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The efficient placement of Phasor Measurement Units (PMUs) is crucial to minimize their number while ensuring full power system observability. A power system is deemed observable when the voltage at every bus is known. This study proposes a topology transformation approach that combines a Zero-Injection Bus (ZIB) with one of its neighboring buses to achieve optimal PMU placement. The choice of the neighboring bus for combination with the ZIB significantly influences the outcome of the merging process. To identify the optimal candidate bus for merging with the ZIB, the proposed approach utilizes three selection principles aimed at determining the minimal number of PMUs necessary for complete system observability. This study also examines the scenario of power flow measurements, formulating the problem using Integer Linear Programming (ILP). The effectiveness of the proposed method is demonstrated through simulations conducted in MATLAB on various IEEE Bus systems, with a detailed explanation provided using the IEEE 14-Bus system. Comparative analysis reveals that the number of PMUs obtained with the suggested method is competitive with existing techniques. Furthermore, this study includes a comparison between the IEEE 30-Bus and IEEE 14-Bus systems by inducing faults at specific buses. This is done to verify whether PMUs placed at optimal locations ensure complete coverage of all buses in both systems. The comparison also extends to evaluating the cost implications of placing PMUs solely at these optimal locations. [ABSTRACT FROM AUTHOR]

The optimal PMU placement problem is placing the minimum number of PMUs in the network to ensure complete network observability. It is an NP-complete optimization problem. PMU placement based on cost and critical nodes is solved separately in the literature. This paper proposes a novel approach, a degree of centrality in the objective function, to combine the effect of both strategies to place PMUs in the power network optimally. The contingency analysis and the effect of zero-injection buses are solved to ensure the reliability of network monitoring and attain a minimum number of PMUs. Integer linear programming is used on the IEEE 7-bus, IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus, and IEEE 118-bus systems to solve this problem. The results are evaluated based on two performance measures: the bus observability index (BOI) and the sum of redundancy index (SORI). On comparison, it is found that the proposed methodology has significantly improved results, i.e., a reduced number of PMUs and increased network overall observability (SORI). This methodology is more practical for implementation as it focuses on critical nodes. Along with improvement in the results, the limitations of existing indices are also discussed for future work. [ABSTRACT FROM AUTHOR]

Phasor measurement units (PMUs) are deployed at power grid nodes around the transmission grid, determining precise power system monitoring conditions. In real life, it is not realistic to place a PMU at every power grid node; thus, the lowest PMU number is optimally selected for the full observation of the entire network. In this study, the PMU placement model is reconsidered, taking into account single- and multi-capacity placement models rather than the well-studied PMU placement model with an unrestricted number of channels. A restricted number of channels per monitoring device is used, instead of supposing that a PMU is able to observe all incident buses through the transmission connectivity lines. The optimization models are declared closely to the power dominating set and minimum edge cover problem in graph theory. These discrete optimization problems are directly related with the minimum set covering problem. Initially, the allocation model is declared as a constrained mixed-integer linear program implemented by mathematical and stochastic algorithms. Then, the 0 / 1 integer linear problem is reformulated into a non-convex constraint program to find optimality. The mathematical models are solved either in binary form or in the continuous domain using specialized optimization libraries, and are all implemented in YALMIP software in conjunction with MATLAB. Mixed-integer linear solvers, nonlinear programming solvers, and heuristic algorithms are utilized in the aforementioned software packages to locate the global solution for each instance solved in this application, which considers the transformation of the existing power grids to smart grids. [ABSTRACT FROM AUTHOR]

This work proposes an approach based on critical dynamic modes to detect four types of phasor measurement unit (PMU) data anomalies in smart power grids. Smart power grids as modern power systems involve new information and communication technologies (ICTs), relying on real-time data, like data of PMUs. Due to the dependence of PMUs on communication technology, PMUs are prone to different data anomalies and false data injection attacks, such as replay attack, which has been rarely considered in previous works. Since detecting abnormal data is necessary for the proper function of the power system, most detection methods have been data-based approaches that may need a large amount of data and lead to more complexity. This paper proposes an online detection approach based on the minimum number of dynamic modes for the local detection of a replay attack and three types of bad data injection on PMU measurements. For this purpose, a distributed modeling of the power system is considered. Then, a replay attack and bad data injections are detected locally by tracking critical dynamic modes. The proposed approach can detect simultaneous data anomalies on more than one PMU. The effectiveness and accuracy of this approach are evaluated through simulations on the 10-machine New England 39-bus power system.

