The IEC 61000-4-30 Class A standard defines the measurement methods, time aggregation, accuracy, and evaluation, for each power quality parameter to obtain reliable, repeatable and comparable results between various brands and models of PQ instruments and systems.
IEC 61000-3-30 Class A Edition 2
IEC 6100-4-30 Class A Edition 2 standardizes the measurements of:
Power frequency
Supply voltage magnitude
Flicker (by reference to IEC 61000-4-15)
Voltage dips/sags and swells
Voltage interruptions
Supply voltage unbalance
Voltage harmonics, and interharmonics (referenced to IEC 61000-4-7)
Mains signaling voltage
Rapid voltage changes
Magnitude of current
Current harmonics and interharmonics (referenced to IEC 61000-4-7)
Current unbalance
IEC 61000-4-30 Edition 3 Introduced new measurements definitions and PQ parameters.
“This third edition cancels and replaces the second edition published in 2008. This edition constitutes a technical revision”.
Rapid voltage changes
Flicker class F1
Magnitude of the current
Current unbalance
Current harmonics (by reference to IEC 61000-4-7)
Current interharmonics (by reference to IEC 61000-4-7)
Additional changes in harmonic parameters from IEEE 519 2014
The number of harmonics to be evaluated. In many application, 50 harmonics are not enough and modern DC to AC inverters used in Wind and Solar generation have significate harmonic component up to the 100th.
Recording resolution – the latest edition of the IEEE 519 requires a daily and weekly harmonic evaluation of both voltage and current at 150/180 cycles (~3sec) resolution per phase. An edition 3 compliant instrument must record this data and prepare a report from the instrument.
Why these revised standards are important to electric utilities?
1. Rapid Voltage Change (RVC) parameter captures voltage changes (sags) that can be disruptive to some loads without exceeding the standard of +/- 5% voltage change limit. An instrument that does not make RVC measurements will miss these events. So a utility may receive customer complaints (most common is light flickers) and not have any data to find the source of the complaint. (most common is large motor starts or other sudden load or distributed generation switching. (tripping)
2. The Edition 3 revision transfers the responsibility for measurement methods continue in this standard, but responsibility for influence quantities, performance, and test procedures are transferred to IEC 62586 -1 and -2.
Part 1, namely IEC 62586-1, was constructed to define a comprehensive PQ device product standard, coined within as PQIs. The standard outlines safety, electromagnetic compatibility (EMC), climatic, and mechanical requirements, and refers to IEC 62586-2 for functional aspects. These requirements serve to ensure the instrument’s robustness will be suitable for its installation within the severe environments of a power station or substation.
Part 2, IEC 62586-24, defines the functional tests cited in the first part of the series. These tests are intended to comprehensively verify the PQ measurement methods outlined in 4-30. This chapter was established to provide traceable and repeatable procedures to verify the compliance of each PQ metric outlined in 4-30. This firstly addresses the main shortcoming of 4-30 and ensures better method adherence between PQ meter manufacturers. Additionally, the standard allows regulatory laboratories adhering to ISO/IEC 170255 to issue conformance reports and certificates according to IEC 62586-1 or IEC 62586-2 (with compliance to IEC 62586-2 meaning compliance to IEC 61000-4-30). The latter provides PQ meter manufacturers a way to provide internationally recognized compliance for the entire scope of PQI requirements.
3. To help ensure accurate PQ metrics in the harsh installation environment of a power station or substation, a number of electromagnetic compatibility (EMC) and influence quantity tests were also added to the scope of the IEC 62586 series.
“IEC 62586-2:2013 specifies functional tests and uncertainty requirements for instruments whose functions include measuring, recording, and possibly monitoring power quality parameters in power supply systems, and whose measuring methods (class A or class S) are defined in IEC 61000-4-30. This standard applies to power quality instruments complying with IEC 62586-1. This standard may also be referred to by other product standards (e.g. digital fault recorders, revenue meters, MV or HV protection relays) specifying devices embedding class A or class S power quality functions according to IEC 61000-4-30. These requirements are applicable in single, dual- (split phase) and 3-phase a.c. power supply systems at 50 Hz or 60 Hz.”
4. Environmental impact on the instrument from a laboratory environment. (25 Degrees C to a substation environment 40 Degrees C + ) is now part of the requirement of this standard. Detailed measurement procedures for Harmonics including to the 100th are included. Reporting of the harmonics to IEEE 519-2014 with harmonic limits specified for 1 and 1 week are included.
5. Detailed measurement procedures for Harmonics including to the 100th are included.
6. Reporting of the harmonics to IEEE 519-2014 with harmonic limits specified for 1 and 1 week are included.
All of these issues can be defined as IEC 61000-4-30 Class A, Edition 3 compliant.
Published by Dranetz Technologies, Inc. Tech Tip: What Makes a Power Quality Problem Worth Solving?, Website: Dranetz.com
Most power quality events won’t shut you down. But when they do, they cost more than just a headache.
The challenge isn’t detecting power quality problems. That’s the easy part. The real question is: Do those issues actually matter to your operation? A dip, a transient, a bit of harmonic distortion—none of these are problems on their own. They’re only problems if your systems are vulnerable to them.
Let’s walk through how to determine that, and why proactive monitoring saves more than it costs.
What’s a Power Quality Problem—Really?
Power quality issues come in many shapes:
• Voltage sags or dips • Transients • Swells • Harmonics • Flicker • Interruptions
But none of these are problems unless they affect your systems. If your equipment can handle the wave shape irregularities without missing a beat, no action needed.
The problem comes when susceptibility meets exposure. If your equipment can’t tolerate a dip—or if enough of them add up over time—it’s not just a nuisance. It’s a liability.
For example, during a commissioning test at a data center, step-load tests were used to validate backup generator performance. Monitoring showed voltage stayed within spec during most of the test, but during a 100% impulse load, frequency dipped below 55 Hz. That momentary dip didn’t trip any alarms—but it did reveal the system was skating close to the edge of its tolerance. If the team hadn’t been monitoring, they wouldn’t have known the frequency drift was that severe—or that it could jeopardize compliance with generator specs and UPS tolerances under full load.
Think Beyond the Event: Consider the Cost
Even if your systems are sensitive, that still doesn’t make every event worth fixing. The second piece of the equation is economic exposure.
• What’s the cost of downtime? • How long does recovery take? • What does mitigation cost, and how long until it pays off?
If a $10,000 UPS avoids a $50,000 shutdown every six months, it’s a no-brainer. If that same UPS is protecting a non-critical load that goes offline once a year with minimal impact, that money’s better spent elsewhere.
Smart PQ strategy lives in this gray area—balancing cost, risk, and resilience.
Monitoring Is Your Early Warning System
You can’t make informed decisions if you don’t have the data. That’s where power quality monitoring comes in.
It helps you:
• Spot problems before they escalate (like signs of capacitor switching issues before they damage gear) • Analyze fault cause and location (so you can stop guessing whether it was utility-side or internal) • Protect mission-critical systems with targeted mitigation • Avoid finger pointing—back your ops team with facts
We sometimes call monitoring the DVR for your facility. When someone says, “Something went wrong at 10:42 a.m.,” you can pull the data and say exactly what happened—and just as important, what didn’t.
Where Do These Problems Start?
According to industry data, around 70% of power quality problems originate inside the facility. Not from the utility.
That means most PQ issues are your responsibility. And the good news is that’s also where you have the most control.
Internal culprits include:
• Adjustable speed drives • Poor grounding or wiring • Load switching • Microprocessor-based devices with high sensitivity
Knowing what your system is doing—not just what the utility’s sending you—makes all the difference.
A Tier III data center, for instance, kept experiencing unexplained voltage sags that caused certain racks to reboot intermittently. At first, the center’s technical team suspected issues upstream with the utility feed. But Class A monitoring showed the culprit was internal: a large HVAC unit cycling on under load.
Every time it kicked in, the inrush current created a brief but deep enough sag on the same panel feeding sensitive IT equipment. The monitoring data clearly tied event timestamps to HVAC cycles.
The fix? They added a soft start controller to the HVAC system and moved key server racks to a separate, isolated circuit. No finger pointing. Just facts—and a stable facility..
Standards That Keep You Honest
If you’re investing in monitoring, data consistency matters. That’s why IEC 61000-4-30 Class A compliance is a non-negotiable for serious applications.
It ensures:
• Repeatable, trusted measurements • Side-by-side comparability between meters • Confidence when using data for reporting or compliance
You’ll find this in our HDPQ line—and we were the first to offer it.
“If two meters give you two different answers, you can’t trust either. Class A compliance fixes that.” — Ross Ignall, Dranetz Director of Product Management, Marketing & Technical Support
Even if your utility doesn’t require it, compliance with these standards protects you. They give you confidence when you’re justifying upgrades or troubleshooting downtime.
Wrap-Up: PQ Is About What You Can Control
You can’t stop lightning. You can’t change your neighbor’s harmonic emissions. But you can understand how your systems respond to power quality events—and make smart decisions based on that.
Start with the basics:
• Know your susceptibility • Understand your exposure • Monitor proactively • Use data you can trust
Key takeaway: Monitoring doesn’t just catch problems—it helps you avoid them in the first place.
Stop guessing. Start knowing.
The Dranetz HDPQ line gives you trusted, Class A power quality data—so you can spot problems early, prove what’s happening, and protect what matters.
Published by Dranetz Technologies, Inc. Tech Tip: Power Quality Standards: What You Need to Know, Website: Dranetz.com
If you’ve ever connected two different power quality meters to the same circuit and gotten different results, you’re not alone. It’s an age-old issue in power quality monitoring. And when it happens, the obvious question is: which reading should you trust?
That’s where power quality standards come in. But knowing which ones apply, especially in the U.S., isn’t always straightforward. This article will walk you through the key differences, why measurement consistency matters, and how to choose the right meters to get accurate, defensible data.
Why Standards Matter
When your system throws a red flag and you need answers fast, the last thing you want is to question your readings. Whether you’re presenting findings to management or troubleshooting a system issue, reliable and repeatable PQ measurements are non-negotiable.
Standards help take doubt off the table. They outline the acceptable limits for power quality parameters and how those parameters should be measured. That second part—how—is often overlooked but critical.
Compliance Standards vs. Monitoring Standards
There’s a meaningful difference between compliance standards and monitoring standards, even though they’re closely related.
• Compliance standards define acceptable performance. They set the pass or fail thresholds for things like voltage harmonics, flicker, or power factor. For example, IEEE 519 provides limits for harmonic distortion in power systems.
• Monitoring standards define how those parameters must be measured. This includes everything from sampling to signal processing methods to accuracy tolerances. If you’re not measuring things the right way, it doesn’t matter how strict your compliance thresholds are. Your conclusions could still be off.
In short, compliance standards tell you what to measure and whether the result is acceptable. Monitoring standards tell you how to measure it accurately and consistently. Without both, the data can’t be trusted.
IEEE and IEC: How They Fit Together
In the U.S., we engineers rely on IEEE recommended practices. These are well-regarded and form the foundation for many of our technical decisions. But they haven’t always kept pace with the advances in PQ measurement standards that other regions have adopted.
That’s where the IEC 61000-4-30 standard comes into play. Developed by the International Electrotechnical Commission, it offers a complete framework for how power quality parameters should be measured. It has become the global reference standard, including for many U.S. applications.
IEEE standards like 519 (harmonics) and 1459 (flicker) have started incorporating IEC measurement methods. This is a step in the right direction. Still, for PQ events like sags, swells, and interruptions, IEC 61000-4-30 remains the more comprehensive guide.
Why This Matters in the U.S.
Unlike Europe, where IEC 61000-4-30 Class A meters are often required, the U.S. market is less standardized. There is a mix of older instruments, varied interpretations of IEEE guidelines, and inconsistent data reporting from one facility to the next.
That inconsistency can make it hard to prove a point when you need to. It becomes especially challenging if:
• You’re trying to pinpoint the root cause of downtime • You need to show whether the problem is internal or with the utility • You’re justifying infrastructure upgrades • You’re presenting data to leadership and need full confidence in the numbers
Using a power quality meter that is fully compliant with IEC 61000-4-30 Class A Edition 3 helps resolve these issues. It ensures your measurements are accurate and consistent, regardless of brand or location.
First to Conform to IEC 61000-4-30
Dranetz was the first manufacturer to meet IEC 61000-4-30 Class A requirements. You can be assured our HDPQ family of meters are fully compliant and offer reliable and repeatable measurements.
What to Look For
If you’re in facilities, utilities, or any operation where uptime and compliance are priorities, choose a meter that meets IEC 61000-4-30 Class A Edition 3 requirements. These instruments:
• Provide accurate, standardized measurements of sags, swells, harmonics, flicker, frequency, and more
• Are validated through certified testing defined by IEC 62586
• Support your efforts to meet U.S. standards like IEEE 519-2014 and beyond
Dranetz was the first to bring a fully IEC 61000-4-30 Class A compliant meter to market. Our HDPQ Plus family meets both IEC and IEEE standards, giving you dependable data when it matters most. What’s more, we are certified to IEC 62586 and have the certificate to prove it. Others in the industry claim compliance, but never prove it.
Published by Andi Abdul Halik LATEKO1, Yusri Syam AKIL2, Universitas Muhammadiyah Makassar (1), Hasanuddin University (2) ORCID: 1. https://orcid.org/0000-0002-9002-131X
Abstract. This paper employs a Vector Error Correction Model (VECM) analysis to investigate the influence of economic growth (GDP) and investment (FDI) on electricity consumption (EPC) in Indonesia. By examining annual data from 1971-2019, the study explores the short-term dynamics and long-run equilibrium relationships among the variables. A negative relationship is observed between EPC and GDP in the long run, while a negative relationship exists between EPC, GDP, and FDI in the short term. The short-run analysis reveals that GDP significantly influences EPC at the three-year horizon, and FDI has a significant negative effect on EPC at the one- and two-year horizons. Another result concerning the causality test indicate a unidirectional relationship between EPC and GDP, while EPC and FDI exhibit bi-directional causality. The findings underscore the influential role of GDP and FDI in driving changes in EPC. Understanding these relationships is crucial for policymakers and energy planners in effectively managing electricity demand, infrastructure investments, and sustainable economic growth. This research contributes to the existing literature by providing insights specific to Indonesia, guiding decision-making processes regarding energy infrastructure development, energy efficiency measures, and sustainable economic development.
Streszczenie. W artykule wykorzystano analizę Vector Error Correction Model (VECM) w celu zbadania wpływu wzrostu gospodarczego (PKB) i inwestycji (BIZ) na zużycie energii elektrycznej (EPC) w Indonezji. Analizując dane roczne z lat 1971-2019, badanie bada krótkoterminową dynamikę i długookresowe relacje równowagi między zmiennymi. W długim okresie obserwuje się ujemną zależność między EPC a PKB, podczas gdy w krótkim okresie istnieje ujemna zależność między EPC, PKB i BIZ. Analiza krótkookresowa ujawnia, że PKB istotnie wpływa na EPC w horyzoncie trzyletnim, a BIZ mają znaczący negatywny wpływ na EPC w horyzoncie rocznym i dwuletnim. Kolejny wynik dotyczący testu przyczynowości wskazuje na jednokierunkową zależność między EPC a PKB, podczas gdy EPC i BIZ wykazują dwukierunkową przyczynowość. Odkrycia podkreślają wpływową rolę PKB i BIZ w napędzaniu zmian w EPC. Zrozumienie tych zależności ma kluczowe znaczenie dla decydentów i planistów energetycznych w skutecznym zarządzaniu zapotrzebowaniem na energię elektryczną, inwestycjami w infrastrukturę i zrównoważonym wzrostem gospodarczym. Badania te wnoszą wkład do istniejącej literatury, dostarczając spostrzeżeń specyficznych dla Indonezji, kierując procesami decyzyjnymi dotyczącymi rozwoju infrastruktury energetycznej, środków efektywności energetycznej i zrównoważonego rozwoju gospodarczego. (Analiza VECM dotycząca wpływu wzrostu gospodarczego i inwestycji na zużycie energii elektrycznej w Indonezji)
Keywords: Vector Error Correction Model, economic growth, investment, electricity consumption, Indonesia. Słowa kluczowe: Vector Error Correction Model, wzrost gospodarczy, inwestycje, zużycie energii elektrycznej, Indonezja.
