction of standards we can expect that before the testing methods described in IEC 61000-4-39 are introduced to the product standards it might take several years.
It should be noted here, that because of possible equipment classification and purpose, that equipment may be the subject of different requirement, both emission and immunity. Significantly different may be also the interpretation of these requirements. For example, when we consider gas measurement equipment, especially in case of gases that are dangerous to people, the tolerance for incorrect data is single numbers in percentage, while in cases where the measurement or transmission can be repeated and the measured value is not critical in decision making process, the allowed error level can be even as high as 50%. Off course most of manufacturers that like to be seen as reliable and reputable state much higher requirements for their products. Whichever the case is, there are some (mostly low cost) products that are available in the market that not only do not work properly (in clients opinion) but also do not even comply with basic requirements. It is not possible to avoid situations where non-compliant products are introduced to the market. Even with thorough control and market surveillance this scenario needs to be taken into account.
The other fact that must be noted here is that very often the same level of ignorance of the developers of some solutions can cause significant problems with their products or systems compatibility. Commonly known rule is that even if you use components that are standards compliant it does not guarantee that you end up with the resulting compatible product. The same is still true (and even in higher scale) in the IoT world. Full integration of equipment together with dimensions requirements for IoT devices causes very small (read: almost non-existent) separation between the components (especially when compared with wave lengths).
The question is, how to test IoT equipment against the EMC requirements? Do we test them:
• With classical approach – like other equipment before, according to EN 55022, EN 55024 or EN 55032? • With real time measurements? • Like components (or PCBs)? • Like whole systems (from over a dozen to several hundreds of elements?
Classical approach
The most well-established and current approach on the market for many years has been to test IoT or similar devices according to standards and requirements for typical IT equipment (until recently it were mainly EN 55022 standard [3] for emission measurements and EN 55024 standard [4] for immunity measurements), and in recent times for multimedia equipment (i.e. EN 55032 standard [5], superseding the EN 55022 and some other standards, and waiting for publication is EN 55035 standard which will cover the immunity requirements for multimedia equipments, and among others will supersede the EN 55024 standard – the CISPR 35 Publication is already published, so we probably will not wait for EN 55025 for too long). Using this approach is the closest one to regulatory requirements for introduction to the market of new equipment according to Radio Equipment Directive (RED) [6] or ElectroMagnetic Compatibility Directive (EMCD) [7].
In the case of testing according to the requirements of the above mentioned Directives harmonized standards [3, 4, 5], the tests are performed in tests set-ups strictly defined in the standard. This means that all the set-ups of every single part of equipment are made according to those standards. However, there is always the question whether the measuring system corresponds to the basic need of an IoT device under test, whereby, in principle, those devices have at least a few, a dozen or several hundred components in their basic functionality, each of which may, according to the Directives [6, 7] requires testing as a single item because of being also marketed as a single item.
In a typical tests performed in accordance with the guidelines and requirements of the abovementioned standards, the measuring/testing system is assembled in the way in which there may be multiple components of the device and the auxiliary/associated equipment. An example of such a system prepared for testing with the distinction of individual components is shown in Figure 1.
In the case of most IoT devices we mainly deal with low power devices and most of them are battery powered, so the most interesting scenario for the measurements, and closest to the practical applications of these devices and the most representative of the entire product group are the IoT devices tests in the typical case of radiated disturbances emissions measurements, as most of these devices are not equipped with external wiring that could be potential sources of reception and generation (radiation) of disturbances.
Fig.1. Example of a host system with different types of modules [5]
A typical, standard dictated set-up of several larger systems of devices during radiated emission testing is shown in Figure 2. Such placement of individual test objects can generate problems for devices that require multiple cooperating auxiliaries for their correct operation or consist of multiple modules, linked together. Case studies and divagations on this subject are discussed later in this article.
Fig.2. Typical test set-up for radiated emission tests [5]
The immunity standard for multimedia devices, which would indicate the direction of the test as EN 55032, has not yet been introduced in Europe (although CISPR 35 Publication which does just that, as it was mentioned before, is already published – it is difficult to say what slows down the introduction of it in Europe, especially when it is taken into account, that both CISPR 32 and CISR 35 were always meant as complementary publications/standards). The EN 55032 standard is significantly is far more sensitive to problems with complex equipment and their modular design when compared to EN 55022, which it replaced. Looking at the construction of EN 55032 standard and its preparation for the commonality of certain fragments with the expected EN 55035 standard for the immunity of multimedia equipment, it is to be expected that the latter one (still awaiting the unification with the European Norms requirements) will also take into account the complexity and multi-module construction of contemporary (and probably future) devices. On the other hand, it should not be expected that all the issues related to the internal compatibility of multi-modular devices (and systems) or interactions between elements of the system will be satisfactorily addressed. Meanwhile, it is clear today that these issues may be the biggest challenge in the of IoT devices world.
