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![Figure 7. Operational characteristics of a voltage surge relay [58].](profile/Ahmed-Arefin-2/publication/363658537/figure/fig5/AS:11431281085030694@1663592352434/Operational-characteristics-of-a-voltage-surge-relay-58_Q320.jpg)
The application of the Phasor Measurement Unit (PMU) in the power system is expanding day by day since it provides a higher reliability through fast symmetrically monitoring and protection and assists in controlling power systems. For power systems, islanding is a significant event due to its hazardous consequences. To detect islanding events, seve...
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... data can be used for monitoring and developing protection algorithms to make the power grid more reliable. Figure 1 presents the principal components of PMUs [13][14][15]. The general structure of PMUs comprises a synchronization unit along with a measurement and wave transmission unit [16]. ...
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... this case, the breaker opens at 3 s and the microgrid frequency shows a substantial drift. The threshold was set at 60.5 Hz, which exceeds 4.7 s. Figure 10. PMU-based islanding detection using frequency data [57]. ...
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... islanding detection using frequency data [57]. Figure 11 shows a frequency plot of experimental data, which are recorded from six different sites in the UK's power system [34]. To create an islanding event, in this case, an inter-connected trip happened at 02:48:37 and generation loss started and lasted for 5 h. ...
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... Figure 9, frequency difference is considered, instead of system frequency (50/60 Hz). On the other hand, Figures 11 and 12 show different frequencies (50 Hz and 60 Hz). However, the frequency plots are different between them since they do not have the same electrical system. ...
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... the frequency plots are different between them since they do not have the same electrical system. Figure 12 shows a phase angle difference plot where an inter-connected trip happened at 2:48:37, and it drifts closely for 5 h. The main drawback that makes the scheme less reliable is that it uses probabilistic component analysis (PCA) on the phase angle data and assumes a normal distribution, but the phase angle difference component typically shows a non-Gaussian and non-linear behavior. ...
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... main drawback that makes the scheme less reliable is that it uses probabilistic component analysis (PCA) on the phase angle data and assumes a normal distribution, but the phase angle difference component typically shows a non-Gaussian and non-linear behavior. Figure 13 shows an islanding detection algorithm for a six-bus power system where voltage and current phasors were considered. In this case, the islanding event occurs at 0.02 s; afterwards, the voltage phasor angles change gradually. ...
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... I14, I21, I23, I25, I54, I56 presents the different buses current angle. Figure 14 shows the island detection technique using the phasor measurement unit bus voltage angle of the actual Utility Kerteh, Malaysia system [17]. In this work, the PMU was installed at the utility bus (PAKA) voltage angle and the DG bus (GTG-G-1) to receive the bus voltage angle. ...
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... this work, the PMU was installed at the utility bus (PAKA) voltage angle and the DG bus (GTG-G-1) to receive the bus voltage angle. In addition, the authors considered two different island conditions, namely over frequency (Figure 14a) and under frequency (Figure 14b), where over frequency happens when the DG capacity is higher than the connected feeder load and under frequency happens when the DG capacity is lower than the connected feeder load. For islanding detection, one of the most important criteria is the transient behavior of the voltage and current phasor since the detection algorithm is based on this behavior. ...
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... this work, the PMU was installed at the utility bus (PAKA) voltage angle and the DG bus (GTG-G-1) to receive the bus voltage angle. In addition, the authors considered two different island conditions, namely over frequency (Figure 14a) and under frequency (Figure 14b), where over frequency happens when the DG capacity is higher than the connected feeder load and under frequency happens when the DG capacity is lower than the connected feeder load. For islanding detection, one of the most important criteria is the transient behavior of the voltage and current phasor since the detection algorithm is based on this behavior. ...
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... data can be used for monitoring and developing protection algorithms to make the power grid more reliable. Figure 1 presents the principal components of PMUs [13][14][15]. The general structure of PMUs comprises a synchronization unit along with a measurement and wave transmission unit [16]. ...
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... this case, the breaker opens at 3 s and the microgrid frequency shows a substantial drift. The threshold was set at 60.5 Hz, which exceeds 4.7 s. Figure 10. PMU-based islanding detection using frequency data [57]. ...