Introduction
The relationship between economic growth, investment (particularly foreign direct investment – FDI), and electricity consumption has received significant attention in the literature. Understanding this relationship is crucial for policymakers and energy planners in formulating effective strategies for sustainable energy development. In the context of Indonesia, a rapidly growing economy in Southeast Asia, it becomes imperative to examine the impact of economic growth and FDI on electricity consumption.
A number of studies have investigated the relationship between economic growth and electricity consumption. For instance, in [1] conducted a study for the middle east and south Africa and found evidence of a positive relationship between economic growth and energy consumption. Similarly, in [2] examined OECD countries and observed a bidirectional relationship between GDP and non-renewable electricity consumption. Next a study for Tunisia found longrun bi-directional causality between GDP and energy consumption [3]. For the impact of FDI on energy consumption, it has also been explored as can be found in the literatures [4-6]. In [4] investigated Pakistan countries and identified a positive relationship between FDI and energy consumption. In [5] focused on Bangladesh and found a bi-directional causality between FDI and energy consumption. Meanwhile in [6] analysed European countries and established a positive and strong relationship between FDI and energy consumption.
Regarding methods for analysis, the VECM approach has been widely used in many studies. For example, in [7] employed a VECM framework to examine the relationship between CO2 emissions, energy consumption, and economic growth in Pakistan and found evidence of a positive and significant relationship between them. In [8] investigated the causal effects between CO2 emissions, use of energy, GDP, and population in India using ARDL and VECM methods and revealed a positive relationship between GDP and energy use.
Moreover, country-specific studies have been conducted to explore the relationship between economic growth, FDI, and electricity consumption. For example, in [9] investigated the impact of renewable energy consumption, GDP, and FDI in Kazakhstan and Uzbekistan. Their study highlighted a two-way relationship between FDI and renewable energy consumption in these two countries. In [10] analysed China and found a positive relationship between renewable energy, FDI, and economic growth. Besides that, several studies have examined the relationship between the two variables and energy consumption using advanced econometric techniques [11-14]. The authors in [11] conducted a causality analysis between energy consumption, FDI, and GDP for several countries (Mexico, Indonesia, Nigeria, and Turkey), and established a long-run equilibrium relationship between these variables. In [12] examined Benin countries and found a significant long-run relationship of electricity consumption, FDI, and GDP. In [13] focused on 13 MENA countries and observed a positive relationship between energy consumption, ICT, FDI, and economic growth. Another study in [14] employed a decomposition scale approach to investigate the impact of financial development and FDI on renewable energy consumption for 39 countries.
The existing literatures provide valuable insights into the relationship between economic growth, FDI, and electricity consumption for some different countries. However, limited research has been conducted specifically for Indonesian context. This study proposes a VECM approach to analyse the impact of economic growth and FDI on electricity consumption in Indonesia. The analysis focus on the short-term dynamics and long-run equilibrium relationships between the observed variables. Besides can fill the research gap, resulted information can provide more insights for decision-making processes regarding energy infrastructure development, energy efficiency measures, and sustainable economic development in Indonesia. Some related studies for the context of Indonesia can be found in [11, 15-16].
The remainder of the paper organized as follows. The second section describes data and methodology. In Section 3, the obtained results and analyses for each stage are highlighted. In the final section, the conclusion and future works of the study are presented.
Methodology
The analysis in this study focuses on examining the relationship between Electric Power Consumption (EPC), Gross Domestic Product (GDP), and Foreign Direct Investment (FDI) in Indonesia over a period of 48 years, from 1971 to 2019. The data for each variable is obtained from the World Bank [17]. Figure 1 provides a visual representation for the trend of each variable over the years. It is evident from the figure that EPC, GDP, and investment have shown a consistent increase. For instance, the per capita primary energy consumption has risen from 14.2969 kWh in 1971 to approximately 1084 kWh in 2019. Similarly, the GDP has grown from 9.333 billion USD in 1971 to 1119.099 billion USD in 2019, while investment has increased from 0.299 billion USD in 1971 to 24.993 billion USD in 2019. The increasing trends in these variables make it intriguing to investigate their interrelationships further. To do so, this study employs co-integration and causality analyses, including unit root tests to assess data stationarity, lag selection processes for determining optimal lag length, Johansen co-integration tests to identify long-run relationships, and Vector Error Correction Model (VECM) analysis to examine both short-term dynamics and long-run equilibrium relationships among the variables [18].
Fig.1. Electric power consumption, economic growth, and investment from Year 1971 – 2019 in Indonesia.
Results and Analysis
A. Unit Root Test
The stationary properties of the observed variables are examined using Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests, and the results are summarized in Table 1. The tests reveal that the variables are not stationary in their levels, as indicated by the p-values exceeding 0.05. However, when tested in first differences, all variables (EPC, GDP, and FDI) exhibit p-values below 0.05, indicating stationarity after differencing (non-stationary data are rejected at a 5% significance level). Therefore, the variables are considered stationary at first differences.
Table 1. Results for unit root test
.
B. Optimal Lag Length for VECM Model
The next step involves determining the optimal lag length for the VECM model. Lag order selection is crucial for obtaining a better model fit. In this study, several common lag selection criteria are utilized, including the Sequential Modified LR Test Statistic (LR), Final Prediction Error (FPE), Akaike Information Criterion (AIC), and Schwarz Criterion (SC). The values obtained for each lag selection criterion are presented in Table 2. Based on the results, the optimal lag length for the VECM model is identified as the fourth lag, and because the data was differencing, the lag used in the next step is 3. This determination is supported by the values of the applied selection criteria, where the lowest values are consistently obtained at the fourth lag, as observed in the LR, FPE, and AIC criteria. Subsequently, the stability of VECM model is assessed. Figure 2 displays the inverted values of the characteristic roots, revealing that the majority of these
Table 2. Results for lag length selection
.
Fig.2. Unit root distribution chart.
Inverse roots of AR characteristic polynomial values fall within the unit circle. This observation suggests that the constructed VECM model is stable and suitable for the subsequent step of the co-integration test analysis.
C. Co-integration Analysis
In this step, the focus is on observing the long-term relationship among the EPC, GDP, and FDI variables. To examine the relationship, a co-integration test using the optimal lag length from the previous step is conducted, employing the Johansen co-integration test. The results of the co-integration test are presented in Table 3. The values of the Trace statistics and Maximum Eigen statistics indicate whether the null hypothesis can be rejected at a 5% significance level or if a co-integration relationship (R = 0) does not exist. Additionally, the null hypotheses concerning the existence of at most 1 and 2 co-integration relations (R ≤ 1 and R ≤ 2) are also rejected at the same significance level. These findings suggest the presence of more than 3 co-integration equations, indicating that the analysed variables exhibit a shared tendency over a long period. Co-integration signifies a systematic co-movement among the variables considered in the model [19]. Consequently, it can be concluded that EPC, GDP, and FDI in Indonesia have a long-run relationship.
Table 3. Results for co-integration test
.
D. VECM Granger Causality Analysis
In the final stage of this study, the VECM Granger causality test is applied to the model using the differenced data obtained in the previous step. This test is utilized to examine the short-run and long-run causal relationships between the variables included in the model. Table 4 presented VECM results. The presence of significant coefficients with a negative sign suggests a long-term relationship between the variables, while coefficients with a non-significant negative sign indicate a short-term dynamic relationship [20]. The error correction mechanism reveals a short-term relationship among all the variables. In the long term, there exists a negative relationship between EPC and GDP. However, in the short term, there are indications of a negative relationship between EPC, GDP, and FDI.
Table 4. Long-term and short-term relationships of the Vector Error Correction
.
Equation (1) shows the co-integration formula of the model:
In the long-term, there exists a negative relationship between EPC and GDP, while a positive relationship is observed between EPC and FDI. This implies that an increase in EPC in Indonesia encourages the FDI to rise, while concurrently leading to a decrease in GDP.
The analysis of the causality relationship among the variables using the VECM model reveals important findings. Specifically, the results indicate that GDP has a negative and significant impact on EPC at the three-year horizon, while FDI demonstrates a negative and significant effect on EPC at the one- and two-year horizons. These results, which show the causality relationship among the variables, are presented in Table 5.
Fig.3. Impulse responses of the variables.
In order to assess the causal relationship between the variables, the Granger causality test is employed. The results of this test, which shows the causal relationship between the variables, are presented in Table 6. At a significance level of 5%, it is observed that there exists a unidirectional causal relationship between the variables EPC and GDP. Specifically, the GDP variable significantly influences EPC as indicated by a F-statistic probability below 0.05, namely 0.0208 (leading to the rejection of the null hypothesis). Additionally, a bidirectional causality is found between EPC and FDI. However, there is no causal relationship observed between GDP and FDI. These findings confirm that GDP and FDI play crucial roles in driving the increase in EPC. Therefore, it is essential for stakeholders to facilitate greater access and reduce constraints in utilizing electric power consumption to achieve high levels of economic growth and investment.
Table 6. VEC Granger Causality
.
In order to assess the impact of disturbances on the variables under consideration, the impulse response function is employed. This function provides insights into the timing and magnitude of the variables’ responses to disturbances originating from other variables [21]. Figure 3 illustrates the general impulse responses of EPC, GDP, and FDI to innovations (other variables), respectively. The results demonstrate a significant and gradual increase in the response of GDP and FDI to EPC over a 10-year period.
Conclusions
This paper focuses on conducting co-integration and VECM causality analysis within the Indonesian context, considering three key variables: electric power consumption (EPC), GDP, and FDI. The analysis reveals that all the variables exhibit a long-run relationship, which is confirmed through co-integration analysis utilizing the Johansen cointegration test. In the long run, a negative relationship is observed between EPC and GDP. However, in the short term, there are indications of a negative relationship between EPC, GDP, and FDI. Specifically, the results reveal that in the short-run causality analysis, GDP has a significant negative impact on EPC at the three-year horizon. Additionally, FDI shows a significant negative effect on EPC at the one- and two-year horizons. The causality test results indicate a unidirectional causal relationship between EPC and GDP, with GDP significantly influencing EPC. Furthermore, a bi-directional causality is observed between EPC and FDI, while no causal relationship is found between GDP and FDI. It is evident that the volume of GDP and FDI serves as driving factors for the increase in EPC. Consequently, stakeholders, including the government, play a crucial role in reducing constraints and facilitating access to electric power consumption in relevant sectors, potentially through policy interventions. These efforts are essential for stimulating rapid economic growth and attracting foreign investment. It should be recognized that the level of economic growth directly impacts foreign direct investment, thereby increasing the likelihood of foreign investors to invest in various sectors in Indonesia. The findings of this study hold significant value for public policymakers involved in designing energy policies, particularly for the electricity sector, to effectively support economic growth and foreign investment in Indonesia. For future research, we will consider more variables for application, such as the long-term prediction of electricity consumption.
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Authors: Andi Abdul Halik Lateko, Ph.D., Department of Electrical Engineering, Universitas Muhammadiyah Makassar, Indonesia, E-mail: halik@unismuh.ac.id (corresponding author); Yusri Syam Akil, Ph.D., Department of Electrical Engineering, Hasanuddin University, Indonesia, E-mail: yusakil@unhas.ac.id.
Source & Publisher Item Identifier: PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 100 NR 2/2024. doi:10.15199/48.2024.02.28
Published by Olga Pilipczuk, University of Szczecin ORCID: 0000-0001-7078-2544
Abstract. Currently, edge computing supports many solutions in the energy industry and has attracted the research interest of many researchers. The edge computing concept is relatively novel, the first impactful works were noticed in 2014-2015 years. Thus, there is a lack of studies summarizing the research progress on edge computing in the energy industry. The overview of current research on edge computing is substantial for comprehensive understanding of the research status and future perspectives in this field. The aim of the study was to define and present the current state of research on edge computing applications in the energy sector. To analyse the research trends and perspectives in scientific works development, the bibliometric approach was used. The data were extracted from Web of Science and Scopus platforms. The results comprise the keywords analysis, research field analysis, geographic distribution overview, time trends analysis, as well as author and their affiliation analysis. Additionally, the analysis of the most cited paper has been provided and showed by means of word cloud image. The findings of this research allowed to define the perspectives and future directions for edge computing in the energy sector.
Streszczenie. Obecnie przetwarzanie brzegowe wspiera wiele rozwiązań w branży energetycznej i wzbudza zainteresowanie wielu badaczy. Koncepcja obliczeń brzegowych jest stosunkowo nowa, pierwsze znaczące prace zauważono w latach 2014-2015. Brakuje jednak opracowań podsumowujących postęp badań nad przetwarzaniem brzegowym w energetyce. Przegląd aktualnych badań nad przetwarzaniem brzegowym jest istotny dla kompletnego zrozumienia stanu badań i przyszłych perspektyw w tej dziedzinie. Celem podjęcia badań było określenie i przedstawienie aktualnego stanu badań nad zastosowaniami obliczeń brzegowych w energetyce. Do analizy kierunków badań i perspektyw rozwoju prac naukowych wykorzystano podejście bibliometryczne. Dane zostały pobrane z platform Web of Science i Scopus. Wyniki obejmują analizę słów kluczowych, analizę pola badawczego, przegląd dystrybucji geograficznej, analizę trendów czasowych, a także analizę autorów i ich afiliacji. Dodatkowo przedstawiono analizę najczęściej cytowanych prac, którą przedstawiono za pomocą obrazu chmury słów. Wyniki tych badań pozwoliły określić perspektywy i przyszłe kierunki rozwoju obliczeń brzegowych w sektorze energetycznym. (Przegląd zastosowań obliczeń brzegowych w energetyce)
Keywords: edge computing, energy sector, bibliometric analysis Słowa kluczowe: przetwarzanie brzegowe, sektor energetyczny, analiza bibliometryczna
Introduction
Although the term edge computing (EC) is a relatively novel concept, it is becoming a popular alternative to IoE and IoT applications [1]. In brief, EC is a new computing model that analyzes and processes portions of data using compute, storage, and network resources distributed in paths between data sources and cloud data centers [2]. Edge computing focuses on short-term real-time data analysis on the device side and can better support real-time local analysis and enterprise intelligent processing [3]. Meanwhile, it has several useful features, such as decentralization, low latency, high efficiency, and reduced traffic pressure, making it more efficient and secure compared to simple cloud computing [3]. It can also be useful for smart home and smart city applications based on collaborative edge [2].
Edge computing brings many advantages to emerging problem solutions. Edge computing in pare with fog computing are attractive solutions to the problem of data processing on the Internet of Things [4]. Edge computing enables a new generation of intelligent applications that can take advantage of the latest advances in artificial intelligence and machine learning. It brings the many benefits of cloud computing to the world of OT, including containerization, virtualization, and modern approaches to application orchestration and updates [5].
New edge computing solutions can also revolutionize the energy industry [5]. Edge-enabled, high-voltage products will build the foundation for the Internet of Energy (IoE) [6]. Nowadays, energy efficiency has become one of the most significant topics for both cloud servers and mobile devices [7]. Though energy efficiency in cloud data centres has been thoroughly investigated, energy efficiency in edge computing is largely left investigated due to the complicated interactions between edge devices, edge servers, and cloud data centres [7]. EC can reduce the amount of data traversing the network. It moves the processing power from the cloud to a point closer to the end user or device. EC enables smarter grids and allows enterprises to better manage their energy consumption.
EC has a key role in supporting smart grid applications such as demand management and grid optimization. Sensors and IoT devices connected to edge platforms in factories, plants, and offices are used to monitor energy consumption and analyse energy levels in real time [8]. By tracking and monitoring energy consumption in real time and visualizing it through dashboards, companies can better manage their energy consumption and take preventative measures to limit energy usage [8]. This article is meant to serve as a survey of recent advancements in Edge computing highlighting the core applications.