Real-time monitoring of transient stability has gained significant importance in the power system. This paper proposes an advanced hybrid Transient Stability Assessment (TSA) monitoring system, composed of a Convolutional Neural Network (CNN) and Random Forest (RF) classifier which captures the PMU measurements in real-time. CNN utilizes heatmaps for detecting the transient stability status which is hereby used as a feature extractor for the RF classifier. The IEEE-14 and the IEEE-39 bus systems are used as case studies in MATLAB. A wind turbine with a doubly-fed induction generator is also installed to inspect its impact on the power system stability. The efficiency of the system is tested under different contingency scenarios by implementing various faults on distinct locations under dynamic loading conditions. The test results verified that the proposed hybrid CNN-RF (Convolutional Neural Network-Random Forest) model exhibited excellent results in terms of monitoring accuracy, higher precision, and performance in the presence of noise. [ABSTRACT FROM AUTHOR]

One of the most prevalent and destructive types of cyber-attacks on power systems is the false data injection (FDI) attack. In a false data injection attack, the attacker inflicts large damages on the network by manipulating the measurements. The pivotal solution to opposing this type of cyber-attack is to use phasor measurement units (PMUs). In this paper, a new method is presented to confront the FDI attack by using the optimal placement of PMU instruments. In the proposed algorithm at the beginning, all PMUs placements that achieve network observability are determined using the tabu search (TS) algorithm. Then, from the observable placement vectors, the placements that minimize the possibility of a cyber-attack on the network is identified. For this purpose, a new attack criterion is presented, which is obtained from the adversary strategy in the attack scheme. Since the measurements obtained from the PMUs must be transferred to a phasor data concentrator (PDC) center, the PDC placement also must be determined. In this paper, the optimal placement of PDC is presented by considering the cost of communication infrastructure, because the cost of communication infrastructure between PMUs and PDC is significant. For this purpose, we have used the Kruskal algorithm. The simulations performed on the IEEE 30-bus and 118-bus test system confirm the effectiveness of the proposed method for opposing cyber-attacks and reducing the cost of communication infrastructure. [ABSTRACT FROM AUTHOR]

In power systems, maintaining transient stability is crucial to avoid unanticipated blackouts. The role of Transient Stability Assessment (TSA) is vital for quickly identifying and promptly addressing instabilities. TSA facilitates rapid reactions to serious fault conditions. This paper pioneers the integrated comparison of two distinct methodologies—Maximal Lyapunov Exponent (MLE) methods and Convolutional Neural Networks (CNN)—in a single unified framework for transient stability assessment in power systems, uniquely evaluating their accuracy and reliability for TSA. The CNN-based method uses measured time series data from voltage magnitude, phase angle, and frequency measurements at generator buses, while the MLE approach utilizes both phase angles and frequency of generator buses. This paper provides a qualitative and quantitative comparison of the performance and accuracy of MLE and CNN. This research utilizes the same case studies conducted on the Nordic32 system for both MLE and CNN to ensure robust, unbiased comparisons and promote interdisciplinary research, aligning with current trends in AI integration in power systems.

This paper proposes an empirical wavelet transform (EWT) based method for identification and analysis of sub-synchronous oscillation (SSO) modes in the power system using phasor measurement unit (PMU) data. The phasors from PMUs are preprocessed to check for the presence of oscillations. If the presence is established, the signal is decomposed using EWT and the parameters of the mono-components are estimated through Yoshida algorithm. The superiority of the proposed method is tested using test signals with known parameters and simulated using actual SSO signals from the Hami Power Grid in Northwest China. Results show the effectiveness of the proposed EWT-Yoshida method in detecting the SSO and estimating its parameters.

This paper presents an assessment of corona loss performance for two overhead transmission lines of different ages using synchronized phasor measurements. In general, it is believed that the aged overhead transmission lines have constrained performance compared to the newer ones as they have been exposed to different environmental conditions for longer period of time. Therefore, in view of availability of synchronized measurements it is possible and of relevance to assess how the corona losses are influenced by line age and what are the main influencing factors. The results of the analysis indicate that there are higher corona losses associated with older overhead transmission lines and respective information is directly applicable for transmission system operators in their system operational planning in order to optimize the purchase of losses from the market.