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... islanding detection using frequency data [57]. Figure 11 shows a frequency plot of experimental data, which are recorded from six different sites in the UK's power system [34]. To create an islanding event, in this case, an inter-connected trip happened at 02:48:37 and generation loss started and lasted for 5 h. ...
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... corresponding event happened in the evening, at 18:17:15 of the same day, which lasted for 1 h and 22 min. From Figures 10-12, we can observe that there are different frequency plots, which are different from each other, since they considered different forms of frequency. In Figure 9, frequency difference is considered, instead of system frequency (50/60 Hz). ...
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... Figure 9, frequency difference is considered, instead of system frequency (50/60 Hz). On the other hand, Figures 11 and 12 show different frequencies (50 Hz and 60 Hz). However, the frequency plots are different between them since they do not have the same electrical system. ...
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... the frequency plots are different between them since they do not have the same electrical system. Figure 12 shows a phase angle difference plot where an inter-connected trip happened at 2:48:37, and it drifts closely for 5 h. The main drawback that makes the scheme less reliable is that it uses probabilistic component analysis (PCA) on the phase angle data and assumes a normal distribution, but the phase angle difference component typically shows a non-Gaussian and non-linear behavior. ...
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... main drawback that makes the scheme less reliable is that it uses probabilistic component analysis (PCA) on the phase angle data and assumes a normal distribution, but the phase angle difference component typically shows a non-Gaussian and non-linear behavior. Figure 13 shows an islanding detection algorithm for a six-bus power system where voltage and current phasors were considered. In this case, the islanding event occurs at 0.02 s; afterwards, the voltage phasor angles change gradually. ...
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... I14, I21, I23, I25, I54, I56 presents the different buses current angle. Figure 14 shows the island detection technique using the phasor measurement unit bus voltage angle of the actual Utility Kerteh, Malaysia system [17]. In this work, the PMU was installed at the utility bus (PAKA) voltage angle and the DG bus (GTG-G-1) to receive the bus voltage angle. ...
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... this work, the PMU was installed at the utility bus (PAKA) voltage angle and the DG bus (GTG-G-1) to receive the bus voltage angle. In addition, the authors considered two different island conditions, namely over frequency (Figure 14a) and under frequency (Figure 14b), where over frequency happens when the DG capacity is higher than the connected feeder load and under frequency happens when the DG capacity is lower than the connected feeder load. For islanding detection, one of the most important criteria is the transient behavior of the voltage and current phasor since the detection algorithm is based on this behavior. ...
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... this work, the PMU was installed at the utility bus (PAKA) voltage angle and the DG bus (GTG-G-1) to receive the bus voltage angle. In addition, the authors considered two different island conditions, namely over frequency (Figure 14a) and under frequency (Figure 14b), where over frequency happens when the DG capacity is higher than the connected feeder load and under frequency happens when the DG capacity is lower than the connected feeder load. For islanding detection, one of the most important criteria is the transient behavior of the voltage and current phasor since the detection algorithm is based on this behavior. ...
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... The results of simulations were performed on the IEEE 34-node feeder and demonstrate that the anomaly detection algorithm performed better to detect various classes of anomalies such as single line to ground faults. Arefin et al. (2022) focuses on detecting topological errors and islandings within the power network by utilizing PMU data. The researchers specifically utilized frequency and phase angle data coupled with time series anomaly detection techniques to identify and detect the islanding events. ...