Summarizing all the above, many works were published on the EC topic. However, there is a lack of publications on bibliometric analysis on EC in the energy sector. Thus, the aim of this paper is to present the current state of research on edge computing applications in the energy sector and define the main perspectives and challenges.
The paper is structured as follows: after the introduction section the materials and methods are described. Next, the results extracted from WoS and Scopus databases are presented and the discussion on the main findings is provided. In the final part of the paper, the conclusions and future work perspectives are presented.
Research
The research procedure was planned as follows. The procedure begins with the research field identification. After that, the research data base was selected. For above mentioned in this part reasons, the bibliometric study presented was focused on the Web of Science and Scopus databases separately. The following key words were used: “Edge computing” AND “energy”.
The following bibliometric parameters were analysed: publication type, research fields, years, countries, affiliations and authors, funding sources.
First, the analysis by publication type was conducted. The most popular type of publication was original article and conference/proceedings paper according to both databases (see Fig. 1, 2). Moreover, according to WoS the 53 book chapters, 162 conference reviews, 103 reviews and six books were published as well. The number of book chapters occurring in Scopus database was smaller. 0 books and just 3 book chapters were published. The number of review articles was similar and counted 94 papers.
Fig.1. WoS publications on edge computing by paper type
Fig.2. Scopus publications on edge computing by paper type
In the next step, the publication analysis in dynamics from 2014 to 2022 was done together with the prediction till 2024 year by forecast linear trend line application (see Fig. 3,4). Although the beginning of the publication period in the WoS and Scopus databases was recorded only in 2015, the increase in number of publications was rapid. In 2021, it exceeded 1 thousand papers.
Fig.3. WoS publications on edge computing by years
Fig.4. Scopus publications on edge computing by years
Analyzing the publications by countries, the comparable results were extracted from both databases (see Fig.5,6). The huge advantage of China was noticed; more than 2300 publications. Its contribution was several times greater than other countries. The second place obtain the USA, the third – England/United Kingdom.
In the next step, the publication analysis in dynamics from 2014 to 2022 was done together with the prediction till 2024 year by forecast linear trend line application (see Fig. 3).
Although the beginning of the publication period in the WoS and Scopus databases was recorded only in 2015, the increase in number of publications was rapid. In 2021, it exceeded one thousand papers.
Fig.5. WoS publication on edge computing by countries
Fig.6. Scopus publications on edge computing by countries
China’s scientific institutions were also the leaders in category by affiliations (see Fig.7,8). The leading position obtain the Beijing University of Posts and telecommunication with more than two hundred publications, which is approximately one tenth of the total number of Chinese publications.
The most publications occurred in IEEE Society journals such as “IEEE Access,” “IEEE Internet of Things” journal, “IEEE transactions on Vehicular technology,” “IEEE Transactions in Wireless Communications”, and others. Among other publishing houses, the two MDPI journals were popular, namely “Sensors” and “Electronics” (see Fig. 9,10). Among others, Elsevier, Hindawi and Wiley publishing houses were popular. Lecture notes in Computer science by Springer were the popular choice for conference papers.
Fig.7. WoS publication analysis on edge computing by affiliation
Fig.8. Scopus publication analysis on edge computing by affiliation
Fig.9. Publication analysis on edge computing by title
Fig.10. Publication analysis on edge computing by title
The author analysis reveals significant differences between WoS and Scopus databases (see Fig. 11). In WoS database, three leading positions were obtained by Zhang Y, Chen X and Liu Y. In Scopus database, the leading positions were obtained by Han Z, Xu X., Guizani M. In Scopus, Zhang Y obtained the ninth position. And, vice versa, in WoS Han Z obtained the 10th position.
Fig.11. Publication analysis on edge computing by author (a) according to WoS database; (b) according to Scopus database
Due to fact that most of the works were published by China, the great amount of research was funded by Chinese organizations. The advantage of National Natural Science Foundation was noticed in WoS database as well as in Scopus database.
A great amount of research was funded by Chinese organizations such as the National Natural Science Foundation, National Key Research Development Program, China Postdoctoral Science foundation, Beijing Natural Science foundation etc. (see Fig.12,13). The huge advantage of the National Natural Science Foundation was noticed. 247 papers were funded by the National Science Foundation from the USA. More than one hundred research were founded by European Commission. 129 of Scopus publications were funded in terms of the Horizon 2020 Framework Program.
Fig.12. WoS publication analysis on edge computing by funding organizations
Fig.13. Scopus publication analysis on edge computing by funding organizations
At the last step the following keywords were extracted for keyword cloud construction (Figure 14): “mobile edge computing”, “fog computing”, “mobile cloud computing”, “computation offloading”, “resource management”, “green computing”, “mobile network architecture”, “computation offloading”, “allocation of computing resources”, “mobility management”, “standardization”, “use-cases”, “Internet of Things”, “enabling technologies”, “security and privacy, and applications”, “energy harvesting”, “dynamic voltage and frequency scaling”, “power control”, “QoE”, “Lyapunov optimization”, “non-orthogonal multiple access”, “resource allocation”; “modelling and simulation”, “survey”, “partial computation offloading”, “dynamic voltage scaling”, “collaboration between communication and computation resources”, “mobile edge caching”, “D2D”, “SDN”, “NFV”, “content delivery”, “energy minimization”, “small cells”.
Fig.14. Keyword cloud based on most cited papers.
Discussion and conclusion
To create the overview of state of art on EC in the energy sector the obtained results were presented using tree map showed on Figure 15. The figure described the most popular type of paper, journal, the most productive author, university, founding organization and country which have leading position in the field.
Fig.15. The overview of the papers on EC progress in energy sector.
It should be mentioned that this research has several limitations. First, the data was analysed by means of WoS and Scopus databases analyses tool are restricted to their capabilities of analysis. On the other hand, it allows to avoid several difficulties and challenges mentioned in materials and method section. Another limitation is that the research includes publications in English, and it is recommended that future research include documents published in other languages.
Despite the quick speed of increasing the number of publications on EC, it is still in its infancy stage in many countries. The small number of scientific publications on bibliometric analysis of EC is owed to its novelty in comparison to other emerging technologies such as AI, big data etc.
However, the current research concentrates around several counties and affiliations creating challenges in EC widespread.
The data extracted from WoS and Scopus databases allowed to define the following findings:
• The research history on EC starts from 2014.
• The research on EC was concentrated in several countries, several institutions and research teams.
• A small number of scientific publications on bibliometric analysis on Edge Computing due to its novelty in comparison to other emerging technologies such as AI, Big data etc.
• Quick speed of increasing the number of publications on EC.
The keyword cloud together with the literature study allow to define the current most popular publication trends on EC applications in energy industry. It was founded that the main future directions for edge computing development in energy industry are the following: mobile edge computing, sustainability issues in edge computing, smart grids, security and privacy issues, green edge computing and IoE, smart cities. Moreover, to make the research areas on EC more sustainable the more research should be funded in other geographic regions, especially in EU countries.
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Authors: dr Olga Pilipczuk, Uniwersytet Szczeciński, Instytut Zarządzania, ul. Cukrowa 8, Szczecin, olga.pilipczuk@usz.edu.pl
Source & Publisher Item Identifier: PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 100 NR 5/2024. doi:10.15199/48.2024.05.45
Published by Elbrus AHMEDOV1, Nadir ALİYEV2, Seymur SADIQOV3,Azerbaijan State Oil and Industry University (1, 2) ORCID: 1. 0000-0003-3348-7946; 2. 0009-0001-3165-1493
Abstract. The present article delves into strategies aimed at augmenting the impulse electrical strength of the insulation in high-voltage transformer windings. The research focal point centers on an authentically produced autotransformer boasting a 330 kV voltage rating, serving as the object of inquiry. The investigation systematically probes the influence of coil winding methods on both the electrical robustness of the winding and the insulation between the discs. To assess the insulation reliability inherent in a series-wound contra shield, adhering to the transformer method, a laboratory examination was conducted by the IEC 60076-3 standard, specifically designed to evaluate the impact induced by a direct lightning strike. Subsequently, a simulation model was constructed within the VLN program to ascertain the voltage distribution in the unconventional interleaved winding approach of an alternating transformer. A lightning impulse voltage test was executed, pinpointing critical junctures. A comparative analysis was then undertaken to discern the disparities between the results obtained. Remarkably, in contrast to the contra shield method featuring wound windings, the interleaved winding technique exhibited superior impulse electrical strength and, concurrently, proved to be cost-effective.
Streszczenie. W artykule omówiono strategie mające na celu zwiększenie udarowej wytrzymałości elektrycznej izolacji w uzwojeniach transformatorów wysokiego napięcia. Główny punkt badań skupia się na autentycznie wyprodukowanym autotransformatorze o napięciu znamionowym 330 kV, będącym przedmiotem badań. W badaniach systematycznie badany jest wpływ metod nawijania cewek zarówno na wytrzymałość elektryczną uzwojenia, jak i izolację pomiędzy tarczami. Aby ocenić niezawodność izolacji właściwą dla przeciwosłony szeregowo uzwojonej, stosującej metodę transformatorową, przeprowadzono badania laboratoryjne zgodnie z normą IEC 60076-3, specjalnie zaprojektowane do oceny uderzenia wywołanego bezpośrednim uderzeniem pioruna. Następnie w programie VLN zbudowano model symulacyjny w celu ustalenia rozkładu napięcia w niekonwencjonalnym podejściu do uzwojenia przeplatanego transformatora przemiennego. Przeprowadzono test napięcia impulsowego pioruna, identyfikując krytyczne punkty. Następnie przeprowadzono analizę porównawczą, aby dostrzec rozbieżności pomiędzy uzyskanymi wynikami. Co ciekawe, w przeciwieństwie do metody przeciwosłonowej obejmującej uzwojenia uzwojone, technika uzwojenia przeplatanego wykazała doskonałą wytrzymałość elektryczną impulsu, a jednocześnie okazała się opłacalna. (Zwiększanie udarowej wytrzymałości elektrycznej izolacji uzwojeń transformatorów wysokiego napięcia)
High-voltage transformers are one of the most important elements of electrical energy transmission and supply systems. For a transformer to operate reliably for a long time, the electrical strength of its insulation must withstand both operating and impulse voltages. The causes of damage to the insulation of power transformers are electric and magnetic fields, gas and hydrodynamic processes, temperature, and various external factors such as humidity, pollution, etc., which are consequences of complex physical and chemical processes occurring under their influence [1–4]. The main condition for the development of damage is that the action of the electric field in the insulating gap exceeds its electrical strength.
Transformers may be subject to factory test voltage after manufacture or impulse voltage (lightning or short circuit voltage) during operation. The purpose of a power transformer surge voltage insulation test is to check the ability of the transformer insulation to withstand high voltage surges that may occur during a fault or overvoltage. The testing process involves applying a voltage pulse of a certain shape, duration, and amplitude to the transformer winding, followed by measuring the characteristics of the resulting voltage and current [5–8]. However, testing the insulation of a power transformer with a pulse voltage can give both positive and negative results. If the surge voltage is too high or the test is not carried out correctly, there is a risk of damage to the transformer. This can lead to expensive repairs or even the complete failure of the transformer. Therefore, the impulse electrical strength of winding insulation is one of the main factors ensuring the reliability and durability of transformers. Defects within the insulation.
The purpose of the presented work is to study ways to increase the pulse electrical resistance of the insulation of the windings of high-voltage transformers.
As mentioned, when high-voltage transformers are exposed to pulsed voltage, several electrophysical processes can occur in the winding and insulation of the turns. Such processes include [9–11].
• charging and discharging a capacitor: when a pulse voltage is applied to the winding of a transformer, the turns behave like capacitors and are charged and discharged depending on the duration and frequency of the pulse. This can cause a strong electric field to appear in the winding insulation and lead to partial discharge and breakdown;
• electromagnetic induction-pulse voltage creates an electromagnetic field in the transformer winding, which can cause eddy currents in the wires and local heating of the insulation, causing breakdown; heating of the dielectric: a strong electric field can cause heating of the dielectric in the insulation, thermal degradation, and subsequent breakdown;
• electromechanical stresses: impulse voltage can cause mechanical stresses in both the winding and the insulation, which can lead to deformation and cracking of the insulation.
Problem setting
Under the influence of impulse voltage, transition processes develop in the transformer winding, in the adjacent winding elements – in the insulation between the windings or disks, and also between the grounded parts of the transformer – the magnetic conductor and the coil. To see the transition processes, let’s look at the equivalent electrical circuit of one phase (for example, high voltage) of the transformer winding (Fig.1). To study the electromagnetic processes taking place here, let’s introduce elements connected in series to the coil, for example, windings and coils. Each branch of the circuit consists of the inductance ΔL of the element, its capacitance to ground ΔC, and the capacitance ΔK between the elements.
Fig.1. Replacement circuit of the transformer
When transformer windings are exposed to atmospheric pulse voltages, the current flowing through the capacitances of the windings is comparable to the currents flowing through the inductances (and even significantly exceeds them at the initial stage) due to the high rate of change in voltage before the pulse. It is possible to increase the impulse electrical strength of the insulation of high-voltage transformer windings using various methods:
• correct choice of insulating material: The selection of insulating materials significantly impacts the impulse dielectric strength of transformer insulation. Insulating materials with high breakdown voltage, low dielectric loss, and good thermal conductivity can enhance the impulse electrical reliability of the system. The most commonly used insulation materials include cellulose paper, pressboard, and epoxy resin. Currently, composite materials based on polymer nanocomposites with high pulse electrical resistance have also been developed [14, 15].
• insulation processing technology: The manufacturing process of transformer insulation significantly affects the impulse dielectric strength of the insulation system. Techniques such as vacuum impregnation and partial discharge detection can be employed to improve the quality of the insulation system. Additionally, the use of high-quality insulation materials, proper drying methods, and precise temperature control during the manufacturing process can help enhance the impulse resistance of the insulation system.
• design optimization: Optimizing the transformer winding design and insulation system allows for an increase in the dielectric strength of the pulse. For instance, the impulse dielectric strength of an insulation system can be elevated by methods such as increasing the thickness of the insulation layers, improving the quality of the connection between the layers, and ensuring uniform voltage distribution. Moreover, the use of alternative winding methods makes it possible to reduce the electric field strength inside, thereby increasing the electrical strength of the pulse.
Let’s examine the impact of the winding method on the electrical strength of the insulation in a high-voltage transformer. The subject of our investigation is a newly developed single-phase autotransformer with a voltage rating of 330 kV, manufactured by ATEF. The relevant parameters are outlined in Table 1.
Table 1 High voltage transformer parameters
.
The winding configuration employed for this transformer wound using the contra shield method. The circuit is augmented with supplementary elements designed to enhance the relatively uniform distribution of electrical voltage across the windings and the pulsed electrical insulation resistance.
In this particular arrangement, shielding electrical cardboard (depicted in Fig.2.,1) and an additional shielding coil (illustrated in Fig. 2.,3) are systematically positioned between the disks. The specifications of the structural components comprising the high-voltage winding are detailed in Table 2.
Fig.2. Winding diagram of a 330 kV high-voltage transformer with 54B-62B disk numbering: 1 and 2 denote the high-voltage winding input, 3 represents additional shielding cardboard insulation, and 4 represents shielding turns.
Table 2. Structural parameters of the high-voltage winding of the transformer 330 kV
.
To determine the reliability of the transformer insulation, a test was carried out using a pulse voltage generator (BREMER Transformatoren GmbH-GEOS) according to the IEC60076-3 standard [16-19]. Tests were carried out under lightning impulse voltage (LI) and lightning impulse chopped (LIC). The parameters of the lightning pulse supplied to the high-voltage winding of the transformer are in Fig. 3.The transformer was successfully tested and the insulation electrical strength was determined to be at the required level [20-22].