Due to their accurate time stamping, phasor measurement units (PMUs) are being used for the modern day power grid protection applications. As PMUs provide synchronous data, so in this paper, a fault location algorithm has been proposed for a three-terminal non-homogeneous transmission network with the help of synchronized data, available at all the three ends. The line parameters are estimated before they are used in solution for identifying the faulted section and fault location. PSCAD ® software is used for obtaining synchronized voltages and currents data of the network by simulating various types of faults. MATLAB is used for developing software code for estimating the fault location and fault section identification. The fault location estimation have been carried out considering transmission line model as frequency distributed transmission line model to evaluate the performance of the proposed algorithm. The fault location algorithm has also been evaluated for effect of fault resistance, source internal angle, source impedance, synchronization errors, change in network configuration, dc offset effect, fault type in other phases and CT saturation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In order to ensure the accurate application of the data collected by the phasor measurement unit (PMU), it is necessary to eliminate the abnormal data in its measured values. The existing PMU abnormal data identification algorithm has the disadvantages of high algorithm complexity, difficulty in online updating, difficulty in the calibration of multi-source data, and difficulty in application relying on multi-source data. In this paper, an abnormal data identification framework is proposed based on the PMU event data and abnormal data model and the definition of PMU abnormal data identification information entropy. On the basis of the framework, a PMU abnormal data identification algorithm is proposed based on the balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm. The proposed algorithm is implemented, and an algorithm experiment is carried out for the PMU dataset of a substation. The experimental results show that the proposed algorithm has better accuracy and real-time performance than one-class support vector machine (OCSVM) algorithm and gap statistic algorithm.

Calculation of steady state mode is the most used tool of modern electrical networks’ analysis. The current study is focused on the application of synchronized phasor measurements technology for steady state mode calculation. There is shown that the solely use of synchronized phasor measurements as initial information for calculating the steady state mode or in conjunction with traditional telemetry from Supervisory Control And Data Acquisition system (SCADA) has a number of advantages over classical methods, which is due to the simplification of the computational process, reduced time and required computational memory, and more reliable informational support. This paper presents the novel approach which consists in steady state mode calculations using accelerated and ultra-accelerated algorithms. These algorithms are based on the implementation of the method of determining values and special structure of the matrix of nodal admittances, what allow to reduce the number of equations, that must be solved in order to find the searched nodal voltages. Besides, there is no need to set balancing node, and dependences between searched and known nodal voltages become linear. The calculations performed for test scheme IEEE-14 demonstrate that both algorithms provide high accuracy results, increase the simplicity and speed of calculation by separate solving of linear equations.

Modern power systems are rapidly transitioning towards a fully digital substation paradigm. Based on the IEC 61850, a common communication protocol between the different intelligent electronic devices (IEDs) promises a significant enhancement in terms of efficiency and interoperability. In this context, synchronization represents a crucial aspect as it allows us to rigorously compare measurements taken at the same time in different locations. In this paper, we consider a measurement chain for synchrophasor estimation based on digital inputs: an instrument transformer, a stand-alone merging unit (SAMU) and a phasor measurement unit (PMU). Both the SAMU and the PMU are equipped with independent synchronization sources. In case the SAMU loses its synchronization, the final measurement result would be considered invalid until a complete restoration of the SAMU synchronization status. In view of a longer continuity of operation, this paper proposes an alternative approach to evaluate the PMU Time Quality in real-time. This approach allows for continuing crucial monitoring and control operations, such as state estimation and fault detection, even in the presence of temporary loss of synchronization. A characterization, in both simulated and experimental conditions, proves the potential and reliability of the proposed approach. In the considered test case, the come-back within a sufficient time quality is correctly detected in less than 200 s, while waiting for the full restoration of the SAMU time reference would cost several minutes.

Phasor measurement units (PMUs) are deployed at power grid nodes around the transmission grid, determining precise power system monitoring conditions. In real life, it is not realistic to place a PMU at every power grid node; thus, the lowest PMU number is optimally selected for the full observation of the entire network. In this study, the PMU placement model is reconsidered, taking into account single- and multi-capacity placement models rather than the well-studied PMU placement model with an unrestricted number of channels. A restricted number of channels per monitoring device is used, instead of supposing that a PMU is able to observe all incident buses through the transmission connectivity lines. The optimization models are declared closely to the power dominating set and minimum edge cover problem in graph theory. These discrete optimization problems are directly related with the minimum set covering problem. Initially, the allocation model is declared as a constrained mixed-integer linear program implemented by mathematical and stochastic algorithms. Then, the 0/1 integer linear problem is reformulated into a non-convex constraint program to find optimality. The mathematical models are solved either in binary form or in the continuous domain using specialized optimization libraries, and are all implemented in YALMIP software in conjunction with MATLAB. Mixed-integer linear solvers, nonlinear programming solvers, and heuristic algorithms are utilized in the aforementioned software packages to locate the global solution for each instance solved in this application, which considers the transformation of the existing power grids to smart grids.