Energy systems require radical changes due to the conflicting needs of combating climate change and meeting rising energy demands. These revolutionary decentralization, decarbonization, and digitalization techniques have ushered in a new global energy paradigm. Waves of disruption have been felt across the electricity industry as the digitalization journey in this sector has converged with advances in artificial intelligence (AI). However, there are risks involved. As AI becomes more established, new security threats have emerged. Among the most important is the cyber-physical protection of critical infrastructure, such as the power grid. This article focuses on dueling AI algorithms designed to investigate the trustworthiness of power systems’ cyber-physical security under various scenarios using the phasor measurement units (PMU) use case. Particularly in PMU operations, the focus is on areas that manage sensitive data vital to power system operators’ activities. The initial stage deals with anomaly detection applied to energy systems and PMUs, while the subsequent stage examines adversarial attacks targeting AI models. At this stage, evaluations of the Madry attack, basic iterative method (BIM), momentum iterative method (MIM), and projected gradient descend (PGD) are carried out, which are all powerful adversarial techniques that may compromise anomaly detection methods. The final stage addresses mitigation methods for AI-based cyberattacks. All these three stages represent various uses of AI and constitute the dueling AI algorithm convention that is conceptualised and demonstrated in this work. According to the findings of this study, it is essential to investigate the trade-off between the accuracy of AI-based anomaly detection models and their digital immutability against potential cyberphysical attacks in terms of trustworthiness for the critical infrastructure under consideration.
... The challenges that arise are the two-way flow of power and energy, faster frequency variations, reduced network inertia, large power fluctuations that cause additional dynamics [1][2][3]. All this and more presents great difficulties in protecting and managing the system [4][5][6]. Moreover, an undeniable increase in the adoption of microgrids will also introduce new problems in ensuring the main goal, which is to maintain power system stability [7][8][9]. ...
... Depending on test selector position in Figure 8, either Equation (4) or Equation (5) is added to the nominal grid angular position 2π f 0 nT s . ...
In this paper, a customizable low-cost voltage waveform generator based on a real-time desktop PC and embedded data acquisition card synchronized with Coordinated Universal Time (UTC) is presented. A software approach to phase-locked loop synchronization with an external Global Positioning System (GPS) pulse signal is utilized to achieve a time uncertainty of ±1μs. This avoids expensive hardware modules for synchronization and timing purposes, which are commonly presented in literature. Besides the application for controlling the test platform, our own phasor data concentrator (PDC) application is running concurrently on the host PC. The latter is used for collecting and comparing the syncrophasor data from the test platform against the syncrophasor data measured by phasor measurement units (PMUs) under the test. The paper describes all procedures for generating reference test signals. Numerous case studies were performed, and experimental results for steady-state compliance as well as frequency ramp and phase modulation tests for dynamic compliance are presented in detail. All tests confirm that customizable test platform meets the requirements of IEEE/IEC standards. Compared to other calibrators, the cost as well as the specifications and point-by-point concept of data processing makes the described test platform suitable for performance analysis of PMU algorithms implemented on various development boards.
... It is worth observing that the phasor signal model assumes that the signal energy is stationary within the considered observation interval and that the signal energy is mostly concentrated in a narrow bandwidth around the fundamental frequency. When these assumptions are not met (e.g., during an instantaneous step change of amplitude or phase), the PMU estimates suffer from definitional uncertainty due to the model inconsistency between the spectral properties of the signal under test and its phasor representation [31]. ...
The ever-increasing penetration of renewable energy sources and distributed generation needs developing and deploying more sophisticated and precise control techniques. The paradigm shift from high-rotational inertia towards inverter-connected facilities has made modern power systems subject to strongly non-stationary operating conditions. In this context, phasor measurement units (PMUs) represent a promising solution due to their accuracy and time synchronization. The current paper further develops the concepts recently published proposing performance assessment of PMUs relying on metrics specifically defined to quantify the estimation accuracy, reporting latency, and response time. Our focus is on determining the confidence interval associated with these metrics and thus derive a robust approach for their application in measurement-based network controlling efforts. The configuration of the used simulation model is detailed, and the state estimation results as a function of the selected measurement weighing criteria are presented, concluding as a possible application of the reliability metrics in Weighted Least Squares and Discrete Kalman Filter state estimators, highlighting the performance enhancement as well as the theoretical limits of the proposed approach.
... Energies 2023, 16, 4054 2 of 34 comparison due to the ability to provide the phasor signals in microseconds with accuracy and a faster way through the GPS [4][5][6]. The application of the PMUs in the system network facilitates tracking grid dynamics in real-time, obtaining data for wide-area monitoring, assessing the state estimation, predicting system failure, helping to plan energy delivery proactively, and providing improved protection and control [7][8][9]. ...