The advantageous attribute of the winding technique illustrated in Figure 2 lies in its technological simplicity and enhanced temporal efficiency during coil preparation. Nevertheless, a notable drawback of this method manifests in including a shielding coil, necessitating the utilization of supplementary materials, notably cardboard and copper. Furthermore, the incorporation of a shielded winding results in a diminution of coil turns, attributed to spatial constraints, and introduces challenges in attaining resilience against prescribed lightning test voltages commensurate with voltage classes of 400 kV and above.
Fig.3. The form of the lightning impulse (LI) test voltage applied to the 330 kV power transformer and its parameters: Ut= -1040.448 kV; T1 = 1.055 µs; T2 = 41.630µs.
As is commonly understood, a disk-type winding comprises a configuration of turns wound in the radial direction. Notably, the number of turns on a given disk may exhibit variability. However, the augmentation of turn count, concomitant with an enlargement of radial dimensions, induces an escalation in the potential difference across the terminal turn of the coil. Concurrently, the inter-disk spacing gives rise to inductance and capacitance, thereby engendering variable high voltage levels within discrete circuit segments during lightning impulse testing. This variability in voltage distribution may, consequently, lead to insulation breakdown, a curtailed operational lifespan for the transformer in its entirety, and, in certain instances, even precipitate transformer failure during testing.
We conducted a comparative analysis of various winding methods, namely the contra shield and interleaved techniques, intending to enhance the electrical strength of transformer winding insulation by achieving a uniform electric field between turns. The consideration of interleaved methods aims to diminish the strength and nonuniformity of the electric field between disks without the need for additional shielding winding. The alteration of primary and secondary turn directions during coil winding in the interleaved manner facilitates a more even distribution of the electric field between disks, thereby mitigating the risk of breakdown attributed to partial discharge. This results in the uniform distribution of alternating voltage across transformer disks, maintaining a constant voltage between adjacent turns. Nevertheless, it is essential to acknowledge the capacitance effect, which may lead to voltage surges and consequently higher voltages between windings at the loop’s termination.
We employed the VLN program to assess the effectiveness of the proposed Interleaved methods against different overvoltage’s and test voltages [23]. With the VLN program, transformer testing can be simulated at any voltage. Furthermore, the VLN program enables the measurement of potential differences between windings, between windings and grounded components, and simultaneously between specific points on turns (disks) through the application of a lightning impulse voltage. Utilizing these parameters, reports can be generated, allowing for the determination of the insulation resistance of the transformer windings to pulse voltages.
Fig.4.a, shows the contra shield winding method of the real transformer winding, and Fig.5.b, shows the proposed interleaved winding method.
Fig.4. Transformer Winding Methods a – the contra shield winding method of the real transformer; b- the winding with the interleaved method
Fig.5 depicts the actual 330-kV transformer, while Fig.6 illustrates the simulation outcomes of the lightning voltage impact on the proposed transformer, which features windings wound using the interleaved method. Each curve in both figures (Figure 5 and Figure 6) represents the voltage and its distribution in the form of a pulse wave, respectively, affecting the gap between individual discs. Given that the high-voltage winding of the transformer receives input from its central part, the voltage’s impact on the spacing of the discs above and below that point is identical. For simplicity, the simulation results are presented solely for the lower half of the winding.
Fig.5. Calculation diagram under the influence of FW 1.20/50 μs of a real transformer, wound using the contra shield method, in the VLN program. a-curves of the distribution of voltage falling on the disk gap; b – the sign of the curves corresponding to the serial number of the disks
Fig.6. Calculation diagram under the influence of FW 1.20/50 μs of a real transformer, wound using the interleaved method, in the VLN program: a-curves of the distribution of voltage falling on the disk gap; b – the sign of the curves corresponding to the serial number of the disks.
Table 3. Voltages occurring between the disks of a real 330 kV transformer.
.
Table 4. Voltages occurring between the disks in the winding wound using the interleaved method of the transformer.
.
Table 3 presents simulated voltage values occurring between the discs of the 330 kV actual transformer, while Table 4 details the voltages in the windings of the interleaved transformer. These values were computed using the VLN program. The tables also include parameters like maximum voltage between the disks, duration, minimum safety factor, and others. The tables exclusively provide parameters for critical intervals. Through an analysis of the obtained values, it was concluded that intervals 48-53 (Tables 3 and 4), situated closer to the high-voltage input section, are critical points.
A comparative assessment of parameters in these tables reveals that the insulation resistance against impulse electric voltages in the winding of windings is notably high for both methods. However, when considering the safety factor, the interleaved winding exhibits superior safety indicators compared to windings employing the contra shield method. Consequently, in the interleaved winding, the minimum safety factor attains a higher value due to a relatively more evenly distributed electric field between the disks. As a result, the application of the interleaved winding method in high-voltage transformer windings yields more effective results.
Conclusions
The study aimed to investigate the impact of the winding method employed in the high-voltage winding of a 330 kV transformer on its impulse electrical strength. Lightning impulse electrical voltage, in accordance with the IEC60076-3 standard, was applied to assess the insulation’s electrical strength. To enhance the impulse electrical strength of the transformer’s insulation and achieve uniformity in the electric field between windings and disks, a comparative analysis of simulated and experimental results was conducted using the VLN program. This analysis considered winding methods employing contra shield and interleaved techniques. While both winding methods exhibited high resistance to impulse electric voltages during winding, a safety factor-based assessment revealed superior safety indicators for windings employing the interleaved method compared to those wound using the contra shield method. Based on the results of the analysis, it was determined that the impulse electrical strength of the insulation during winding with the interleaved method has a higher value than that of the contra shield winding method. Moreover, the interleaved method eliminates the need for an additional shielding winding, preventing additional material loss. Simultaneously, it facilitates a further increase in the impulse electrical strength of the insulation for 330 kV and higher voltage transformers.
REFERENCES
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Authors: Ahmedov Elbrus Nasi. Associate Professor, Candidate of Physical and Mathematical Sciences. Head of the Department of Electromechanics Azerbaijan State University of Oil and Industry. Baku city, Azadlyg avenue 20, E-mail: elbrusahmed@gmail.com . Sadiqov Seymur Erestun. Master of the Department of Electromechanics of the Azerbaijan State University of Oil and Industry. Baku city, Azadlyg avenue 20, E-mail: seymursadiqov59@gmail.com
Source & Publisher Item Identifier: PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 100 NR 8/2024. doi:10.15199/48.2024.08.30
Published by 1. Róbert Štefko1, 2. Michal Kolcun1, 3. Marek Bobček1, 4. Damian Mazur2 , 5. Bogdan Kwiatkowski2, Technical University of Košice (1), Rzeszow University of Technology (2) ORCID: 1. 0000-0002-2477-4559, 2. 0000-0002-8041-9076, 3. 0009-0004-1912-211X; 4. 0000-0002-3247-5903, 5. 0000-0001-5287-2191
Abstract. The continuous rise in electricity consumption and the integration of renewable energy sources into distribution grids are gradually posing challenges to conventional protection systems. This trend significantly impacts traditional centralized methods of electricity generation, shifting towards local production and consumption and moving closer to emerging microgrids. This aspect was specifically considered during the development of the current power system we utilize. To ensure the successful emergence of microgrids, it’s imperative to begin addressing the issue of protecting these renewable energy sources, especially considering that our current devices lack communication or remote-control capabilities. The size of the topology and the number of devices managed at each point in a microgrid will play a crucial role. Therefore, addressing the overall management of individual devices and resources within the microgrid is essential.
Streszczenie. Ciągły wzrost zużycia energii elektrycznej i integracja odnawialnych źródeł energii z sieciami dystrybucyjnymi stopniowo stawiają wyzwania konwencjonalnym systemom ochrony. Ten trend znacząco wpływa na tradycyjne scentralizowane metody wytwarzania energii elektrycznej, przesuwając się w kierunku lokalnej produkcji i zużycia oraz zbliżając się do powstających mikrosieci. Ten aspekt został szczególnie rozważony podczas opracowywania obecnego systemu energetycznego, z którego korzystamy. Aby zapewnić pomyślne powstanie mikrosieci, konieczne jest rozpoczęcie zajmowania się kwestią ochrony tych odnawialnych źródeł energii, zwłaszcza biorąc pod uwagę, że nasze obecne urządzenia nie mają możliwości komunikacji lub zdalnego sterowania. Rozmiar topologii i liczba urządzeń zarządzanych w każdym punkcie mikrosieci będą odgrywać kluczową rolę. Dlatego też zajęcie się ogólnym zarządzaniem poszczególnymi urządzeniami i zasobami w mikrosieci jest niezbędne. (Projekt systemu zabezpieczeń rozproszonych źródeł energii w sieci dystrybucyjnej)
Keywords: protection system, protection relay, distribution grids, distribution energy sources. Słowa kluczowe: system zabezpieczeń, przekaźnik zabezpieczający, sieci dystrybucyjne, dystrybucyjne źródła energii.
Introduction
The integration of renewable energy sources (RES) into existing energy grids heralds a transformative shift, marked by reduced transmission losses and heightened operational reliability. However, this transition brings forth a host of new challenges, particularly in the realms of protection, control, and fault localization systems. Addressing these challenges necessitates ongoing research and innovation in distribution grid technology, with microgrids emerging as a beacon of promise for the future.
One of the defining characteristics of microgrids is their remarkable adaptability, seamlessly transitioning between islanded operation and grid-connected mode. This flexibility is empowered by their inherent self-control capabilities, allowing them to autonomously manage energy generation and consumption. In the event of system faults or disruptions, microgrids can swiftly switch to islanded mode, drawing upon local and renewable energy sources to maintain power supply reliability.
Despite the technical complexities inherent in microgrid design, the benefits they offer outweigh the associated concerns. An essential aspect of microgrid planning is the careful selection and integration of energy sources, balancing local generation with renewable inputs to optimize grid performance. This strategic energy mix is pivotal in mitigating control and protection system challenges within distribution grids, ensuring robust and reliable operation.
Renewable energy units embedded within microgrids fundamentally alter the traditional energy distribution paradigm. By situating energy production closer to consumption points, microgrids enable bidirectional energy flow, effectively decentralizing power generation. This paradigm shift prompts critical questions regarding energy mix optimization, sustainability, and grid flexibility, particularly in response to diverse weather conditions and demand fluctuations.
Navigating these challenges requires a reevaluation of conventional grid infrastructure. A comparative analysis, as depicted in Fig. 1, underscores the significant departure from the radial energy flow model observed in traditional power systems. In contrast, microgrids facilitate bidirectional energy flow by shortening distribution lines and integrating distributed energy sources, paving the way for a more resilient and adaptable energy landscape.
Fig.1. Illustrates the disparity in energy flow directions between conventional power systems and microgrids [1].
Definition of Microgrid
Microgrids can be defined as small local distribution grids that supply electrical energy to consumers, generating electricity through distributed energy sources. These grids must achieve self-sufficiency in electricity production, necessitating an appropriate energy mix based on geographic location to meet load demands.
According to a European Union research project, a microgrid encompasses low-voltage distribution systems with distributed energy resources, storage devices, energy storage systems, and flexible loads. These systems can operate connected or disconnected from the main grid. In essence, a microgrid is a modern autonomous energy distribution system primarily powered by local renewable energy sources [2].
Similarly, the U.S. Department of Energy defines a microgrid as a group of interconnected loads and distributed energy resources within clearly defined boundaries, acting as a single controllable unit concerning the grid. Microgrids can connect and disconnect from the grid, allowing operation in both connected and islanded modes [2].
Protection of distributed sources of electrical energy connected to the distribution grid is essential for several reasons. Inverters include a built-in protection system and perform self-checks in case of faults. Additional protection systems, such as circuit breakers, circuit protection devices, fuses, and surge protectors, are employed to safeguard the inputs. However, protective relays utilized in low-voltage grids often have limited functionality, primarily monitoring frequency and voltage, and detecting ground faults or disconnections during grid outages.
Current State of Research
Current research suggests various perspectives on the further development of smart grids and microgrids. While one of the current research trends indicates the use of Phasor Measurement Units and centralized processing of measured data, only after the development of new protective relays with enhanced mutual communication. A similar direction is also taken by article [3], in which the research team focused on introducing a new method for developing protective relays based on simulation and applying it to the development of localized devices for protecting distribution lines. Applying this method to the development of localized devices for protecting distribution lines and verifying its effectiveness and accuracy through comparing the simulated model with physical testing on relays.
The article [4], similarly to the previous one, suggests the utilization of simulation. It explores the necessity of hard-in-the-loop simulation (HILS) for cooperative protection research in meshed distribution grids. It highlights the issues with protection in these grids and proposes HILS as a solution. The authors analyse traditional testing methods and present a case study demonstrating the effectiveness of HILS. Overall, it represents an important contribution to addressing issues in the field of electrical power distribution.
The article [5] proposes an adaptive protection strategy for power distribution systems with distributed generation (DG), specifically addressing relay malfunctions in radial distribution systems integrated with photovoltaic (PV) sources. It combines Fuzzy Logic (FL) and Genetic Algorithm (GA) to dynamically adjust relay settings based on changes in PV capacity and load demand, aiming to enhance system reliability without infrastructure redesign. By analysing scenarios and comparing with traditional methods, the study demonstrates the effectiveness of FLGA in optimizing relay operation, offering a valuable solution for improving power distribution systems amidst increasing DG integration.
The article [6] examines the influence of integrating distributed photovoltaic (PV) systems on distribution grid protection, particularly regarding low voltage ride-through (LVRT) events. It points out that conventional protection methods may not adequately address the altered fault characteristics resulting from PV integration. The paper proposes a distributed PV LVRT control strategy to mitigate these impacts while providing support during faults. Through analysis and simulations, the strategy’s effectiveness is demonstrated, paving the way for improved fault management in active distribution grids.
The article [7] outlines a collaborative effort with ENEL Distribution São Paulo to enhance intelligence, automation, and protection in the low voltage (LV) overhead distribution grid. It introduces self-healing methodology for LV grids, previously utilized in medium voltage (MV) grids. The development and improvements to LV Control equipment for real-time monitoring and transformer protection are discussed, along with simulations validating the strategy’s effectiveness. The article highlights the need for advanced monitoring and protection equipment in LV grids and introduces an innovative solution to address this gap.
The article [8] addresses challenges in implementing relay protection systems in distribution grids due to the integration of distributed energy resources, proposing an automated system to adjust protection settings. It outlines an adaptive dynamic protection scheme and optimization methods, along with the development of microservices for automatic calculation and adjustment of parameters. Experimental results demonstrate the system’s effectiveness in accurately adjusting protection settings for various scenarios, highlighting its potential to enhance grid reliability. Overall, the article presents a promising solution to evolving distribution grid challenges through automated protection adjustment systems.
Protection Relays for Distribution Grids: Ensuring Electrical System Security
Programmable protective relays are electronic devices used in transmission and distribution electrical grids to detect and respond to various faults and anomalies in the system. These relays can perform various protective functions and are programmable according to specific requirements and needs of the distribution grid. Each manufacturer of these relays has its own way of setting the parameters. Their main task is to monitor electrical parameters such as voltage, current, and frequency, and in case of detecting abnormalities or faults, to trigger protective actions such as disconnecting a section using a contactor. Programmable protective relays are an important component of distribution grid protection systems and help ensure safe and reliable operation of electrical grids.
In the territory of the Slovak Republic, there are three electricity suppliers: the Western Slovak Distribution Company (ZSD), the Central Slovak Distribution Company (SSD), and the Eastern Slovak Distribution Company (VSD). These distribution companies in Slovakia manage high-voltage lines and ensure the supply of electricity to consumers. Each of the recommended protective relays according to Table 1. has been tested by VSD and meets strict requirements for response time and measurement accuracy. Currently programmable protective relays are applied in the territory of the Slovak Republic according to the requirements of the local Eastern Slovak distribution energy company (VSD) for sources exceeding 10 kW.
Table 1. Approved types of external grid protection by VSD [9]
.