It must be remembered, that the EU Directives essential requirements (and consequently Directives harmonized standards) put main impact at electromagnetic spectrum protection and protection of the systems that make use of that spectrum, especially (historically) radio diffusion systems, against the interfering effects of disturbances generated by devices that are newly introduced to the market. The second essential requirement of EU Directives should theoretically ensure, that those newly introduced to the market devices are adequately secured against intentionally generated electromagnetic fields. Meanwhile in the case of IoT devices, being mostly low power devices, the main problem may be to ensure the compatibility (read: undisturbed cooperation) between the devices. It is true that meeting the two basic objectives (essential requirements) of the Directives should theoretically also ensure that the coexistence of equipment meets the essential requirements of the Directives. However, in the case of IoTs, it may be that, due to their specific location (often direct proximity), for the devices working in the proximity of other devices (in addition to constantly changing their mutual position) the current (classical) approach and the standards theoretically “safeguarded” by the appropriate allowed emission limits and required levels of immunity combined with measurement methods that measure distance distances that do not correspond to actual distances between devices in the IoT world will simply be insufficient.
The first steps to take into account the actual conditions of modern devices coexistence (including those that already can be regarded as IoT devices), as already mentioned, have been made in the IEC 61000-4-39 standard [2]. This March 2017 published international standard (just published as the EN 61000-4-39 on June 9th 2017) is intended to refer to the immunity testing of devices in the immediate proximity of other electromagnetic field generating devices and is related to the problem that has been observed by placing various types of mobile devices and RFID devices close together. Taking these issues into account, IEC 61000-4-39 and its research methods can be a good step towards addressing the challenges we face with the Internet of Things. Unfortunately, it should be taken into account that adoption of standards is quite time-consuming and even after the introduction of this standard, it may take some time before it finds its references in general standards and product standards.
Real time tests approach
The new FFT (Fast Fourier Transform) powered fast receiver solutions, recently emerging in the market for measuring equipment, enable accurate and more complete information about the measured device disturbances that sometimes switch at the nanosecond level. Devices with such short switching times produce very broad electromagnetic spectrum. The use of FFT-equipped receivers allows us to judge the distributed spectrum of signals and at the same time gives us the opportunity to indicate possible electromagnetic compatibility problems resulting from the generation of disturbances over a very wide frequency range.
It is important to note that IoTs often operate in such a way that most of the time they are in standby mode and their period of activity and communication between them occurs sporadically and takes relatively short time – those times range from several milliseconds to single seconds. The types of disturbances produced in such work cycles are very difficult to detect with conventional measurement methods, unless multiple frequency sweeps using the MaxHold function are used, which is very time consuming, but first and foremost it does not guarantee that all emissions (including unwanted or spurious emissions) from the device will be recorded. With the ability to analyse the emissions in real time and thus simultaneously measure for a relatively wide frequency range (instead of stepwise scanning), it is possible to record all events and consequently evaluate the device with a much greater degree of confidence than in the classical approach.
The FFT based EMI receivers can become an element that will significantly enhance classical, CISPR’s requirements based tests with the additional capabilities of using such “fast”, Real Time Analysis using receivers. All the more so, since these types of solutions are slowly being allowed to be used in some CISPR Publications – for the time being mainly only as the support for existing solutions, but in the future the importance of these measurement techniques will most probably increase significantly.
PCBs approach
The issues related to the IoT devices electromagnetic compatibility verification can also be approached in similar way as for the integrated circuits (PCBs) measurements. In most cases speaking of IoT devices we are talking about relatively small components. Most of these devices are designed as integrated circuits which are then integrated on a common plate. Thus it seems that also for such solutions, methods similar to those described in EN 61967-x [8] and EN 62132-x series [9] could be used. On the basis of such or similar solutions, the problem approach used by many R & D designers can be applied. One of the first articles that linked IoT and EMC issues from the engineer point of view [10] shows some considerations on the applicability of this type of measurement techniques to IoT systems and devices.
Fig.3. Measurement system for pin current and pin voltage [10]
Among the main issues that need to be mentioned here, one of the most important ones seems to be the ability to test for EMC in the very early stages of element development. It is very important since the one of the principals of a well-designed EMC layout is to think about EMC issues that have already existed from the very beginning of the product’s life – from the prototype design phase right to the final product. In fact, only this approach provides the right and the best possible EMC design – in the world of IoT, it is equally, or even more, true.
It should be borne in mind, however, that such an approach, if misapplied, may also involve some risk. In most cases, the device will not meet the requirements and conditions of its typical operation. Yes, tests at this level allow for special signals or device software to approximate the worst working conditions (worst case scenario), but this will not always correspond to the final application of the device and its co-operation with other devices.
System approach
Another possible approach to the IoT devices electromagnetic compatibility is to test these devices as whole integrated systems comprising dozens or even hundreds of components (despite it is a significant departure from the approach adopted in the EU Directives [6, 7] because the Directives look at these components as separate devices – mainly because they are marketed separately, neglecting the fact that they work together). In particular, during immunity testing, such systems may exhibit significantly lower susceptibility to the disturbances than single components. This is due to the fact that IoT devices in most cases are able to transfer their functions to other devices, and thanks to that the impact made by the disturbance on a single device does not cause errors in the operation of the entire system. This system approach is therefore much closer to the “real world” IoT applications.