... associated operational uncertainties. WAMS-based flexible protection schemes can be designed to handle the uncertainties related to distance protection and acquire the control parameters of FACTS devices in a brief period [4]. • Optimal PMU placement: Without a comprehensive plan for wide-area protection, there is a lack of optimal PMU placement that can provide complete observability of the power system. ...
The emerging smart-grid and microgrid concept implementation into the conventional power system brings complexity due to the incorporation of various renewable energy sources and non-linear inverter-based devices. The occurrence of frequent power outages may have a significant negative impact on a nation’s economic, societal, and fiscal standing. As a result, it is essential to employ sophisticated monitoring and measuring technology. Implementing phasor measurement units (PMUs) in modern power systems brings about substantial improvement and beneficial solutions, mainly to protection issues and challenges. PMU-assisted state estimation, phase angle monitoring, power oscillation monitoring, voltage stability monitoring, fault detection, and cyberattack identification are a few prominent applications. Although substantial research has been carried out on the aspects of PMU applications to power system protection, it can be evolved from its current infancy stage and become an open domain of research to achieve further improvements and novel approaches. The three principal objectives are emphasized in this review. The first objective is to present all the methods on the synchro-phasor-based PMU application to estimate the power system states and dynamic phenomena in frequent time intervals to observe centrally, which helps to make appropriate decisions for better protection. The second is to discuss and analyze the post-disturbance scenarios adopted through better protection schemes based on accurate and synchronized measurements through GPS synchronization. Thirdly, this review summarizes current research on PMU applications for power system protection, showcasing innovative breakthroughs, addressing existing challenges, and highlighting areas for future research to enhance system resilience against catastrophic events.
... Signal-process computational-intelligence-based approaches, which are capable of handling com nonlinear system issues, have been employed to address the islanding detection c [45]. Few islanding detection methodologies make use of sophisticated mathema telligent, and signal-processing methods as effective identification tools availab literature [46][47][48]. ...
... Signal-processing and computational-intelligencebased approaches, which are capable of handling complicated nonlinear system issues, have been employed to address the islanding detection challenge [45]. Few islanding detection methodologies make use of sophisticated mathematical, intelligent, and signal-processing methods as effective identification tools available in the literature [46][47][48]. ...
Active distributed generations (ADGs) are more prevalent near consumer premises. However, the ADG penetration contribute a lot of dynamic changes in power distribution networks which cause different protection and control issues. Islanding is one of the crucial problems related to such ADGs; on the other hand, islanding detection is also a challenging aspect. Therefore, an extensive review of islanding real-time depiction and islanding detection strategies (IDS) is provided in this work. Initially, the focus is on islanding detection concept depiction, islanding detection standardization, benchmark test systems for IDS validation, and software/tools and an analysis of their pros and cons. Then, the detailed classification of IDSs is presented with an emphasis on remote and local methods. Passive, active, and hybrid can be used further to categorize local IDSs. Moreover, the statistical comparative analysis of the IDSs based on the non-detection-zone (NDZ), cost-effectiveness, and false operation are mentioned. The research gap and loopholes in the existing work based on limitations in the existing work are presented. Finally, the paper is concluded with detailed recommendations.
... A complete overview of the techniques of data analytics and islanding detection of distribution systems using phasor measurement unit data is presented in [51]. Other limitations related to islanding events have also been discussed in the same research work Correspondingly, M. Paolone et al. presented a new model of PMU [52], for a specified dynamic distribution system. ...
The electrical network is a man-made complex network that makes it difficult to monitor and control the power system with traditional monitoring devices. Traditional devices have some limitations in real-time synchronization monitoring which leads to unwanted behavior and causes new challenges in the operation and control of the power systems. A Phasor measurement unit (PMU) is an advanced metering device that provides an accurate real-time and synchronized measurement of the voltage and current waveforms of the buses in which the PMU devices are directly connected in the grid station. The device is connected to the busbars of the power grid in the electrical distribution and transmission systems and provides time-synchronized measurement with the help of the Global Positioning System (GPS). However, the implementation and maintenance cost of the device is not bearable for the electrical utilities. Therefore, in recent work, many optimization approaches have been developed to overcome optimal placement of PMU problems to reduce the overall cost by providing complete electrical network observability with a minimal number of PMUs. This research paper reviews the importance of PMU for the modern electrical power system, the architecture of PMU, the differences between PMU, micro-PMU, SCADA, and smart grid (SG) relation with PMU, the sinusoidal waveform, and its phasor representation, and finally a list of PMU applications. The applications of PMU are widely involved in the operation of power systems ranging from power system control and monitor, distribution grid control, load shedding control and analyses, and state estimation which shows the importance of PMU for the modern world.