When connecting a larger source exceeding 10 kW, it is necessary to apply approved programmable relays as specified by the distribution company managing the area where the source will be installed. For the low-voltage level (LV), the standard wiring diagram for direct measurement by an electricity meter and a smart meter is depicted in Fig.2. Additionally, other components such as surge protectors are part of the assembly, which are additionally protected by 100A fuses, although they are not shown in the diagram in Fig. 2. For the DC part of the system, which connects to the inverter, similar protection of individual interconnected panels into so-called strings and the installation of surge protection on each such string is also necessary.
According to the current requirements of VSD, when connecting a local source to the distribution grid with a capacity up to 10 kW, it is not necessary to install external grid protection for such a source (integrated grid protection cannot be set according to VSD requirements). However, when using more than one inverter with a total installed power exceeding 10 kW, it is necessary to install external grid protection for such a source, which will control the main disconnecting point. The approved types and manufacturers of external grid protection devices according to the VSD company are shown in Table 2. along with their purchase prices [9].
Fig.2. Schematic representation of a protective system for sources from 10 kW to 50 kW connected to a LV distribution grid
Table 2. The required settings of the external gird protection according to the VSD for LV grid [9]
.
For such external grid protections, there is a relatively simple setup using only basic functions to monitor the qualitative parameters of the generated electrical energy. The settings and description are displayed in Table 2.. This external grid protection will primarily function as a monitoring relay. Upon exceeding specified limits, it will trigger a signal to change the state of the output contacts, whose condition can be set for controlling the main disconnecting point. In the context of the protective system, it’s crucial to note that the protective relay solely focuses on the main disconnecting point, neglecting to monitor the operational states of other components within the protection system. Consequently, the electricity distributor is only privy to information regarding the consumer’s electrical energy transactions with the grid – whether it be consumption from or supply to the grid. When considering the integration of distributed energy sources with a capacity of up to 110 kW, the fundamental principle aligns with the protection strategy discussed earlier.
However, a notable deviation lies in the requirement for a current instrument transformer to facilitate accurate measurement of the consumer’s production or consumption by the smart meter. This additional component, depicted in Fig. 3, plays a pivotal role in enhancing the monitoring and management capabilities of the system, ensuring efficient and reliable operation.
The installation of larger energy sources is typically addressed for industrial enterprises that possess relatively large rooftops capable of accommodating such capacity. While conventional photovoltaic stations are also situated in open fields, the prevailing trend indicates a notable increase, especially within the industrial sector. Upon connection to the medium-voltage (MV) level, consideration must also be given to the installation of a transformer and an additional protection system.
Fig.3. Schematic representation of a protective system for sources from 50 kW to 110 kW connected to a LV distribution grid
In such instances, the billing measurement responsibility shifts from the distributor to the higher voltage side, as industrial areas often incorporate their own substations on the consumer’s premises. Consequently, the addition of a voltage instrument transformer becomes necessary to enable the smart meter to measure consumption accurately.
When considering inverters larger than 110 kW, as shown in Fig. 4, the complexity of the protection system increases significantly. The distribution company now mandates monitoring the status of each protective device, necessitating the connection of all individuals signalling states of circuit breakers and fuses to the protective relay. Additionally, there is a requirement for remote monitoring capabilities through dispatching. Consequently, the use of the same protective relays as in previous cases is not feasible, as the protective relay must comply with secure protection protocols in addition to the required protective functions.
Fig.4. Schematic representation of a protective system for sources from 50 kW to 110 kW connected to a LV distribution grid
One of the most widely deployed relays in Europe in recent years is the SEL-751 digital relay, typically installed alongside the RTAC-3505 device for dispatching needs. For microgrid applications, a similar combination will be required to protect the LV level. An issue arises with the overcurrent function when the microgrid transitions from grid-connected operation to islanded operation. If the microgrid relies solely on sources with inverters, utilizing the overcurrent function becomes considerably challenging. This is since the contribution of inverters amounts to a maximum of approximately 120% of the inverter’s nominal current, sustained for a maximum of only 5 seconds [10], [11].
To address this challenge, digital protection must detect the state of control elements to adjust the settings to a more sensitive level, ensuring grid safety. However, implementing this concept in practice for fault location poses difficulties. Increasing the sensitivity of the protection may result in minimal differentiation between fault current (the starting current of the protection) and normal current or the starting current of motors in industrial areas [12].
Currently, VSD does not consider the use of local small sources during a black start due to the necessity to monitor the grid frequency for inverters during synchronization with the grid. Consequently, small renewable energy sources (RES) are automatically connected only after 300 seconds from the restoration of power by the grid [13].
Table 3. The required settings of the external gird protection according to the VSD for a power range: 100kW≤ PN≤5MW for LV grid [9]
.
Table 4. The required settings of the external gird protection according to the VSD for a power range: 100kW≤ PN≤5MW for MV grid [9]
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The comparison between Table 3. and Table 4. sheds light on the nuanced considerations required in configuring external grid protection systems. In Table 3., we find the standard settings designated for power sources ranging from 100 kW to 5 MW when integrated into the LV grid. Conversely, Table 4. offers a glimpse into settings tailored for the same power range but intended for connection to the MV grid. While exploring this comparison, it becomes evident that deviations emerge, particularly concerning the trip time setting for voltage drop or rise at the first level, as evident when comparing Table 2. and Table 3.. However, despite these discrepancies, it’s noteworthy that the remaining settings largely align between the two tables.
Moreover, the method of connection to the grid and the specific voltage level play pivotal roles in determining the appropriate settings. This becomes particularly evident when analysing the settings for indirect measurement from Table 4., where we observe identical configurations, albeit specifically designed for the secondary side measurement. Consequently, these voltage values demonstrate a notable reduction, as visually depicted in Fig. 4.
This comprehensive comparison underscores the critical importance of considering grid connection specifics and voltage levels when configuring external grid protection systems [14]. The observed discrepancies not only highlight the need for meticulous attention to detail but also underscore the necessity for tailored approaches based on the unique characteristics of the grid. By implementing customized settings that account for these nuances, it becomes possible to ensure optimal grid safety and functionality, thereby mitigating risks and enhancing overall system reliability.
Design of a Protection Relay
Currently, there is a lack of universal relays on the market that would be able to meet strict criteria for various options of connecting renewable energy sources (RES) to the distribution grid. With the future deployment of microgrids and smart grids in mind, having such universal devices becomes a crucial necessity.
These systems will need to accommodate various grid topologies, diverse characteristics of RES, and their potential impact on the operation of distribution grids. As microgrids and smart grids increasingly rely on RES, it is essential to have reliable and flexible relays capable of effectively managing and protecting these new energy systems. From the needs described in the previous chapter, it is evident that the device must provide only a few inputs and outputs.
At the same time, it is important for it to have integrated cybernetic security function in the form of Anti-Malware technology, ensuring an elevated level of security. Such a device should include a comprehensive set of security features for user access, configuration management, and monitoring. This will ensure that the system is protected against potential cyber threats and capable of maintaining the integrity and reliability of its operating environment. Each output channel is equipped with both normally open and normally closed contacts, providing both switching and break functions for enhanced versatility. This design feature ensures compatibility with a wide range of devices and systems, allowing users to adapt the device to different scenarios and operational needs easily.
The proposed device, as depicted in Fig. 5, is indeed equipped with a diverse array of communication inputs, offering enhanced flexibility and choices for the user. The USB input facilitates local configuration, permitting users to conveniently adjust settings directly on the device. Moreover, this USB input can also be linked to the internet via an RJ-45 connector, enabling remote access and configuration of the device. Additionally, there exists a communication port tailored for connecting an intelligent meter using standardized RS-232 serial communication, ensuring compatibility with both present and forthcoming systems.
Fig.5. Design of a new device for protecting RES in Distribution grid
Another notable benefit is the inclusion of HMI (HumanMachine Interface) access, which facilitates local control, settings adjustment, and device monitoring. This feature significantly improves user experience and management efficiency. Overall, this solution furnishes a comprehensive and adaptable platform for the management and safeguarding of renewable energy sources.
Further, the device is equipped with two output options, namely OUT1 and OUT2. These output channels provide versatility in connecting and controlling external devices or systems based on specific operational requirements. The availability of multiple output channels enhances the device’s utility and compatibility with various applications, offering users greater flexibility in configuring and managing their renewable energy systems. Each output channel is equipped with both normally open and normally closed contacts, providing both switching and break functions for enhanced versatility. This design feature ensures compatibility with a wide range of devices and systems, allowing users to adapt the device to different scenarios and operational needs easily.
The device evaluates input and output contacts and monitors them based on the measured input data for voltage input contacts L1, L2, L3, and N. Current inputs can be connected to I1, I2, I3, and COM. This configuration allows for comprehensive monitoring and control of the electrical parameters, ensuring efficient operation and protection of the connected renewable energy system. Of course, for a more robust system, input contacts for monitoring the status of individual protective devices will be necessary, which the device will provide in this case. There are up to eight input contacts available, labelled as IN1 to IN8, through which the status of various elements requiring attention from distribution companies when connecting larger power capacities can be detected. These input contacts enable comprehensive monitoring and control, enhancing the safety and efficiency of power distribution operations.
Conclusion
The design of a protection system for distributed energy sources in distribution grids is crucial for ensuring the reliability and safety of electricity supply, especially with the increasing integration of renewable energy sources (RES) and the emergence of microgrids and smart grids.
Addressing the challenges associated with protecting RES requires innovative solutions that cater to various grid topologies, RES characteristics, and operational needs. The proposed device offers a versatile platform equipped with advanced communication inputs, local and remote configuration capabilities, and comprehensive monitoring features.
With the inclusion of HMI access and multiple output options, the device enhances user experience and management efficiency while providing flexibility in connecting and controlling external devices. Additionally, the integration of cybernetic security features ensures the integrity and reliability of the system in the face of potential cyber threats.
Furthermore, the device’s ability to evaluate input and output contacts based on measured data enables efficient operation and protection of connected renewable energy systems. The provision of input contacts for monitoring the status of protective devices enhances safety and efficiency in power distribution operations. In conclusion, the design of a protection system for distributed energy sources in distribution grids marks a pivotal advancement in the realm of electrical grid infrastructure. As the energy landscape undergoes rapid transformation with the integration of renewable energy sources and the proliferation of microgrids and smart grids, the need for robust and adaptable protection systems becomes increasingly pronounced.
The proposed device embodies a holistic approach to addressing the multifaceted challenges posed by these developments. Its versatile architecture, encompassing a diverse array of communication inputs, local and remote configuration capabilities, and comprehensive monitoring features, positions it as a cornerstone in the transition towards a more sustainable and resilient energy ecosystem.
Moreover, the integration of cybernetic security functions underscores a proactive stance towards safeguarding critical infrastructure against emerging cyber threats, ensuring the integrity and reliability of energy distribution networks. This emphasis on security aligns with contemporary imperatives for fortifying infrastructure resilience in the face of evolving digital risks.
Furthermore, the device’s capability to evaluate input and output contacts based on measured data empowers operators with actionable insights for optimizing system performance and mitigating potential risks. By providing a seamless interface for monitoring and controlling renewable energy systems, it enhances operational efficiency and facilitates proactive maintenance strategies.
The inclusion of input contacts for monitoring the status of protective devices further enhances the device’s utility in ensuring grid reliability and resilience. This comprehensive approach to protection system design reflects a commitment to addressing the evolving needs of distribution grids while embracing the principles of sustainability, efficiency, and reliability.
In essence, the design of this protection system represents not only a technological milestone but also a testament to the collective efforts towards building a more sustainable and secure energy future. As we navigate the complexities of modern energy systems, innovations such as this serve as catalysts for progress, ushering in an era of energy resilience and sustainability for generations to come.
Acknowledgments: This work was supported by the Slovak Research and Development Agency under the contract No. APVV-21- 0312 and the Ministry of Education, Science, Research and Sport of the Slovak Republic and the Slovak Academy of Sciences under the contract no. VEGA 1/0627/24.
REFERENCES
[1] ŠTEFKO R., ŠÁRPATAKY M., ŠÁRPATAKY L., et al., Construction and development of microgrids around the world, Elektroenergetika: International Scientific and Professional Journal on Electrical Engineering: Medzinárodný vedecký a odborný časopis pre elektroenergetiku, 15 (2022), no. 1,pp. 16-19, ISSN: 1337-6756. [2] NAREJO G.B., ACHARYA B., SINGH R.S.S. and NEWAGY F., Microgrids Design, Challenges, and Prospects, 1. Edition, 2022, USA: CRC Press. pp. 1–314. ISBN: 978-1-0004-5746-9. [3] LIU H., WANG H., ZHU S., et al., A Simulation-Based Method for Distribution Line Localized Protection Device Development, 2023 IEEE International Conference on Advanced Power System Automation and Protection (APAP), 2023, Xuchang, China, pp. 232–236, ISBN: 979-8-3503-0666-8. [4] NOH J., CHAE W., KIM W., et al., A Study on Meshed Distribution System and Protection Coordination Using HILS System, 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), 2022, Island, Republic of Korea, pp. 344–346, ISSN: 2162-1241. [5] CHANDRAN R.L., ANJU PARVATHY V.S., ILANGO K., MANJULA G.N., Adaptive Over Current Relay Protection in a PV Penetrated Radial Distribution System With Fuzzy GA Optimisation, 2022 IEEE 19th India Council International Conference (INDICON), 2022, Kochi, India, pp. 1–7, ISBN: 978-1-6654-7350-7. [6] LIANG W., ZHAO Y., LIU B., WANG Y., Research on Distributed Photovoltaic Low Voltage Ride Through Control Strategy Considering Distribution Network Protection, 2023 3rd International Conference on Electrical Engineering and Mechatronics Technology (ICEEMT), 2023, Nanjing, China, pp. 550–555, ISBN: 979-8-3503-0369-8. [7] CHAVES T.R., IZUMIDA MARTINS M.A., VINICIUS João D., et al., Study for the Application of Self Healing in the Overhead Low Voltage Distribution Grid, 2022 IEEE International Conference on Power Systems Technology (POWERCON), 2022, Kuala Lumpur, Malaysia, pp. 1–5, ISBN: 978-1-6654-1775-4. [8] SAZANOV V.S., KOVALENKO A.I., VOLOSHIN A.A., et al., The Development of System for Automatic Adaptive Change of Relay Protection Settings in Distribution Networks, 2022 5th International Youth Scientific and Technical Conference on Relay Protection and Automation (RPA), 2022, Moscow, Russian Federation, pp. 1–15, ISBN: 979-8-3503-9991-2. [9] VSD Available online: , accessed April 2022. [10] STEFKO R., CONKA Z., KOLCUN M., Case Study of Power Plants in the Slovak Republic and Construction of Microgrid and Smart Grid. Appl. Sci. vol. 11, no. 11, 2021, pp. 1–22. [11] DIAHOVCHENKO I., YEVTUSHENKO I., KOLCUN M., et. al., Demand-Supply Balancing in Energy Systems with High Photovoltaic Penetration, using Flexibility of Nuclear Power Plants, 20 (2023), No. 11, Acta Polytechnica Hungarica, pp. 115–135, ISSN: 1785-8860. [12] KOLTSAKLIS N., KNÁPEK J., The Role of Flexibility Resources in the Energy Transition, 20 (2023), No. 11, Acta Polytechnica Hungarica, pp. 137–158, ISSN: 1785-8860. [13] MÁSLO K., KOUDELKA J., BÁTORA B., VYČÍTAL V., Asymmetrical, Three-phase Power System Model: Design and Application, 20 (2023), No. 11, Acta Polytechnica Hungarica, pp. 9–27, ISSN: 1785-8860. [14] Vojtek M., MASTNÝ P., MORAVEK J., et. al., Recent Challenges Regarding the Verification of Photovoltaic Inverters Properties and their Compliance with Technical Requirements, 20 (2023), No. 11, Acta Polytechnica Hungarica, pp. 63–82, ISSN 1785-8860.