The optimal PMU placement problem is placing the minimum number of PMUs in the network to ensure complete network observability. It is an NP-complete optimization problem. PMU placement based on cost and critical nodes is solved separately in the literature. This paper proposes a novel approach, a degree of centrality in the objective function, to combine the effect of both strategies to place PMUs in the power network optimally. The contingency analysis and the effect of zero-injection buses are solved to ensure the reliability of network monitoring and attain a minimum number of PMUs. Integer linear programming is used on the IEEE 7-bus, IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus, and IEEE 118-bus systems to solve this problem. The results are evaluated based on two performance measures: the bus observability index (BOI) and the sum of redundancy index (SORI). On comparison, it is found that the proposed methodology has significantly improved results, i.e., a reduced number of PMUs and increased network overall observability (SORI). This methodology is more practical for implementation as it focuses on critical nodes. Along with improvement in the results, the limitations of existing indices are also discussed for future work.

The dynamic monitoring, control, and protection of modern power systems in real time require time-stamped electrical measurements to accurately estimate the bus voltage phasors using the state estimation function under normal and abnormal conditions. These measurements can be acquired by time-synchronized devices, known as phasor measurement units (PMUs). PMUs can measure bus voltage and branch current phasors of a three-phase network, as well as the frequency and the rate of change of frequency (ROCOF), with high speed, accuracy and time stamping provided by global positioning system (GPS) at the coordinated universal time (UTC). Various phasor estimation algorithms have been proposed in the literature, while most of them are concentrated in the discrete Fourier transform (DFT) algorithm, where an integer number of samples multiple of the nominal frequency is required for the computations. In cases where the frequency of the power grid deviates from its nominal value, the raw application of the DFT approach can lead to large errors during phasor estimation. Another approach of the phasor estimation is based on the phase-locked loop (PLL) techniques, widely used in grid tie inverters. PLL techniques can track dynamically (continuous time) the estimated frequency to the time-variant frequency of the power grid. A brief introduction to the basic concepts of the synchrophasor definition is provided, while the main DFT methods for synchrophasor estimation according to recent literature are mentioned. PLL-based PMU techniques are reviewed for both steady-state and dynamic conditions according to IEEE standards. In conclusion, the performance of PLL-based PMU algorithms presented in this literature review is discussed. [ABSTRACT FROM AUTHOR]

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This paper proposes a hierarchical multi-stage approach based on a distributed level phasor measurement unit (µPMU) at local controllers and a phasor data concentrator (PDC) at the central control unit to restore system voltage when it exceeds the limits recommended by the IEEE 1547-2018 standard. The proposed algorithm does not need an accurate system model or employ optimization solutions. Therefore, it has less implementation complexity and would be popular among distribution network service providers (DNSPs) and distribution network operators (DNOs) as it does not suffer from cost and computational complexity limitations. A PMU-PDC-based communication platform has been developed as a more efficient alternative to the supervisory control and data acquisition (SCADA) system, and provides superior characteristics, including a higher sample rate, higher data resolutions, and faster communication. The proposed coordinated algorithm aims to postpone power generation curtailment in distributed energy resources (DERs) with overvoltage problems (local DERs) by incorporating all the DERs that are not subjected to voltage violation (remote DERs) by absorbing their maximum reactive power capacity. If the system voltage has not recovered after absorbing all of the reactive power capacity of all the DERs, a reduced active power curtailment proposed by the algorithm is then applied to the system to control the voltage. The proposed strategy has been simulated in MATLAB and applied to IEEE 13-bus and IEEE 33-bus radial distribution benchmark systems to validate the performance of the system, in terms of its ability to coordinate voltage control and the accuracy of the PMU-PDC-based communication interface. [ABSTRACT FROM AUTHOR]