There are known cases of large installations with multiple devices (for example, sensors) in which due to the one component (or the small group of components) lack of immunity the whole system, due to redundancy discussed above, does not react negatively to the disturbances. Of course, this does not apply to cases in which an incorrectly functioning system component is a key element, which then causes the whole system to crash. In complex systems (and we must remember that in most applications when talking about IoT systems we will deal with complex systems), it may be that not only the vulnerabilities of the devices themselves (hardware susceptibility level), but also software susceptibility level may increase or decrease immunity to disturbances and errors caused by them.
On the other hand, it can (and must) be said that mutual interactions of equipment can also contribute to the increased emission of electromagnetic disturbances of the whole system.
It must be borne in mind that the accumulation of a large number of devices in a small space can cause that even though individual devices are able to meet the emission standards, all systems may no longer be able to maintain this capability.
Summary
Every IoT device that is to be introduced to the market must be able to boast positive results of EMC tests. Currently applied classical approach to electromagnetic tests may, due to being time consuming and costly, not be enough to keep up with the fast-growing technology.
It seems that with the further intensive development of electronics (and with that the IoT devices intensive development), it will be necessary to use complex (hybrid) EMC test methods to ensure the proper operation of systems consisting of so many components. What might be interesting as a “bonus” with such an approach – we can simultaneously get significant reduction of the research, development and test costs.
Even already today measuring devices and standards using the latest measurement techniques are being developed and used to provide real-time measuring capabilities, not just the current practice and the classical emission testing approach, performed only by frequency scanning. This approach, combined with classical (possibly modified) measurement methods and test settings, covers not only individual devices but also entire systems, allowing for better, more reliable EMC testing of IoT devices equipped mainly with ultra-fast processors.
In this paper, the authors took the attempt to gather and present the broadest possible spectrum of ideas and suggestions for how to deal with the massive increase in the number of collaborating or co-existing high-tech electronic devices due to the increased development of IoT devices.
REFERENCES
[1] http://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf [2] IEC 61000-4-39 Electromagnetic compatibility (EMC) – Part 4-39: Testing and measurement techniques – Radiated fields in close proximity – Immunity test [3] EN 55022 Information technology equipment– Radio disturbance characteristics– Limits and methods of measurement [4] EN 55024 Information technology equipment – Immunity characteristics – Limits and methods of measurement [5] EN 55032 Electromagnetic compatibility of multimedia equipment – Emission requirements [6] DIRECTIVE 2014/53/EU OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 16 April 2014 on the harmonisation of the laws of the Member States relating to the making available on the market of radio equipment and repealing Directive 1999/5/EC [7] DIRECTIVE 2014/30/EU OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 26 February 2014 on the harmonisation of the laws of the Member States relating to electromagnetic compatibility [8] EN 61967-x Integrated circuits – Measurement of electromagnetic emissions – Part x:… (series of standards) [9] EN 62132-x Integrated circuits – Measurement of electromagnetic immunity – Part x:… (series of standards) [10] Langer G., Is EMC prepared to handle the challanges of the Internet of Things, Interference technology 2016, ITEM 28 April 2016
Authors: mgr inż. Marek P. Michalak, Instytut Łączności – Państwowy Instytut Badawczy, Zakład Kompatybilności Elektromagnetycznej, ul. Swojczycka 38, 51-501 Wrocław, e-mail: M.Michalak@itl.waw.pl; mgr inż. Monika E. Szafrańska, Politechnika Wrocławska, Wydział Elektroniki, ul. Janiszewskiego 11/17, 50-372 Wrocław, e-mail: monika.szafranska@pwr.edu.pl
Source & Publisher Item Identifier: PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 94 NR 2/2018. doi:10.15199/48.2018.02.12
Published by Pietro Tumino, EE Power – Technical Articles: An Overview of Energy Storage Systems and Their Applications, September 18, 2020.
This article will describe the main applications of energy storage systems and the benefits of each application.
The continuous growth of renewable energy sources (RES) had drastically changed the paradigm of large, centralized electric energy generators and distributed loads along the entire electrical system.
Nowadays, there are many renewable energy resources located much closer to industrial, commercial, or residential areas. This is called “distributed generation.” It is estimated that in the years to come, distributed generation will become more and more evident.
Energy sources like sun and wind are not predictable and subject to sudden changes, furthermore, their integration with current thermoelectric plants is not easy. Considering the continuous increase of renewable energy sources, large-scale thermoelectric plants may reduce their operating power.
Methods of managing the electrical system will need to be modified in response to changes introduced by renewable energy generation.
An energy storage system can provide relevant support to the electrical system for the integration of renewable energy sources.