Authors: Ing. Róbert Štefko, PhD., Technical University of Košice, Department of Electric Power Engineering, st. Mäsiarska 74, 040 01 Košice, E-mail: robert.stefko@tuke.sk; Dr. h.c. prof. Ing. Michal Kolcun, PhD., Technical University of Košice, Department of Electric Power Engineering, st. Mäsiarska 74, 040 01 Košice, E-mail: michal.kolcun@tuke.sk; Ing. Marek Bobček, Technical University of Košice, Department of Electric Power Engineering, st. Mäsiarska 74, 040 01 Košice, Email: marek.bobcek@tuke.sk; prof. Ing. Damian Mazur, PhD., Rzeszow University of Technology, Department of Electrical Engineering and Fundamentals of Computer Science, st. Powstańców Warszawy 12 35-959, Rzeszów, E-mail: mazur@prz.edu.pl. Ing. Bogdan Kwiatkowski, PhD., Rzeszow University of Technology, Department of Electrical Engineering and Fundamentals of Computer Science, st. Powstańców Warszawy 12 35-959, Rzeszów, E-mail: b.kwiatkowsk@prz.edu.pl.
Source & Publisher Item Identifier: PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 100 NR 9/2024. doi:10.15199/48.2024.09.53
Published by Dranetz Technologies, Inc. Website: Dranetz.com
Most high reliability facilities have a significant investment in UPS systems, generators and other mitigation devices in order to prevent electrical supply problems from impacting their business. However, these mitigation devices and related equipment are complex electro-mechanical systems that are themselves susceptible to failure and often do not provide any alarms or notifications when not functioning up to manufacturers specifications. That’s where continuous power monitoring comes in.
Power Quality monitoring systems continually evaluate the health of the electrical supply at key locations within a facility including the utility supply, generators, UPS input and outputs and other critical distribution points and load. These systems have been proven to prevent problems from occurring by proactively detecting anomalies in the electrical supply before they escalate into system failures. These systems are invaluable for troubleshooting failures should they occur as well as monitoring demand, energy and environmental parameters like temperature and humidity.
The ideal time to install any electrical equipment, including power monitoring equipment, is during the initial construction phase. An often overlooked benefit of these monitoring systems is they can be an extremely valuable asset that can be used during site commissioning. During this phase the entire facility is put through its paces with each element being thoroughly tested to validate the design, see if equipment is operating to manufactures/designers specifications and that it is compatible with the overall facility. Power monitoring systems can provide significant added value not only by recording and documenting the successful commissioning of a facility but also in identifying and resolving any system failures that occur at this critical phase.
A large worldwide cable television and media company recently constructed and commissioned a state-of-the art data center and production facility. During construction, Dranetz’s Encore Series System, a permanently installed power quality monitoring system was installed to monitor 25 key locations within the data center. Monitored locations include each utility feed, generators, inputs and outputs of each UPS and critical PDU’s downstream from the UPS. Encore Series was chosen for many reasons including state-of-the-art power quality capabilities, ease of use, web browser interface and cost vs. the competition.
Wanting to take full advantage of its benefits, Encore Series was an integral part of site commissioning with recorded data continually being evaluated and compared to expected results. Among many items, the commissioning procedures included tests to evaluate the source transfer from utility (one of two utility supplies) to generators then back to utility. Unlike other test performed, this transfer test failed multiple times with the facility remaining dark and the test uncompleted as failures were detected prior to completion. Between tests one line diagrams were reviewed and compared to the actual build out in order to verify proper equipment installation and construction in attempts to locate the source of the problem. This evaluation indicated several related breakers were either tripped or in the wrong position. The final test attempted proved much more serious with a complete failure of a utility breaker which exhibited visual damage and a smoke odor.
Being commissioned prior to start of site acceptance testing Encore Series data was reviewed for forensic evidence of this failure. The monitoring system design was such that critical locations important to this test were instrumented providing extremely valuable data which resulted in the quick diagnosis of this problem. Each generator bus (Generator 1, Generator 2) and utility feed (Utility 1, Utility 2) was monitored and waveshapes recorded at locations Generator 1 and Utility 2 at the time of the last test that resulted in the breaker failure.
A close inspection of the current waveforms recorded at both Generator 1 (3000A bus) and Utility 2 (4000A bus) locations provided a key indicator of the source of the problem. Current measurements at each location were many times higher than the rated bus capacity. In fact, current waveshapes on all phases were clipped indicating a saturation of current transformers (CT’s) as a result of currents well in excess of their specifications.
Fig.1. Generator 1 (3000A bus)
Fig.2. Utility 2 (4000A bus)
The data above quickly led the team to closely review the sequence of events leading up to, and resulting from the failure at these locations. As suspected, Generator 1 and Utility 2 buses were connected together as shown in the diagram below at the time of failure. Being out of phase the resultant current draw on the system caused the related breakers to trip and ultimately led to the failure of the Generator 1 breaker. Further investigation indicated a sequencing problem in the programmable logic controller (PLC) allowing the Generator 1 and Utility 2 breakers to be closed at the same time. A programming error in the PLC was determined to be the ultimate source of the problem. The programming error was corrected, the failed breaker repaired and the tests were then successfully performed.
Published by Nahid MUFIDZADA1, Gulgaz ISMAYILOVA2, Azerbaijan State Oil and Industry University ORCID: 1. 0000-0003-4063-2128, 2. 0000-0003-0063-2020
Abstract. Overvoltage is explored on the 35, 10 and 6 kV sides of the electrical network where various characters short circuit occur on its highvoltage part. It has been revealed that the overvoltage which occurs during a short circuit has the highest values if the short circuit is single-phase, as expected. However, disconnecting of all types of short circuits results in higher, overvoltage because the network operates with an isolated neutral and a short circuit. Even when a short circuit occurs on the high voltage (HV) side of the transformer, it does not completely de-energize it. Breakdown occurs at such high currents that they cause excessive voltages. Protection against such high overvoltage can be provided by installing surge arresters at the inputs of 35 kV transformers.
Streszczenie. Przepięcia badane są po stronach sieci elektrycznej 35, 10 i 6 kV, gdzie w części wysokonapięciowej występują zwarcia o różnym charakterze. Stwierdzono, że przepięcie powstające podczas zwarcia ma największe wartości, jeśli zgodnie z oczekiwaniami zwarcie jest jednofazowe. Jednak odłączenie wszelkiego rodzaju zwarć powoduje wyższe przepięcia, ponieważ sieć działa z izolowanym punktem neutralnym i występuje zwarcie. Nawet jeśli zwarcie wystąpi po stronie wysokiego napięcia (HV) transformatora, nie powoduje to całkowitego odłączenia go od zasilania. Awaria następuje przy tak dużych prądach, że powodują one nadmierne napięcia. Ochronę przed tak dużymi przepięciami można zapewnić instalując ograniczniki przepięć na wejściach transformatorów 35 kV. (Przepięcie na stronie górnej i dolnej sieci elektrycznej Napięcie 35 kV Przy powstawaniu i rozłączaniu zwarć różnego rodzaju w części wysokiego napięcia)
Keywords: Overvoltage, short circuits, surge suppressors, switches with shunt resistance. Słowa kluczowe: Przepięcia, zwarcia, zabezpieczenia przeciwprzepięciowe, wyłączniki z bocznikiem
Introduction
Electrical networks of 35 kV belong to distribution networks and operate with an isolated neutral. These networks are the most widespread and extensive, therefore more susceptible to abnormal and emergency conditions. The reliability of 6-35 kV networks determines the uninterrupted power supply to consumers. Emergency modes in these networks occur mainly during short circuits, which lead to an increase in either currents or voltages to high values, depending on the type of short circuit and the operating mode of their neutrals. Disabling a short circuit also leads to high overvoltage, in this case the magnetic energy of the cutting current is converted into electrical energy and increases the voltage. Consequently, the greater the breakdown current, the more overvoltage is created in the network. It is known that switches disconnect the short-circuit part of the network when the current in the switch passes through its zero value or close to this value. It should be noted here that when turning off and on, the switches of three phases operate simultaneously, while the currents of the three phases shifted relative to each other by 2π/3 degrees, do not simultaneously pass through their zero value, therefore, the switches of healthy phases operate at the moment when the currents in these phases have sufficiently large values, i.e. large currents are interrupted, and such current interruptions can cause large overvoltage as stated above [1-7].
With asymmetrical short circuits, the highest overvoltages are observed in healthy phases. On the damaged phase, overvoltage is also observed. These overvoltages that arise mainly depend on the instantaneous value and rate of change of the current in the switch at the moment of its break, on the instantaneous value of the voltages in the phases and the parameters of the circuit [3].
Problem setting
The question of what overvoltages can result from the interruption of large currents have quite great interest. This article is devoted to the consideration of this issue, as well as the transfer of such overvoltages to the secondary side of transformers, i.e. overvoltage on 10 kV and 6 kV bus systems when large currents break on the 35 kV side. The article examines a part of the electrical network in which there are three substations and two lines.
The first substation is a supply substation (SSub/S) with a voltage of 220/35 kV, the second (S/S-1) – 35/10 kV and the third (S/S -2) – 35/6 kV. To protect against overvoltages, surge arresters are installed in 35 kV bus systems. On the 10 kV and 6 kV sides there are corresponding loads S1, S2 – Fig. 1.
Fig.1. Diagram of the electrical network under study
Solution of problems
Various forms of short circuits – one-phase, two-phase, two-phase to ground and three-phase – were performed alternately in the gap between the transformer T1 and the switches in the substation S/S-1. The results obtained are shown in Table1, as well as in Fig. 2 – 4.
Fig.2. Overvoltages in bus systems of 35 kV substation S/S-1 when a single-phase short circuit occurs and switches off.
Fig.3. Overvoltage on the LV side of transformer T1 at occurrence and disconnection of a two-phase short circuit to ground
Fig.4. Voltage at the inputs of transformer T1 when and disconnecting three-phase short circuit.
Table 1 shows the amplitude values of the currents in the first and second lines, the currents on the primary side of transformers T1 and T2.
Table 1. Currents in lines 1 and 2, on the primary side of transformer T1 and capacitive currents of lines 1 and 2
.
In normal operation of the network, the currents in the first and second lines are equal to 400 A and 135 A, respectively, the current in the branch of transformer T1 is 265 A, and the capacitive currents of the primary and secondary lines are 0.8 A and 0.5 A, respectively. the phase voltage values in this mode are equal – at the beginning of the first line 29.32 kV, at the beginning of the second line – 27.35 kV and the end of the second line – 26.23 kV. A, the amplitude values of the phase voltages on the 10 kV and 6 kV sides are respectively equal to 7.48 kV and 4.37 kV (see Table). All these defined values correspond to their real values during normal operation of the network in question.
In the work, it was assumed that all of the above types of short circuits occurred at the moment the voltage of phase A at the short circuit point passed through its amplitude value.
With a single-phase short circuit, there is a slight increase in the phase A current (from a value of 266 A to a value of 369 A) in the branch of transformer T1 in the S/S-1, and in phases B and C there is practically no change in currents. Changes in the current values of lines 1 and 2 are small. The current passing into the ground from the short circuit point is 164 A. This current is closed through the line capacitances, which increase to 46 A.
A single-phase short circuit leads to an increase in voltage in healthy phases. As can be seen from table. 2, at the inputs of transformer T1 in phases B and C, the voltages increase by 2.4 times. Increase in voltage at the end of line 2, i.e. on the high voltage side (HV) of transformer T2 is slightly larger – 2.7 times, due to the superposition of high-frequency voltage fluctuations created on this line. Consideration of changes in voltages and currents at the beginning of the first line and at the end of the second line (in transformer T2) is aimed at determining the influence of a fault occurring in the S/S-1 substation on these values at these specified points, which are located several kilometers from the fault point.
Of interest is the transmission of such overvoltages to the 10 kV and 6 kV sides. On these sides, there is an increase in voltage in the damaged phase by more than 1.7 times, since the current of this phase in the high-voltage part has a slightly larger change. In healthy phases there is practically no increase in voltage.
With a two-phase short circuit to ground (in phases A and B), the currents in the branch of transformer T1 increase by 7 times. The voltage in the bus systems of substation S/S-1 (at the HV inputs of transformer T1) in damaged phases drops to zero, and in the healthy phase increases from a value of 27.32 kV to a value of 42.45 kV, i.e. 1.6 times. In the bus systems of the S/S-2 substation, in phase A the voltage practically does not change, in phase B it decreases by 1.5 times, and in phase C it increases by the same amount (according to the current values of these phases at the time of the short circuit). On the low side of transformer T1 in phases A and B, the voltages are reduced by half, since the short circuit is located in these phases on the high voltage side, therefore, in the high-voltage winding there are practically no currents in these phases, therefore, in the magnetic circuit, half of the magnetic flux of the current of phase C is closed through the rod of phase A, and the other half through the rod of phase B, which leads to a halving of the secondary voltage in these phases. In phase C, the secondary voltage does not change. There is no change in voltage on the low side of transformer T2 (see Table 1).
When a two-phase fault to ground is disconnected, the capacitive currents of the damaged phases of both lines increase greatly (up to 80 A). The current passing into the ground is 245 A (see Table 1).
In the HV bus systems of the S/S-1 substation, the voltage in all phases reaches quite high values, up to 91 kV, i.e. increase by 3.5 times and this is in the presence of surge arresters at this point – fig. 3.
The voltages in the damaged phases on the high side of transformer T1 remain equal to zero, and in the healthy phase they increase excessively (as in a single-phase short circuit), since the shutdown was performed at low values of the currents of the damaged phases and at this point in time the current of the healthy phase was quite large. And, also with an isolated neutral of the network, the high-voltage winding of transformer T1 is not completely de-energized during two phase short circuits. In the case under consideration, in the high-voltage windings of transformer T1, the currents of the damaged phases are almost 140 A, and the healthy phase is 280 A.
Consequently, the disruption of such large currents leads to excessively large overvoltages. Using two contact switches in this case also does not give a positive result. A decrease in overvoltage by 2–3 times is observed, but these values remain excessively high. Installing an arrester at the HV inputs of transformer T1 overcomes this problem. Moreover, these overvoltages do not exceed 99 kV, with a duration of several microseconds, as indicated in the calculations of a single-phase short circuit. In steady state, after a short circuit, the voltages in all three phases become zero [5].
Using two contact switches in this case does not give a positive result. In this case, overvoltages are reduced by 2– 3 times, but these values also remain excessively high. And, when installing an arrester at the HV inputs of transformer T1, these overvoltages are reduced to almost 99 kV, which exceeds their nominal value by 3.7 times, remaining acceptable for a voltage class of 35 kV. These overvoltages have a pulsed form with a duration of up to approximately 10 μs – Fig. 3. In steady state after disconnecting the short circuit, the voltages in all three phases become zero.
The secondary voltages of transformer T1 also increase when the two-phase short circuit is disconnected. In phases A and B, the voltages increase from 3.88 kV (at short circuit) to 9.48 kV, and in phase C from 7.85 to 18.96 kV. The curves of these overvoltages are shown in Fig. 4. In steady state, these voltages become zero.
Both in transformer T1 and in transformer T2, on the HV and LV sides, the voltages of phase A differ little from their nominal values, and the voltages of phases B and C exceed their nominal values by more than 3 times [6].
With a two-phase short circuit (also in phases A and B), the currents of the damaged phases in the branch of transformer T1 increase greatly (almost 11 times), and the change in the current of phase C is small (100 A). The short-circuit current is 2940 A. The voltage in the HV bus systems of the S/S-1 substation in the damaged phases drops from a value of 27.32 kV to a value of 15 kV and does not change in the healthy phase. The voltage changes in the HV bus systems of substation S/S-2 are the same. The voltage on the secondary side of transformers T1 and T2, in phases A and B, is reduced by half, and in phase C remains unchanged (as with a two-phase ground fault). But disconnecting such a short circuit greatly changes all the voltages in the circuit under consideration. Voltages in all phases of the 35 kV bus system of substation S/S-1 increase three times, and in the high voltage bus systems of substation S/S-2 such an increase in voltage occurs only in phases B and C. In phase A the voltage increase is small. Disabling a two-phase fault, as well as disconnecting a single-phase fault and a two-phase fault to ground, leads to excessively large overvoltage values on the primary and secondary sides of transformer T1. In the presence of surge arresters on the HV waters of transformer T1, these overvoltages are reduced on this side to 99 kV and on the LV side to 19 kV in phases A and B, and 38 kV in phase C. As can be seen, 38 kV is 5 times the nominal value of this voltage, which is quite high. On the secondary side of transformer T2, the voltages in phases B and C increase by approximately 3.5 times, and the voltage in phase A changes little. In steady state after a short circuit, the secondary voltages T1 are equal to zero, and T2 are equal to their nominal values.