Reduction of fossil fuel usage, clean energy supply, and dependability are all major benefits of integrating distributed energy resources (DER) with electrical utility grid (UG). Nevertheless, there are difficulties with this integration, most notably accidental islanding that puts worker and equipment safety at risk. Islanding detection methods (IDMs) play a critical role in resolving this problem. All IDMs are thoroughly evaluated in this work, which divides them into two categories: local approaches that rely on distributed generation (DG) side monitoring and remote approaches that make use of communication infrastructure. The study offers a comparative evaluation to help choose the most efficient and applicable IDM, supporting well-informed decision-making for the safe and dependable operation of distributed energy systems within electrical distribution networks. IDMs are evaluated based on NDZ outcomes, detection duration, power quality impact, multi-DG operation, suitability, X/R ratio reliance, and efficient functioning.
The emergence of microgrids and the increasing adoption of Distributed Generation Systems (DGS) have created an opportunity to replace traditional fossil fuels with renewable resources. Such a shift poses security and power quality challenges that must be addressed by academics and industrial research paradigms. Unintentional islanding is an important security concern, as it can result in power quality degradation, electrical hazards, and equipment damage. To address this problem and find efficient solutions, many anti-islanding techniques to detect and eliminate the phenomenon can be found in the specialized literature. These solutions can be classified as passive, active, remote, hybrid, or based on machine learning and signal processing techniques. In this context, this paper provides a comprehensive review of existing anti-islanding methods, highlighting their importance in preventing dangerous situations. The review includes a detailed analysis of advantages and limitations found for each method, as well as its suitability for practical applications. The goal is to provide a valuable resource for researchers and practitioners in the field of distributed power systems, enabling them to choose the most appropriate anti-islanding method for their specific needs. Overall, this paper aims to address the challenges posed by unintentional islanding and promote the adoption of renewable energy resources for a more sustainable future.
Phasor Measurement Units (PMUs) are the backbone of smart grids that are able to measure power system observability in real-time. The deployment of synchronized sensors in power networks opens up the advantage of real-time monitoring of the network state. An optimal number of PMUs must be installed to ensure system observability. For that reason, an objective function is minimized, reflecting the cost of PMU installation around the power grid. As a result, a minimization model is declared where the objective function is defined over an adequate number of constraints on a binary decision variable domain. To achieve maximum network observability, there is a need to find the best number of PMUs and put them in appropriate locations around the power grid. Hence, maximization models are declared in a decision-making way to obtain optimality satisfying a guaranteed stopping and optimality criteria. The best performance metrics are achieved using binary integer, semi-definite, and binary polynomial models to encounter the optimal number of PMUs with suitable PMU positioning sites. All optimization models are implemented with powerful optimization solvers in MATLAB to obtain the global solution point.
Power networks currently make substantial use of phasor measurement units (PMU) due to the ability to enhance system reliability. PMUs can produce data on system frequency, angle, voltage, and current magnitude. A broadly applied algorithm using the discrete Fourier transform has been suggested for determining PMU. PMUs guarantee grid observability, which unquestionably upholds the stability of the power network; however, the main problems with PMUs include noise stuffing, design, signal channeling, and other complicated elements, like on-site data quality. Missing PMU data are recovered using polynomial interpolation. Lagrange interpolation is also used to assess data quality and locate PMU data that are missing. PMU is a monitoring tool that assesses frequency, voltage/current angle, and magnitude. Universally synchronized data from PMU are discovered and analyzed to improve the transmission and distribution side’s dependability. PMUs can handle magnitude and phasors simultaneously, which is why they are also known as synchro-phasor devices.