Main Applications for Energy Storage Systems
Energy Time Shift
This application is quite common and it is one of the main applications already operated by traditional pumped-storage hydroelectric plants. It consists of “buying” energy when the market price is low (by absorbing energy from the grid, ie: charging the batteries or moving the water on the top reservoir in case of hydroelectric pumping) and selling it when the market price is higher.
The benefits of this application are not strictly related to the economic advantages of selling energy at higher prices. Indeed this “energy moving” contributes to increasing the energy demand when it is lower and decreasing it when higher. This leads to so-called “peak shaving,” reducing the impact of the peaks in both generation curve and load curve, resulting in a “smooth” curve shape. This is then easier to predict and easier to manage.
Figure 1. An example of Peak shaving.
A similar application would be to compensate for the energy fluctuations of renewable generators, due to intermittence of the primary source, in order to achieve a more regular generation profile easier to predict.
Voltage Support
Voltage control is a crucial point of an electrical energy system, usually achieved by the reactive power regulation on each generator. This service could be performed by an energy storage system. The voltage control performed by the energy storage system can also fall into the application category of “power quality” as it is very useful to increase the quality of the service provided by the distributor system operator.
Figure 2. An example of Voltage variation out of standard range. Image courtesy of Planetarkpower.
Frequency Regulation (primary, secondary, and tertiary)
Frequency fluctuations can occur when an electrical system’s generation is not matched to the load. These variations are mitigated by a complex control system in which energy storage systems can easily operate, particularly those with a quick response time such as pumped-storage hydroelectric systems or electrochemical systems.
Congestion Management
When network portions subject to power transfer are close to their maximum power limit, the energy storage system can be operated to “cushion” this power transfer, without stopping generators and with no need to apply further investment on the electrical network.
Black Start
For the portions of a network subject to a possible blackout, the inconveniences arising from it can be reduced by using an energy storage system, which could supply enough power to the users affected by the black-out. The ESS could be also used in case of a general blackout for the re-starting of the entire electrical system.
Battery Energy Storage Systems
As mentioned above, there are many applications for energy storage systems and several benefits for the electrical system where an energy storage system is present.
The type of energy storage system that has the most growth potential over the next several years is the battery energy storage system.
The benefits of a battery energy storage system include:
• Useful for both high-power and high-energy applications • Small size in relation to other energy storage systems • Can be integrated into existing power plants • Ease of installation • The price of batteries decreases with continued adoption and availability
Despite technological progress, storing electrical energy in a universally inexpensive way is an ongoing issue. In terms of cost, storing electrical energy remains quite expensive and the main price reductions are related to economy scale due to the market expanding.
Author: Pietro Tumino received his MSEE from the University of Catania in March 2012. His great passion for renewable energies brought him to join Enel Green Power, where he has worked since November 2015, starting at Solar Centre of Excellence in the Solar Design unit/Engineering and now as Project Engineer. He focuses on the design of photovoltaic plants, planning and coordinating photovoltaic projects in the development and execution phases. Previously he worked at Enel Distribuzione, focusing on network technology unit/remote controls and automation systems and helping the development and testing of solutions for smart grids. In his downtime, he loves football, playing guitar, and rock music.
Published by Bilal Asad 1,2,* , Hadi Ashraf Raja 2 , Toomas Vaimann 2 , Ants Kallaste 2 , Raimondas Pomarnacki 3 and Van Khang Hyunh 4
Abstract: An algorithm to improve the resolution of the frequency spectrum by detecting the number of complete cycles, removing any fractional components of the signal, signal discontinuities, and interpolating the signal for fault diagnostics of electrical machines using low-power data acquisition cards is proposed in this paper. Smart sensor-based low-power data acquisition and processing devices such as Arduino cards are becoming common due to the growing trend of the Internet of Things (IoT), cloud computation, and other Industry 4.0 standards. For predictive maintenance, the fault representing frequencies at the incipient stage are very difficult to detect due to their small amplitude and the leakage of powerful frequency components into other parts of the spectrum. For this purpose, offline advanced signal processing techniques are used that cannot be performed in small signal processing devices due to the required computational time, complexity, and memory. Hence, in this paper, an algorithm is proposed that can improve the spectrum resolution without complex advanced signal processing techniques and is suitable for low-power signal processing devices. The results both from the simulation and practical environment are presented.