Of course, a three-phase fault (and a three-phase fault to ground) does not create overvoltages, but disconnecting this type of fault leads to fairly high values of overvoltages, since the currents of a three-phase fault have the highest values compared to currents in other types of faults. In the case under consideration, the three-phase short circuit currents reach 3450 A, exceeding the rated currents by 13 times.
As can be seen from table 1, with a three-phase short circuit, in the HV bus systems of the S/S-1 substation, the voltages in all three phases drop to zero. In the HV bus systems of the S/S-2 substation, the voltage of phase A differs little from its nominal value, and the voltages of phases B and C are reduced by more than half their nominal values. This form of voltage change also occurs in the secondary winding of transformer T2. As for the secondary voltages of transformer T1, these voltages are zero, since on the high side of this transformer the voltages of all three phases are zero.
When a three-phase short circuit is disconnected, almost four times the rated voltage is set in the HV bus systems of substation S/S-1, and three times in the bus systems of substation S/S-2. The voltage at the short-circuited inputs of transformer T1 after disconnecting the short circuit increases from zero to 31.84 kV with a duration of approximately 0.1 s – Fig.4. The secondary voltages of transformer T1 are equal to zero, since the primary voltages of this transformer are equal to zero. And, the secondary voltages of transformer T2 increase significantly. The increase in phase A is approximately 2 times, in phase B – 4.3 times and in phase C 5 times. Note that with a three-phase fault to ground, due to the direct connection of the transformer T1 inputs to the ground, the voltage in them remains equal to zero when the fault is turned off [7].
Conclusions
1. Overvoltages occurring during a single-phase short circuit have higher values than overvoltages occurring during other types of short circuit. In the considered 35 kV network diagram, with a single-phase short circuit, the overvoltage factor reaches 2.4.
2. Disabling all types of asymmetrical short circuits leads to excessively high overvoltages, which have pulse forms with a very short duration.
3. When performing surge protection for 35 kV networks, one should also take into account the overvoltages that occur when disconnecting a short circuit, at which these overvoltages reach excessively high values. The use of two contact switches to interrupt faults reduces these overvoltages, but the reduced values also remain unacceptably high. Protection against such overvoltages can be achieved by installing surge arresters at the transformer inputs.
REFERENCES
[1] Florkowska B., Florkowski M., Zydroń P., Pomiary i analiza wyładowań niezupełnych w układach izolacyjnych wysokiego napięcia przy narażeniach eksploatacyjnych, Przegląd Elektrotechniczny, 2010, 4, 241-244. [2] Florkowski M., Forkowska B., Rybak A., Zydron P., Migration effects at conductor / XLPE interface subjected to partial discharges at different electrical stresses, IEEE Trans. on Diel. and Electr. Insul., 2015, 22, 456 – 462. [3] N.Mufidzade, G.Ismayilova, E.Huseynov. “Effect of line carnation on overvoltage in transformers of rated voltage 330 kV”. International Journal on “Technical and Physical Problems of Engineering” (IJTPE), Iss. 51, Vol. 14, № 2, Jun. 2022. [4] A. Shimada, M. Sugimoto, H. Kudoh, K. Tamura, and T. Seguchi, “Degradation distribution in insulation materials of cables by accelerated thermal and radiation ageing,” IEEE Transactions on Dielectrics and Electrical Insulation 20, pp. 2107, 2013. [5] A. Shimada, M. Sugimoto, H. Kudoh, K. Tamura, and T. Seguchi, “Degradation mechanisms of silicone rubber (SiR) by accelerated ageing for cables of nuclear power plant,” IEEE Transactions on Dielectrics and Electrical Insulation 21, pp.16, 2014.. [6] Kadomskaya K. P., Lavrov Yu. A., Reichertt A. Overvoltage in electrical networks for various purposes and protection against them. Novosibirsk, Publishing house of NSTU, 2004. [7] F.Kh. Khalilov. Overvoltage classification. Internal overvoltage. Edition, Energy Training Center, St. Petersburg, 2013. 2. AC switches for voltages above 1000 V. General technical conditions. Instead of GOST-687-70 and GOST 687-67. Gos.com. USSR by standards. – M., 1979, 98 p
Source & Publisher Item Identifier: PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 100 NR 9/2024. doi:10.15199/48.2024.09.41
Published by Dranetz Technologies, Inc., Technical Documents – Application Note
SUMMARY
Controlled environment agriculture (CEA) relies on advanced technologies such as climate control systems, artificial lighting, and hydroponic or aeroponic growing systems. These systems, in turn, require stable, clean power. This Application Note outlines how a New Jersey-based indoor farming facility addressed persistent HVAC and VFD failures by installing a Camille Bauer PQ5000 permanent power quality and energy meter along with Dranetz master monitoring station. The meter and station setup provided insight into utility feed conditions, helped diagnose internal power events, and offered actionable data to prevent crop loss.
Indoor Cultivation Requires Reliable Power
CEA facilities typically aren’t simple greenhouses. Rather, they’re high-density industrial spaces that replicate ideal outdoor conditions, down to temperature, humidity, airflow, and CO₂ levels. And they must do it 24/7. Such a facility includes:
● High-intensity lighting systems ● HVAC systems tailored for tight humidity and temperature control ● Dehumidifiers and reheat coils ● Irrigation pumps ● CO₂ emitters ● Fans for air movement ● Automated environmental monitoring and controls
The Electrical Load Behind the CEA Facility
Keeping a CEA facility running often requires electrical engineering expertise as much as horticultural smarts. Lighting, HVAC, and environmental control systems often run on dedicated circuits and variable frequency drives (VFDs) to maintain efficiency. Many facilities also operate with year-round HVAC cycles, meaning cooling and dehumidification don’t stop, even in winter.
Key electrical demands include:
● Continuous operation of HVACD (Heating, Ventilation, Air Conditioning, Dehumidification) systems ● High wattage lighting (e.g., LED or HID systems) ● CO₂ delivery and sensor systems ● Centralized control systems and security monitoring
As a result, CEA facilities can be major utility customers. Energy usage can reach 16,000 kWh per day for mid-size operations.
Common Power Quality Challenges
With this level of electrical demand, it’s clear that power quality can directly impact yield, product quality, and profit margins. Even minor disturbances in power quality can disrupt operations. And that’s exactly what started happening at the New Jersey facility.
Power quality risks in CEA operations can include:
● Harmonic distortion: Caused by LED drivers and VFDs, leading to overheating and potential equipment failure. ● Voltage sag or drop: Especially during load start-up events. ● Power interruptions: Even brief outages can stress environmental control systems, leading to inconsistent grow conditions or system lockouts. ● Grid impact from load density: In regions where multiple, large energy consuming sites operate within the same utility zone, the collective demand can stress local distribution systems, causing grid instability and the potential for increased power outages.
These issues aren’t theoretical. At the NJ facility, the HVAC system and VFDs began failing unexpectedly, threatening their high-value crops. Without clean, consistent power, environmental parameters swung outside of acceptable ranges, forcing growers to discard product.
NJ Facility Overview
Figure 1. NJ CEA facility outdoor switchgear
This New Jersey CEA facility spans 58,000 square feet and is fed by a 480V, 8000A service, split into two 4000A services. The electrical infrastructure includes:
● Two 480V pad-mount transformers for utility power ● Outdoor switchgear ● Roof-mounted HVAC units and VFDs ● Complex lighting schedules with ~12-hour cycles ● Estimated 16,000 kWh of daily consumption
The size and complexity of the load created real vulnerability to power quality issues, especially at the utility service entrance.
Installation of the Camille Bauer PQ5000
To understand the root cause of HVAC and VFD issues, the facility team installed a PQ5000 permanent power quality monitor on one of the two 4000A service feeds. The unit was mounted with:
● Voltage disconnects ● CT shunt assembly ●Integration with a Dranetz Master Station for graphical user interface and remote monitoring via web interface
This setup allows facility engineers to:
● Continuously monitor utility feed conditions ● Capture waveform and RMS data in real time ● Proactively monitoring and analyze disturbances without waiting for failure ● Profile energy use by load and time of day
In the first two weeks of operation, the system captured normal operations and RMS startup events, confirming that load initiations weren’t to blame. The facility now has a clear baseline and is prepared to identify deviations before they lead to downtime.
Figure 2. Installed Camille Bauer PQ5000 unit
Why the PQ5000 Works for CEA Facilities
Here’s why PQ5000 is a strong fit for controlled environment facilities:
Continuous Monitoring
Power doesn’t fail on a schedule. The Camille Bauer PQ5000, coupled with the Dranetz Master Station, provides 24/7 data on:
● Voltage sags/swells ● Harmonics ● Frequency variations ● Energy utilization
Figure 3. The Dranetz Master Monitoring Station
Grid & Load Side Visibility
This setup helps separate utility-side events from internal equipment issues, a key need in determining where disturbances originate for fast resolution.
Fast, Actionable Insight
The system offers waveform capture and automated analysis. Site engineers don’t need to sift through raw data to understand what happened.
Energy Profiling
CEA facilities often operate on tight margins. The PQ5000 enables:
● Demand, kWh and other tracking ● Load pattern analysis ● Lighting schedule verification
Scalable & Future-Ready
As the NJ CEA site expands, a second PQ5000 will be added to the remaining 4000A feed. The system’s modular design and web interface support multi-site deployment and long-term scalability.
Adopting a Proactive Monitoring Approach
Power quality problems can be silent yield killers in indoor cultivation facilities. Failures in HVAC, VFDs, and other systems can damage crops before anyone notices.
By installing the PQ5000 and master monitoring station, this NJ facility moved from reacting to equipment failures to proactively monitoring the health of its electrical infrastructure. They experienced:
● Greater confidence in environmental control ● Better decision-making using power profile data ● Reduced risk of batch loss due to unknown power events
Figure 4. NJ CEA facility’s waveform summary
Ready to Get Ahead of Power Problems?
If you’re designing or running a CEA facility for high value crops, you will want to be as proactive as possible about PQ issues, like these NJ growers are. The Camille Bauer and Dranetz permanent PQ monitoring systems give you the insight to manage uptime and protect crop quality, and improve energy efficiency with data you can trust.
Visit dranetz.com/product/pq5000 to learn more or contact our team for help tailoring a monitoring plan to your facility.
Dranetz and Camille Bauer are GMC Instruments brands for power quality and energy management. GMC Instruments is a global leader in electrical measurement and testing technology. GMC Instruments Americas is the GMC Instruments sales and support center for the Americas for all GMC Instruments brands.
Website: Dranetz.com , Call 1-800-372-6832 (US and Canada) or +1-732-287-3680 (International)
Published by Minaxi1, Sanju Saini2, Garima Tiwari3, Deenbandhu Chhotu Ram University of Science and Technology, Murthal ORCID: 1. 0000-0003-4172-725X; 2. 0000-0003-1390-4861; 3. 0000-0002-3004-0375
Abstract. Blackout restoration is crucial to energy security and infrastructure resilience. Black-start procedures must be used to restore a power grid methodically. Grid recovery requires selecting the correct unit black-start optimization methods. Each Dijkstra shortest path approach determines a unit’s optimum recovery route after a large power loss. A full indication includes unit capacity, climbing rate, beginning power, recovery time, and route recovery capacitance. An exhaustive index. This index facilitates unit startup. We end with a unit black-start strategy using the optimal recovery route, unit start sequence, and unit start limitations. This method works in the IEEE30 node system simulation. Research suggests the black-start method may boost unit recovery and success. Black-start strategy performance is assessed for two prominent graph-based algorithms, Dijkstra and A. Unit black-start analysis is assessed for Dijkstra and A algorithms. Priorities include start sequence and recovery path optimization. Grid recovery efficiency and efficacy depend on performance measures. Optimization, route length, and calculation time improve process dependability and efficiency. Dijkstra’s simple, reliable approach works well in certain situations. The heuristic A* algorithm works well in certain cases. Both strategies are used in this paper to improve system performance. Explaining the power system’s peculiarities comparatively allows for selecting an algorithm.
Streszczenie. Przywracanie po awarii ma kluczowe znaczenie dla bezpieczeństwa energetycznego i odporności infrastruktury. Procedury czarnego startu muszą być stosowane w celu metodycznego przywracania sieci energetycznej. Przywracanie sieci wymaga wybrania prawidłowych metod optymalizacji czarnego startu jednostki. Każde podejście Dijkstry do najkrótszej ścieżki określa optymalną trasę odzyskiwania jednostki po dużej utracie mocy. Pełne wskazanie obejmuje pojemność jednostki, szybkość wznoszenia, moc początkową, czas odzyskiwania i pojemność odzyskiwania trasy. Wyczerpujący indeks. Ten indeks ułatwia uruchamianie jednostki. Kończymy strategią czarnego startu jednostki, wykorzystując optymalną trasę odzyskiwania, sekwencję uruchamiania jednostki i ograniczenia uruchamiania jednostki. Ta metoda działa w symulacji systemu węzłów IEEE30. Badania sugerują, że metoda czarnego startu może zwiększyć odzyskiwanie i sukces jednostki. Wydajność strategii czarnego startu jest oceniana dla dwóch wybitnych algorytmów opartych na grafach, Dijkstry i A. Analiza czarnego startu jednostki jest oceniana dla algorytmów Dijkstry i A. Priorytety obejmują sekwencję uruchamiania i optymalizację ścieżki odzyskiwania. Efektywność i skuteczność odzyskiwania sieci zależą od miar wydajności. Optymalizacja, długość trasy i czas obliczeń poprawiają niezawodność i wydajność procesu. Proste, niezawodne podejście Dijkstry sprawdza się w pewnych sytuacjach. Heurystyczny algorytm A* sprawdza się w pewnych przypadkach. Obie strategie są używane w tym artykule w celu poprawy wydajności systemu. Wyjaśnienie osobliwości systemu energetycznego w sposób porównawczy pozwala na wybór algorytmu. (Hybrydowe podejście do poprawy odtwarzania sieci)
Recently, extreme weather disasters, malfunctioning power equipment, and human mistakes have caused largescale blackouts in domestic and worldwide power networks [1], [2]. Some examples include the 2019 Argentina “6.16” blackout, which impacted the whole nation [3], the 2021 Texas “2.15” power outage, and the 2022 Taiwan “33” island-wide blackout, which caused considerable economic losses. The guarded grid must be prioritized and restored to safeguard key municipal infrastructure from catastrophic disasters and external damages. The restoration control method is complicated and time-consuming. Developing a logical unit recovery route search technique may boost risk resilience and grid recovery time, which has major research and engineering consequences. Unit-optimal recovery route management alone cannot speed up power loss recovery.
Restoring power after a blackout uses black-start power. These generators are called “self-starting generators” because they can start themselves and restore power without external power [4]. The method of “unit start-up” in power generating involves black-starting producing units that cannot start themselves after a large power loss [5]. This method allows units to be reactivated and power-generating again, enabling load recovery and network reinstatement. A unit start-up approach includes both the recovery route and the unit start-up procedure; therefore, the two options are usually interrelated [6].