In an attempt to provide reliable power distribution, smart grids integrate monitoring, communication, and control technologies for better energy consumption and management. As a result of such cyberphysical links, smart grids become vulnerable to cyberattacks, highlighting the significance of detecting and monitoring such attacks to uphold their security and dependability. Accordingly, the use of phasor measurement units (PMUs) enables real-time monitoring and control, providing informed-decisions data and making it possible to sense abnormal behavior indicative of cyberattacks. Similar to the ways it dominates other fields, deep learning has brought a lot of interest to the realm of cybersecurity. A common formulation for this issue is learning under data complexity, unavailability, and drift connected to increasing cardinality, imbalance brought on by data scarcity, and fast change in data characteristics, respectively. To address these challenges, this paper suggests a deep learning monitoring method based on robust feature engineering, using PMU data with greater accuracy, even within the presence of cyberattacks. The model is initially investigated using condition monitoring data to identify various disturbances in smart grids free from adversarial attacks. Then, a minimally disruptive experiment using adversarial attack injection with various reality-imitating techniques is conducted, inadvertently damaging the original data and using it to retrain the deep network, boosting its resistance to manipulations. Compared to previous studies, the proposed method demonstrated promising results and better accuracy, making it a potential option for smart grid condition monitoring. The full set of experimental scenarios performed in this study is available online. [ABSTRACT FROM AUTHOR]

As the traditional generation is gradually replaced by inverter-based resources, a lack of rotational inertia is now a common issue of modern power systems, which leads to an increasingly larger rate of change of frequency (RoCoF) following contingencies and may result in frequency collapse. As a crucial index of the frequency security and stability of power systems, the accurate estimation of the RoCoF can be a foundation for the development of advanced operations and control techniques of the future power system. This paper firstly analyzes the role of the RoCoF in typical blackouts occurring in recent years and discusses the physical and numerical nature of the RoCoF; then, by introducing the frequency spatial distribution of the power system, the paper discusses the concept of the "center" RoCoF that can present the frequency security and stability of the entire system. The estimation and prediction techniques of the maximal power system RoCoF following a contingency and the existing real-time tracking techniques of the power system RoCoF are comprehensively reviewed. Finally, the open questions and related research topics of the RoCoF estimation are discussed. [ABSTRACT FROM AUTHOR]

The present scenario, the power demand has been increased so; it requires the power system to operate within limits. As the load is varying continuously, the voltage stability problems will arise. The foremost cause of voltage instability is deficiency of reactive power. The voltage stability and power transfer capability can be enhanced by compensation of reactive power. By placing Flexible AC Transmission System (FACTS) devices the performance of power system can be improved but these devices are expensive so it has to be placed at optimal location. This paper deals with the optimal placement of TCSC and Fuel cell with PMU in IEEE-14 bus system using PSAT to analyze and enrich the voltage stability margin of a power system network in real time monitoring. In real time the measurement of power system parameters such as current and voltage phasors, frequency can be effectively carried out by synchronized PMU. [ABSTRACT FROM AUTHOR]

With the growing emphasis on mitigating climate change, the power industry is moving toward renewable energy sources as an alternative to fossil fuel-based power plants. The transition to renewable energy has created numerous challenges, one of which is the low levels of inertia that impact the stability of power systems. Therefore, inertia monitoring has become an integral part of power system operation to dispatch renewable energy sources while maintaining frequency stability. This article presents an online method to continuously estimate the inertia of a power system. The inertia is computed from data provided by Phasor Measurement Units (PMUs) using small variations in frequency and power under ambient conditions. The method uses electrical and kinetic energy variations to compute inertia. In addition, a $Q$ -learning-based method is presented to identify mechanical power changes to discard invalid inertia estimates. The method is demonstrated using the IEEE-39 bus system to monitor the regional inertia of the test system.

The distribution of measurement noise is usually assumed to be Gaussian in the optimal phasor measurement unit (PMU) placement (OPP) problem. However, this is not always accurate in practice. This paper proposes a new OPP method for smart grids in which the effects of conventional measurements, limited channels of PMUs, zero-injection buses (ZIBs), single PMU loss contingency, state estimation error (SEE), and the maximum SEE variance (MSEEV) are considered. The SEE and MSEEV are both obtained using a robust $t$-distribution maximum likelihood estimator (MLE) because $t$-distribution is more flexible for modeling both Gaussian and non-Gaussian noises. The A- and G-optimal experimental criteria are utilized to form the SEE and MSEEV constraints. This allows the optimization problem to be converted into a linear objective function subject to linear matrix inequality observability constraints. The performance of the proposed OPP method is verified by the simulations of the IEEE 14-bus, 30-bus, and 118-bus systems as well as the 188-bus practical distribution system in China.