Keywords: electrical machine; machine learning; data acquisition; FEM; signal processing; Arduino; artificial intelligence
1. Introduction
The research in the predictive maintenance of electrical machines is touching new horizons. Cloud computation and distributed low-cost sensors are integral for Industry 4.0 standards. They can also be considered a paradigm shift in the predictive maintenance of electrical machines. Low-cost data acquisition sensors are becoming essential as electrical machines are becoming increasingly popular in small and medium-range electric vehicles. The research in the field of condition monitoring of electrical machines using stator currents [1–3], stator voltages [4–6], speed and torque ripples [7,8], stray flux [9–14], vibration analysis [15–19], thermal analysis [20–23], acoustic analysis [24–27], work in the steady-state interval [28], or transient regime [9,29–32] can be considered as mature enough after over a century of research. The research path started with conventional signal processing and harmonic estimation-based techniques. Here, the fundamental rule was to discover the fault-based new frequency components in the machine’s global signal. The signal processing techniques were explored by researchers extensively to secure or protect the tiny, sensitive, fragile, and load-dependent fault-based information. For this purpose, the improvement in the spectrum resolution both in stationary and transient regimes was the common point of interest. To remove the spectral leakage, the best practice both in IEEE and industry standards is to obtain the coherent sampling to the maximum extent [33,34]. A variety of other methods have also been explored in the literature, such as filter banks [35], adaptive filters [36,37], 2D feature [38], optimization of truncating windows [39,40], singular value decomposition [41–43], orthogonal matching pursuit [44–46], interpolated DFT techniques [47], Taylor Fourier transforms [48], multiple signal classification (MUSIC) [49,50], fault estimation using weighted iterative learning [51], auxiliary classifier generative adversarial network [52], and estimation of signal parameters via rotational invariance technique (ESPIRIT) [53]. The complexity of the required memory and calculation time are, however, problems that can limit their application in low-power data processing devices. The next major research domain is the mathematical modelling of electrical machines, as those are essential for the design, control, analysis, and fault-based simulations of electrical machines. The main task on which researchers put a lot of focus is to reduce the approximations and the simulation time of the fault simulation-compatible mathematical models. A large amount of research can be found in literature, ranging from finite element method (FEM) [54] to analytical models such as modified winding function analysis (MWFA) [55–57], reluctance network-based [58], and hybrid models [59,60]. As these models should be detailed and able to simulate every kind of fault, the simulation time and complexity are a big issue. The extended simulation time for fault diagnostics is not acceptable, as in the most advanced diagnostic techniques the simulation should run in parallel with the actual hardware, such as digital twin and hardware in the loop. A considerable research effort regarding the minimization of the simulation time both in FEM and analytical techniques can be found in literature, where [61] used piece-wise polynomial function for model order reduction, [62] used Loewner matrix interpolation, [63,64] used proper orthogonal decomposition, [65] used Krylov subspace techniques, etc. The development of these models opened new research directions where they can be used in the hardware in the loop environment [66], parameters estimation [67,68], digital twin [69], and inverse problem theory [70]. The research in these domains is complicated though due to the complex mathematical models, coupling effects in the motor variables, multiple solution points of the same problem, etc. These problems then opened the field, such as optimization theory [71], probability and stochastic analysis [72], non-linear control theory [73], and statistical analysis [74] of the global signals for the predictive maintenance of electrical machines. The development of these models paved the way towards another more advanced field, artificial intelligence [75]. A significant number of AI-based research articles can be seen in the literature and the number is increasing by leaps and bounds. The accuracy and maturity of AI algorithms depends on the data size and its variety under different loading and faulty conditions. Thanks to the research in the field of mathematical modelling, data collection under different faulty and loading conditions for a variety of different machines is possible using simulations. Moreover, data storage on the cloud can increase the training data set every day. The common point in all conventional and advanced techniques is the input signal. Mostly, the global signals remain the same for all types of machines as the state variables of all machines are almost the same. Now there is a paradigm shift in the measurement of all those signals using low-cost data acquisition devices such as Arduino cards and sending the data in the database without loss or any additional infiltrations such as noise.
In this paper, an algorithm is proposed that can improve the spectral resolution with the help of the following contributions.
1. The integral number of cycles and the signal’s length whose prime factors are appropriate are calculated first. The fractional parts of the signal in the start and end reduce the spectrum’s resolution, and an inappropriate length of the signal with a large number or size of prime factors decreases FFT’s efficiency by increasing the complexity, required memory, and calculation time.
2. The low sampling frequency is the main problem when the data acquisition devices are not very powerful and are intended to work online with systems such as Arduino. In Industry 4.0, those low-cost devices can have significant importance because of Internet of Things (IoT), distributed smart sensors, and cloud computation. The low sampling frequency leads to poor frequency resolution and increased spectral leakage. The main reason for this is sharp changes in the acquired signal. Hence, those sharp changes are proposed to be removed using data interpolation. This step is also important when the diagnostic algorithms depend on the mathematical model of the system. The most accurate models are the finite element method (FEM), based which the computational complexity is always a challenge. By using data interpolation, only the minimum number of steps can be simulated, and the rest of the values can be approximated.
3. Detecting any data discontinuity and removing it. In low power smart sensors, the chances of data loss cannot be neglected. This data loss can happen during its transmission from card to cloud due to network issues, due to some clock issues in the data acquisition card itself, or due to limited memory to save the signal before its transmission. This data loss is fatal for FFT-based spectrum analysis. This is due to the resultant data discontinuities in the acquired signals. So, a method is devised to remove data discontinuity, if any.