Power grid management and restoration need the blackstart technique to handle a difficult energy infrastructure situation: a complete blackout or loss of electricity throughout an electrical system. After such an event, power restoration is urgent and complicated. Restoring power generation and energy delivery to end-users, industrial sectors, and vital infrastructure is the biggest challenge [7]. Electrical systems need black-start strategies to provide continuous power delivery even under challenging conditions. This is because these groups reduce the immediate effects of a power loss and maintain social stability. Actively studying and optimizing these strategies helps solve dynamic power grid issues. Current power systems are reliable, and several methods have been developed to keep them safe [8], [9]. Large traditional synchronous generating units have been replaced by smaller distributed generation (DG) units in power systems. Distributed generating units powered by intermittent renewable resources affect several system activities, including dispatch and commitment. The power system’s high renewable energy content, along with unexpected weather occurrences and human error, increases the risk of blackouts. A series of linked failures might cause major power outages [10], [11].
Power restoration after a blackout requires black-start power. Production of electricity units may start generating electricity on their own to repair the network without external power. This helps when the entire system blacks out [4]. This application defines “unit start-up” as power-generating units that cannot start independently after an extensive loss. A black-start power supply does this. This assistance helps them produce electricity again, establishing the groundwork for network restoration and electrical load recovery [12]. The sequence of starting a unit and its recovery path must be carefully considered when creating a start-up strategy. These options are interrelated inside the approach [13]. The power system restoration decision-making process has traditionally included milestone stages. One research [14] explored a unit start-up technique to reduce restoration time at each phase. The second research [15] employed sequencing and traversal approaches to determine unit startup order. This restored more non-black-start units quickly. A later study [16] examined the device’s capacity recovery. According to [17], unit start-up includes recovery route charging time in the first phase. In [18], the elements that affect the unit’s black-start recovery are covered in detail. Calculating the unit’s recovery path using the shortest route technique and reference [17]. Distant operation coverage factor, line operation length, and recovery probability are studied [19]. After developing the function that forecasts line commissioning time, the unit start-up priority index determines the start-up order. Dijkstra’s method optimizes unit recovery route introduction. According to the literature [20], start sequence selection is multi-conceptualized. A data envelopment analytic technique using a backtracking algorithm solves the later unit selection issue. Goals include reducing unit recovery time and improving recovery success. Black-start procedures are crucial for power grid management to restore electricity after a complete blackout or system failure. They are crucial to electrical system reliability and continuity.
Domestic and international professionals optimize generator-starting procedures. Unit start-up and milestone parts of a power system restoration process of selection were created to shorten restoration times at all levels [21]. The literature [15] used traversal and sequencing algorithms to find the unit start-up sequence that restored the most non-black-start units fastest. The literature [22] was also concerned with optimizing system-generating capacity within a certain timeframe. Recovery route charging time is considered in the unit’s start-up function [23]. Unit restoration accuracy during black-start and recovery routes are examined using K shortest path analysis [18]. The literature addresses line operation time, remote operation coverage, and line recovery [24]. Create a unit start-up preference index and line commencement time expectation function to determine start-up order. While waiting, Dijkstra’s algorithm optimizes the unit as the starter’s recovery path. The literature [25] states that a multi-constrained backpack problem is resolved using data envelopment analysis and a backtracking method to identify the next unit to commence.
This study aims to shorten the unit’s recovery time and speed up its recovery. Prioritizing a black-start technique that considers the unit’s recovery trajectory and reactivation process will achieve the goal. Mathematical models for black-start power units are established in this article. These models have source and non-black-start power. It then finds the best way to start units after a major power loss using Dijkstra’s and A*’s shortest route algorithms. Also included are unit capacity, climbing rate, and beginning power. A complete black-start plan considers the unit start sequence, optimal recovery path, and unit start constraints. Simulation verifies this method’s efficacy. The black-start unit was enabled initially while constructing the power system restoration method. For grid restoration, this device supplies initial electricity. After each target generator activation, a recovery plan is determined. It lists the black-start units that will recharge each non-black-start unit. This research will provide a unique contribution to power grid resilience and black-start strategy design. Assuring grid recovery reliability and efficiency via algorithmic selection is the goal.
The building of mathematical models for non-black-start power sources and black-start power units, including gas turbines, starts this study. In the second portion, Dijkstra’s shortest path approach is used to find the best recovery route for units that must be started after a large power loss. It then mixes the recovery route with the unit’s capabilities, climb rate, beginning power, and other specifications. By considering unit initiation limits, a thorough black-start approach is created. This method depends on initial unit sequence and restoration efficiency. This approach is proven via simulation. Electricity is originally supplied by the black-start unit to restart the grid. Next, black-start units charge each non-black-start unit to gradually start the target generators. So-called Power System Restoration Planning.
2. Modeling used for present work
A 30-bus IEEE test setup with Every source in [26] collects system data. This data includes generator, load, shunt capacitor, and transmission line cost and emission coefficients. To accommodate non-smooth fuel cost functions, ramp rate coefficients have somewhat adjusted IEEE-30 bus system cost coefficients. At 100 MVA, the data is expressed.
2.1 Unit Recovery Path Search Using Dijkstra
A popular method for determining the shortest route in weighted networks is Dijkstra’s algorithm. Already at the origin, this technique finds the shortest path by spreading outward till the final vertex. This method relies on breadth-first search [27]. Once the grid is separated as a topology diagram G = (V, E), where V represents the graph’s vertices and E represents its branches, its loads, generators, lines, and transformers are removed as undifferentiated nodes. Weighing the branches according to Equation (1) takes into account the line’s charging time, transformer operation time, and capacitance value to construct the weighted topology graph.
.
From nodes i to j, wij represents the branch weight. Line charging and transformer recovery periods are included in the normalized branch recovery time, tij. Normalized capacitance value cij represents the branch’s recovery success rate between nodes i and j, and equation (2) shows how the adjacency matrix A outlines the grid topology diagram’s connection link.
.
Node 1 is the black-start power node. Nodes 2–6 identify the unit to be started. Set VS = [1] contains the nodes that found the shortest route in the initial state. Vo = {2,3,4,5,6}, which includes all remaining nodes. Furthermore, D = [0,1,2,3, ∞, ∞] represents the appropriate distances between each node.
.
First, develop the adjacency matrix according to Equation (4). Node 2 is in set VS because set D shows it as the closest point to node 1. VS has [1,2], Vo has [3,4,5,6], and node 2’s shortest route is recorded.
Between the first and second nodes is 1 unit. According to the second row of the matrix of adjacency, node 2 is the intermediate node and seven distance separates nodes one to six. The initial specified D distance is greater than this figure. The distance set D now includes 0, 1, 2, 3, infinite, and 7. Set D finds node 3 closest to node 1, ignoring node 2. The quickest path to node 3 is documented by updating the VS and Vo lists to [1,2,3], [4,5,6]. The middleman is node 3. To alter D = [0,1,2,3,7,6], we add 7 and 6 from node 1 to nodes 5 and 6. Two units separate nodes 1–3. After refreshing VS = [1,2,3,4] and Vo = [5,6], node 4’s shortest route is collected. The revised D set places node 4 in Vo 3 distance from node 1. Nodes 1 and 5 are 7 distances apart because node 4 is the intermediate. The Vo node nearest to node 1 is node 6, 6 units away. We observe node 5’s shortest path and alter VS = [1,2,3,4,6] and Vo = [5]. As node 1 and node 6 are not directly connected, the quickest path from node 5 is quickly revealed. D comprises the shortest distances: [0, 1, 2, 3, 7, 6]. The process flow of pathfinding. Additionally, S is the shortest route matrix.
2.1.1. Integrated Index of Unit Start-Up Sequence
a) Index of Unit Characteristics
When selecting units to recover in order, use these criteria [28]:
1) Hot-start units are restored first to optimize hot-starting. (2) When there is insufficient power to create electricity during system recovery, units with low starting powers are prioritized for a smooth start. (3) For fast system recovery, units with faster-increasing rates are restored first. (4) High-recovery units are prioritized to ensure power production capacity.
A unit characteristic index is calculated using unit capacity, climbing rate, and initial power in Equation (5).
.
The characteristic index of the kth unit is “O(k)”. “C(k)” is the normalized climb rate, “S(k)” is capacity, and “P(k)” is its beginning power. Normalization is shown in Equation (6).
.
Where, xmin, xmax, x* stand for the minimum, maximum, and normalized values of x, y, and x* respectively. This model has the following variables defined: the amount of time the device takes to start up, in seconds; tc; the moment it launches its power delivery and links to the grid; tmax, the duration of time it delivers its highest level of active power externally; K, the pace at which the unit is rising; Pst, the unit’s active power while using the plant as its power source; KN, the unit’s average climbing rate; Furthermore Pmax, the unit’s maximum active power.
.
The kth unit’s composite index is denoted by Z(k), its characteristic index is denoted by O(k), and its distance index is represented by D(k).
b) Restrictions on Unit Startup
The time constraint of a start-up:
.
The variable “tmax” represents the upper limit for the hot-start time of the unit, whereas “ts” provides the starting time of the non-black-start unit. It represents the maximum time the unit may be hot-started before failing. The unit must meet Equation (9) (minimum cold-start time) if its hot-start time exceeds it.
.
The variable “ts ” denotes the initial time of the non-black-start unit, while “tmin“ reflects the minimum time required for the non-black-start unit to reach its operating temperature.
c) Start-up power constraint
The following equation can be written as p is the system’s black-start unit count, The system’s restored non-black-start unit count is denoted by q,
.
the active power output from the ith black-start unit at time t is represented by Pi(t), the active power output from the jth non-black-start unit at time t is represented by Pj(t), and the start power needed to start the next unit is called Pst.
d) Constraints on the start/stop condition of the unit It is expected that once the unit has been started, it will continue to function without any further shutdowns. Therefore,
.
The variable Sk(t) represents the status of unit k at time t, where a value of 1 indicates that the unit has started and a value of 0 indicates the opposite.
e) Power constraints
.
where PGi denotes the generator set’s maximum active power output and Pmin denotes the lowest permitted active power output.
Gi denotes the generator set’s active power; PGi denotes the maximum permissible output of active power; QGi denotes the generator set’s reactive power; Qmin denotes the lowest permissible output of reactive power; Qmax denotes the maximum permissible output of reactive power; Li denotes the active power transmitted by the ith line; and Pmax denotes the maximum permissible power available.
f) Voltage constraints
The values of Umin, Umax, and Ui represent the lower and upper voltage limits, respectively, of the ith node, Ui being its magnitude of the voltage value.
.
2.1.2. Unit Start-Up Process The unit starts policy development.
Here’s how to start the unit. Analyze the grid’s topology and attributes. Transformers, lines, generators, and loads are branches and nodes. Weight branches by line capacitance, transformer working time, and line charging time. Weighted topology diagrams with adjacency matrix A outcome from this idea.
Dijkstra’s method is used to each non-black-start unit’s distance index to discover the unit’s recovery route’s shortest path. Look at the sequence of the units’ starts to determine which should be launched next, disregarding those with a black start. Examine power, voltage, hot-start time, and start power limits. Unqualified units are either placed at the start of the beginning sequence or through the cold start process until they fulfill the standards. Update the system’s recovery status while getting the fastest-starting units. A* (A star) is a popular algorithm used for pathfinding and optimization in various domains, including analyzing recovery paths and start sequences [29], [30]. A* is an informed search algorithm that combines the benefits of Dijkstra’s algorithm and heuristics to efficiently find the shortest path while exploring the graph [31], [32]. Here’s how A* can be applied to analyze recovery paths and start sequences:
2.1.3. Recovery Paths in a Network
In network management and fault recovery, A* can be used to find the most efficient recovery paths for restoring network connections after a failure. This involves finding a path that minimizes a specific cost while considering the network topology.
• Source Node: The point of network failure. • Destination Node: The destination for rerouted traffic.
In both scenarios, A* is employed to efficiently find optimal paths or sequences by using heuristics to guide the search process. The choice of an appropriate heuristic can significantly impact the algorithm’s performance and accuracy in finding the optimal solution.
The aforementioned advantages make A* a powerful instrument for addressing a diverse array of issues that include the identification of the optimal route or solution inside a network or graph. The versatility, effectiveness, and assurance of optimality that it offers make it a preferred option for several applications.
2.2. Difference between Dijkstra’s and A* Algorithms
Dijkstra’s algorithm and A* (A-star) algorithm are two popular graph search algorithms used for finding the shortest path between two nodes in a graph. Here’s a tabular comparison of the main differences between these two algorithms:
Fig.1. Hybrid Dijkstra and A* Algorithm
Fig. 1 Hybrid Dijkstra and A* Algorithm 2.2.1. Unit Recovery Path Search Using Hybrid Dijkstra and A* The combination of Dijkstra’s algorithm and A* (A-star) algorithm for unit recovery path search is often referred to as the Hybrid Dijkstra-A* algorithm. Both Dijkstra’s and A* algorithms are popular pathfinding algorithms used in computer science and robotics for finding the shortest path between two points in a graph or grid. The hybrid nature of this algorithm comes into play by combining the results of Dijkstra’s and A. Instead of using A throughout the entire search, you can leverage the information obtained from Dijkstra to guide the search. During the A* search, if the algorithm encounters a node that has already been visited by Dijkstra’s and if the current path to that node is shorter than the path found by Dijkstra’s, you can update the information for that node using the shorter path. Fig 1 shows the Hybrid approach of restoring path. This way, the algorithm benefits from the efficiency of A* while incorporating the additional information provided by Dijkstra to improve the accuracy of the pathfinding.
3. Result Discussion
The result discussion serves as a A* (A-star) and Dijkstra’s algorithm are used in the hybrid method to route recovery in a system graph to maximize the shortest path search. Dijkstra’s method ensures that the shortest route is found by investigating every option, and A* employs heuristics to effectively direct the search. With g(n) standing for the cost from the start node, h(n) for the heuristic estimate to the objective, and f(n)=g(n)+h(n) for the total cost, the hybrid method preserves node information. The method updates costs and investigates neighbors while iteratively choosing nodes from a priority queue with the lowest f(n). This hybrid method effectively solves the route recovery problem in a system graph by balancing the effectiveness of A* with the dependability of Dijkstra’s.
3.1. Capacities and ON time of Each Node
Incorporating capacity values into the hybrid algorithm ensures that the path recovery process aligns with real-world resource constraints. The combination of Dijkstra’s and A* with capacity awareness leads to more efficient and practical solutions in complex network scenarios. Incorporating on-time values into the hybrid algorithm ensures that the path recovery process aligns with temporal dynamics. The combination of Dijkstra’s and A* with on-time awareness leads to more informed and adaptive pathfinding solutions in dynamic environments.
3.2. Comparison Graph
The comparison graph shows the different structures graphs and nodes explored in all three algorithms shown in figure 2 and table 1.
Table 1. Comparison of Algo with Nodes and Distances
.
Table 2. Comparison of Algo with Time
.
Fig.2. Comparison of Algo with Graphical wise
The first section compares the length of paths found by the hybrid algorithm, A, and potentially Dijkstra’s if included in the comparison. The second section compares the number of nodes explored during the pathfinding process by the hybrid algorithm, A, and Dijkstra’s. The table 2 and the time and nodes performances for each algorithm across different scenarios.
In each scenario, the hybrid algorithm provides better results, striking a balance between optimality (Dijkstra) and efficiency (A).
4. Conclusion and Future Scope
In conclusion, Dijkstra and A algorithms in black-start techniques and recovery route optimization provide grid resilience in a complex but realistic way. We improve power grid restoration efficiency, adaptability, and reliability by combining both methods, reducing downtime and strengthening the electrical infrastructure. The inclusion of cutting-edge algorithms will help our grids withstand unexpected obstacles as power system management advances and some future directions are:
1. Real-Time Data Integration 2. Quantum Computing Applications 3. Integrate cybersecurity measures into resilience plans 4. Optimize algorithms 5. Enhance Smart Grid Synergies 6. Incorporate adaptive control mechanisms into algorithms to modify performance and adaptability. 7. Community-Based Resilience Methods 8. Quantifiable Resilience measures 9. Inter-sector Integration
Future grid resilience directions strive to provide flexibility, efficiency, and security in the face of changing problems and technology.
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Source & Publisher Item Identifier: PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 100 NR 9/2024. doi:10.15199/48.2024.09.13
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