Micro-phasor measurement units (µPMUs) with a micro-second resolution and milli-degree accuracy capability are expected to play an important role in improving the state estimation accuracy in the distribution network with increasing penetration of distributed generations. Therefore, this paper investigates the problem of how to place a limited number of µPMUs to improve the state estimation accuracy. Combined with pseudo-measurements and supervisory control and data acquisition (SCADA) measurements, an optimal µPMU placement model is proposed based on a two-step state estimation method. The E-optimal experimental criterion is utilized to measure the state estimation accuracy. The nonlinear optimization problem is transformed into a mixed-integer semidefinite programming (MISDP) problem, whose optimal solution can be obtained by using the improved Benders decomposition method. Simulations on several systems are carried out to evaluate the effective performance of the proposed model.

Synchrophasor measurement units (PMUs) provide synchronized measurement data for wide-area applications. To improve the effectiveness of synchrophasor-based applications, field PMUs must be tested to ensure their performance and data quality. In the companion paper (Part I), we proposed a field PMU test and calibration framework consisting of a PMU calibrator and analysis center. Part I presents the development and test of the PMU calibrator. This paper focuses on the analysis center and field test applications. First, the critical component of the analysis center is the signal identification module, for which the step and oscillation signal identification methods are proposed. Here, the performance evaluation criteria of PMU in these two cases are different from others. The methods include a step signal detection method based on singular value decomposition (SVD), which has the capability of weak step detection to account for energy leakage of the signal during the step process, and an oscillation signal identification method based on SVD and fast Fourier transform, which can accurately extract oscillation components that benefit from the adaptive threshold setting method. Second, the analysis center software is implemented based on identification results. By integrating the PMU calibrator in Part I with the analysis center in Part II, we can examine in depth the field PMU test applications in three test scenarios, including standard, playback, and field signal test. Results demonstrate the effectiveness and applicability of the proposed field PMU test methods from both Parts I and II.

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Natural calamities that include heavy storms, earthquakes, snowfall, and drought/heat waves are now predominant over the world. These natural calamities cause high impact low frequency (HILF) events, such as multi-location faults and multi-area stressed conditions, that may cause large blackouts and reduce system resilience. This paper proposes a system integrity protection scheme (SIPS) to accurately identify the multi-location faults and multi-area stressed system conditions. The proposed SIPS accurately detects the multi-location faults for synchronized operation of distance relay and simultaneously provides backup distance relay blocking in higher operating zones, during stressed system conditions. For the entire operation, data from WAMS are used to first detect the abnormal operating areas followed by identification of vulnerable buses in the system. Further, by comparing the phase angles of transmission lines connected to vulnerable buses, faulted lines are identified. The effectiveness of the method is tested for multiple scenarios carried out on the WSCC 9-bus system and New England 39-bus system in MATLAB/Simulink platform. For practical implementation, the performance is validated on a 4-bus test network on Typhoon hardware in loop (HIL) system. Adequate and synchronized relay operation results in the augmentation of resiliency in the power system network. Calculated resistance indices reflect increase in transmission capacity up to 140%, load loss reduction percent up to 0%, and improvement in the active power deficiency up to 57% during different operating conditions for considered test systems. [ABSTRACT FROM AUTHOR]

Several factors affect existing electric power systems and negatively impact power quality (PQ): the high penetration of renewable and distributed sources that are based on power converters with or without energy storage, non-linear and unbalanced loads, and the deployment of electric vehicles. In addition, the power grid needs more improvement in the performances of real-time PQ monitoring, fault diagnosis, information technology, and advanced control and communication techniques. To overcome these challenges, it is imperative to re-evaluate power quality and requirements to build a smart, self-healing power grid. This will enable early detection of power system disturbances, maximize productivity, and minimize power system downtime. This paper provides an overview of the state-of-the-art signal processing- (SP) and pattern recognition-based power quality disturbances (PQDs) characterization techniques for monitoring purposes. [ABSTRACT FROM AUTHOR]