4. Repeating the cycles for the improvement in the resolution with minimal discontinuity. The increased number of signal cycles lead to a better frequency resolution. As the current and voltage cycles of the electrical machines working under steady state regime are periodic, they can be repeated to increase the signal’s length. This repetition of the signal should not be random, which can make the resolution worse. Hence, a technique is proposed to repeat the cycles before frequency analysis if necessary.
2. The Theoretical Background
Almost all kinds of faults modulate the machine’s global variables with a particular set of frequencies. The number and the amplitude of those frequency components are a function of the fault type and severity. During the early stages of fault, these harmonics are tiny in amplitude and difficult to detect. They tend to hide themselves under the frequency lobe of the powerful neighboring frequency component. The strength of any diagnostic algorithm is determined from its ability to detect those harmonics at the early stage of the fault. For this purpose, the resolution of the frequency spectrum is of significant importance, which increases with the decrease in the spectral leakage of the powerful frequency components. To reduce the spectral leakage, a variety of advanced signal processing techniques are available in the literature, but at the cost of increased computational time and complexity. It makes those algorithms less suitable for low power signal processing and controller boards. For low power smart sensor-based data acquisition and processing devices, the following fundamental precautionary measures should be accounted for.
5. The signal frequency and sampling frequency must follow conditions of coherency. The perfect coherent data is very difficult to obtain because of measurement equipment limitations and noise. This non-coherency can be avoided by windowing techniques [76]. However, the clever selection of the window is very important to obtain a narrower main lobe with less leakage energy inside the lobes. So, specialized knowledge about the windowing function and its impact on the spectrum is needed to deal with the problems, which cannot be a very easy solution. The drawback of FFT is that any mismatch between the sampling frequency and signal frequency can cause spectral leakage.
6. The signal should have an integer number of cycles. The fractional parts of the signal in the start or end increase the spectral leakage and increase the requirement of windowing function. This approach will increase the efficiency of FFT, will reduce the dependency on windowing function, and will reduce spectral leakage, even if the signal is noisy or its frequency is near the Nyquist rate. The quality of the frequency spectrum can be checked by measuring the signal to noise ratio (SNR), total harmonic distortion (THD), spurious free dynamic range (SFDR), signal to noise and distortion ratio (SNDR), effective number of bits (ENOF), etc. The number of cycles in a signal can be calculated as
.
In this equation, J is the total number of cycles, fin is the frequency of the fundamental component of the near sinusoid signal, fs is the sampling frequency, M is the recorded signal’s length, Jint are the integral number of signal cycles, and Δ is the fractional part. The non-zero Δ leads to the spectral leakage.
A signal from time domain to discrete domain can be represented as
.
where hh represents the higher order harmonics and can be defined as follows: anand bnare the Fourier coefficients.
.
In squirrel cage induction machines, the main causes of these higher order harmonics are the non-sinusoidal winding distributions, changing airgap reluctance due to rotor and stator slot openings, inherent eccentricity, material saturation, harmonics coming from the supply, and any fault if present in the machine. However, all these harmonics are tiny in comparison with the fundamental component and the overall current signal remains near sinusoidal. The initial purpose is to calculate Jint in the acquired signal and discard the fractional part Δ.
The integer number of cycles are calculated in the way that all values greater than the RMS value both on positive and negative half cycle are marked as +1 and −1. All elements are merged into one if the adjacent sign is the same to make a new signal say w[m]. We merge adjacent same values into one element and take the absolute value.
3. The Effect of Discontinuities in the Signal
Although FFT is a very powerful tool that is extensively used in the field of signal processing, for smooth, periodic, uniformly sampled points and long signals, FFT no doubt gives accurate results. However, the results become significantly erroneous if there are singularities or discontinuities in the signals. Thanks to the symmetrical and sinusoidal distributed design and performance parameters of electrical machines, almost all global signals such as current, voltage, and flux are periodic. The data discontinuities are however possible due to the limitations of the data acquisition devices, particularly if those are low power cards. This can be because of network limitations such as delay or loss of data transfer from the device to cloud. Because of the high sample rate, there is a high chance of data loss while data is being transferred from sensors to the low power cards. This is mostly because of the delay in the clearance of the buffers when data are being transmitted for a long time, i.e., a couple of days to weeks. An example of such a data acquisition system is shown in Figure 1.
Figure 1. The schematic diagram of data acquisition and transmission to the cloud using IoT.
Data loss can occur in two scenarios for the above data acquisition setup, while the data are being transferred from sensors to the low powered cards and the other while the data are being transferred from the cards to cloud. The protocols used for data transmission have their own limitations too. The loss of data during transmission can be due to the limitation of network or delay/loss of network while transferring. Another reason might be due to the buffers being overloaded and not being properly cleared up before the next data come in, which can result in a loss of data while in transmission. These sharp changes in the signal are the potential cause of hiding the low power fault-based frequencies due to the increased spectral leakage of significant harmonics. It also decreases the computational time of FFT, decreases its efficiency, and increases the need for increased data length. The experimental setup used to recreate such scenario is shown in Figure 2.
Figure 2. Experimental setup for data collection.
The induction motor is used to collect current signals for all three phases, and it is then transmitted to the cloud using Arduino (low powered card). This is the most common approach used for the data acquisition system when using a low powered card. There are alternate systems that have been proposed that further consider data losses with a local backup of collected data at a node [ref], but the following approach is still widely used. The flow chart of the setup used for data collection for this experiment is shown in Figure 3.
Figure 3. Flow chart for the data acquisition setup.
The setup was run continuously for multiple days with different sampling rates to generate data losses. At higher sampling rates, the data losses occurred more often as the buffer became overloaded. Because of the limitation of the processing power of Arduino (low powered cards), data loss became inevitable in these cases. This is why the sampling rate tended to be on the lower side in most cases, but this also resulted in the samples being too low and similar data loss issues could occur if it kept running for a more extended period. The other scenario was also created by interrupting the network connection. In this case, wi-fi was used to transmit data from Arduino to the cloud database. Upon interruption of the network, as no data were transmitted, this resulted in data being lost. For some protocols, it could result in a delay at the receiving end, but this will still have components lost for the received signal. The setup was used to obtain signals with data discontinuity to check the result of the proposed algorithm.
The data discontinuities were detected by making a moving subtraction filter. The amplitude difference of every two consecutive samples defined the magnitude of discontinuity in them. For example, in Figure 4, nine discontinuities along with their amplitude are discovered that need correction.
.
Figure 4. (a) The acquired stator current, (b) the result of moving subtraction filter for the detection of discontinuities, and (c) after the correction of discontinuous samples.
For correction, the discontinuous sample is replaced with the average value of the samples x [n − 1] and x [n + 1]:
.
The integer number of cycles can be calculated using zero cross detection, but, in that case, wrong computation can occur if there is any data discontinuity in the signal. If there are more than one consecutive missing data samples then there are some possible methods of correction. Replace the missing samples with the samples from the same location of the subsequent cycle. The other way is that the samples will be replaced by random values, depending on the amplitude of the available samples at the start and end of the missing segment and the amplitude will be iteratively corrected. The third way is that if the cycles are affected in a worse manner, then it can be totally replaced with the healthy one from the signal. This paper at the moment deals with only one discontinuity between two healthy samples.
4. Counting the Integral Number of Cycles and Removing the Fractional Parts
The integral number of cycles are calculated in the following steps.
A. The samples of the acquired stator current are compared with the RMS value. The samples with a magnitude greater than the RMS value for both the positive and negative side are replaced with one, while all of the other samples are replaced with zero as shown in the equation below and Figure 5b.
.
Figure 5. (a) The stator current with red line representing the RMS value, (b) the samples validating the conditions given in b, (c) the shifting of negative samples towards positive side by taking modulus, and (d) merging the consecutive samples of same value in one.
B. The modulus of the resultant vector is taken to shift the negative-sided samples to the positive side, as shown in Figure 5c.
C. The consecutive samples with same magnitude are merged into one and represented in Figure 5d. The final number of samples on the zero or unity axis are equal to the number of signal cycles.
After counting the number of cycles, the data are saved until the index of steric completing the integral number of cycles in Figure 5d. Now, two types of discontinuities may still persist in the signal: the minor discontinuity due to low sampling frequency, as shown in Figure 6, and the possible discontinuity at te starting and ending time.
Figure 6. The estimation of intermediate solutions using data interpolation.
Both problems can be solved by signal interpolation. It will not only improve the smoothness of the signal, but also refine the zero crossing points, as shown in Figure 7.
Figure 7. (a) The stator current and approximate zero crossings at a sampling frequency of 4 kHz, (b) the corresponding envelope shifted across zero line with approximate zero crossings, (c) the signal with improved sampling frequency and approximate zero crossings, and (d) the corresponding envelope shifted across zero line with approximate zero crossings.
5. Algorithm
The proposed algorithm is shown in Figure 8. Its main parts include the removal of DC offset which decreases the possibility of a frequency bin at 0Hz in the spectrum, detection and correction of data discontinuities which increase the spectral leakage, removal of starting and ending fractional parts and the repetition of the signal if necessary.
Figure 8. The algorithm for counting the integral number of cycles, removal of signal discontinuities and fractional parts of the signal, data interpolation, and repetition, if necessary.
6. Results
6.1. Simulation Results
The motor’s stator current harmonics can be broadly classified into three major categories: the winding and supply-based odd multiples of the fundamental component, the slotting harmonics, and the fault generated harmonics. The mathematical description of these harmonics is given in Table 1. The fault and slotting harmonics are the function of slip and tend to move in the spectrum as the load varies, while the winding MMF and the supply harmonics retain their position in the spectrum. Electrical machine simulations are necessary for several reasons, such as design, control, analysis, and training of the fault diagnostic algorithms, creation of digital twin, inverse problem theory, hardware in the loop environment, and parameters estimation. However, the biggest drawback of finite element method (FEM) models of electrical machines is the computational complexity and the required simulation time. Moreover, the small step size and the simulation of complete geometry is required for better resolution of the spectrum because for predictive maintenance, the importance of wideband harmonics cannot be denied. For this purpose, the algorithm is first implemented on FEM-based simulation signals with a low sampling frequency. In Figure 9, it can be seen that even at a high step size with a sampling frequency of 4 kHz, the spectrum counting the integral number of cycles increases the resolution significantly without the need for any truncating window. Moreover, the effect of communication channel-based data discontinuities and their correction is shown in Figure 10.
Table 1. Fault definition frequencies.
.
Figure 9. The simulated stator current spectrum showing stator winding and slotting harmonics before and after counting integral number of cycles (INOC).
Figure 10. The effect of signal discontinuities on the spectrum resolution.
6.2. Practical Results
For practical investigations, two similar machines were connected back-to-back. One machine works as a loading machine, while the other was used as a testing motor where the healthy and broken rotor bar carrying rotor were tested. Table 2 shows the nominal parameters of the machine under investigation. Figures 11 and 12 show the improvement in the spectrum resolution by removing the fractional parts of the signal and data discontinuities without any truncating window. The tiny broken rotor bar harmonics near the strong supply and spatial harmonics became well legible.
Table 2. The machine specifications.
.
Figure 11. The practical stator current spectrum showing stator winding, slotting, and broken rotor bar-based harmonics before and after counting the integral number of cycles (INOC).
Figure 12. The practical stator current spectrum showing stator winding, slotting, and broken rotor bar-based harmonics with and without discontinuities.
The frequency of slotting harmonics in the current spectrum in comparison with their expected frequency according to the equations given in Table 1 as a function of slip is shown in Table 3. It is clear that the amplitude of those harmonics decreases with the decreasing slip, which makes their detection difficult when the machine is working under low or no-load conditions.
Table 3. The rotor slot harmonics (RSH).
.
7. Conclusions
Low sampling frequency, fractional parts of the signal at starting and ending, and data discontinuities in the time domain can lead to spectral leakage in the frequency domain when applying the FFT (Fast Fourier Transform) algorithm. Spectral leakage refers to the effect where energy from a signal at one frequency “leaks” into other nearby frequencies, creating artifacts in the spectrum that are not present in the original signal. There can also be interruptions between the transmitted signals due to limitations of the hardware used or because of a loss of network. This can also lead to data loss or the receiving signal missing some harmonics and having some junk values in between. This can further lead to an incorrect analysis of the collected signal, and, in some cases, it might even be more fatal, i.e., could lead to the machine being damaged if the issue occurs in the case of monitoring an electrical machine.
One way to mitigate these effects is by applying a window function to the data before performing the FFT. A window function can smooth out the signal at the edges of the analysis window, reducing the abrupt changes and thus the spectral leakage. However, even with a window function, some level of spectral leakage may still be present, depending on the characteristics of the signal and the choice of window function. Moreover, the application of advanced signal processing techniques makes it computationally complex for low power data acquisition and processing devices.
This paper shows how a simple algorithm can improve the spectrum resolution by removing the above-mentioned problems.
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Author Contributions: Conceptualization, B.A., H.A.R. and T.V.; methodology, B.A. and H.A.R.; software, B.A. and H.A.R.; validation, T.V., A.K. and R.P.; formal analysis, V.K.H.; investigation, B.A.; resources, T.V.; data curation, B.A.; writing—original draft preparation, B.A. and H.A.R.; writing— review and editing, T.V.; visualization, A.K.; supervision, V.K.H.; project administration, V.K.H. and T.V.; funding acquisition, T.V. and V.K.H. All authors have read and agreed to the published version of the manuscript.
Funding: The “Industrial Internet methods for electrical energy conversion systems monitoring and diagnostics” benefits from a 993.000€ grant from Iceland, Liechtenstein, and Norway, through the EEA Grants. The aim of the project is to provide research in the field of energy conversion systems and to develop artificial intelligence and virtual emulator-based prognostic and diagnostic methodologies for these systems. Project contract with the Research Council of Lithuania (LMTLT) No is S-BMT-21-5 (LT08-2-LMT-K-01-040). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available within the article. Conflicts of Interest: The authors declare no conflict of interest.
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Authors: 1 Department of Electrical Power Engineering, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan 2 Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 12616 Tallinn, Estonia; hadi.raja@taltech.ee (H.A.R.); toomas.vaimann@taltech.ee (T.V.); ants.kallaste@taltech.ee (A.K.) 3 Department of Electronic Systems, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania; raimondas.pomarnacki@vilniustech.lt 4 Department of Engineering Sciences, University of Agder, 4879 Grimstad, Norway; huynh.khang@uia.no * Correspondence: bilal.asad@taltech.ee
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