Modern power systems are rapidly transitioning towards a fully digital substation paradigm. Based on the IEC 61850, a common communication protocol between the different intelligent electronic devices (IEDs) promises a significant enhancement in terms of efficiency and interoperability. In this context, synchronization represents a crucial aspect as it allows us to rigorously compare measurements taken at the same time in different locations. In this paper, we consider a measurement chain for synchrophasor estimation based on digital inputs: an instrument transformer, a stand-alone merging unit (SAMU) and a phasor measurement unit (PMU). Both the SAMU and the PMU are equipped with independent synchronization sources. In case the SAMU loses its synchronization, the final measurement result would be considered invalid until a complete restoration of the SAMU synchronization status. In view of a longer continuity of operation, this paper proposes an alternative approach to evaluate the PMU Time Quality in real-time. This approach allows for continuing crucial monitoring and control operations, such as state estimation and fault detection, even in the presence of temporary loss of synchronization. A characterization, in both simulated and experimental conditions, proves the potential and reliability of the proposed approach. In the considered test case, the come-back within a sufficient time quality is correctly detected in less than 200 s, while waiting for the full restoration of the SAMU time reference would cost several minutes. [ABSTRACT FROM AUTHOR]

In the modern epoch, phasor measurement units (PMUs) are deployed within the power grids for real-time supervision of the grids' static and dynamic behavior as the complexity of power grids has increased manifold due to the integration of extensively different generating resources. This paper presents a stability study of measurement class for reference phasor measurement unit for 1 year using the traceable PMU Calibration System at CSIR-National Physical Laboratory, New Delhi with ± 50 ppm to ± 100 ppm uncertainty. The PMU under test is evaluated for measurement class (M class) critical parameters for steady as well as dynamic performance analysis at 50 frames per second (fps) reporting rate with nominal frequency of 50 Hertz (Hz). The performance of PMU is evaluated and tested in conformance with IEC/IEEE 60255-118-1:2018 standard. A very stable reference PMU has been demonstrated for the transformation and enrichment of the power sector enabling an adaptive, reliable, sustainable, and digitally robust ecosystem. [ABSTRACT FROM AUTHOR]

Due to the significant number of distributed generators in the electric power system, islanding detection requirements are becoming an increasingly prominent aspect of the power system. The island detection system depends on accurate threshold determination since an incorrect threshold might result in a hazardous situation. To evaluate the proposed method's capacity to discriminate between different events, this study examined different unintentional islanding conditions such as under frequency and over frequency. The purpose of this study is to establish the threshold of the under and over frequency island conditions. The under frequency island condition happens when the distributed generator (DG) capacity exceeds the amount of connected load; on the other hand, the over frequency island condition happens during a higher connected load compared to the capacity of the DG. The contribution of this research is to propose an unintentional island threshold setting technique based on bus voltage angle difference data of the phasor measurement unit (PMU). In the PowerWorld simulator, the Utility Kerteh (location: Terengganu, Malaysia) bus system has been designed and simulated in this work. The test system has four distinct islanding scenarios under two conditions, and the performance of the proposed methods demonstrates that for the under frequency islanding conditions the scenario's threshold can be taken at a minimum of 40 milliseconds (ms) and a maximum of 60 ms, while the over frequency condition island threshold can be placed at a minimum of 60 ms and a maximum of 80 ms depending on the scenarios. Therefore, the proposed technique will be contributed to increase the reliability of the overall distribution grid so the unintentional island can be detected faster in terms of time. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

With the recent developments in renewable energy generation and addition of power electronic devices, power system dynamics have become extremely complex. One of the challenges faced due to this transition is the sub synchronous oscillations caused by the interaction of renewable energy sources and various components of the power grid. Recently reported incidents due to sub synchronous oscillations highlight the need of monitoring and suppression of these harmful oscillations in real time. This paper gives an overview of the phenomena of sub synchronous oscillations and discusses the existing monitoring and damping techniques along with their limitations. Further, it highlights the research trends along this path. [ABSTRACT FROM AUTHOR]

The network identification plays a very prominent role for the network operator to accomplish the various objectives such as state-estimation, monitoring, control, planning, and real-time analytics. The network structure varies from time-to-time and its details are often not available with the network operator. To address this issue, in this article, an alternating direction method of multipliers (ADMM) based framework is presented herein to identify the network topology and line parameters using smart meter and microphasor measurement unit (μ) measurements. The presented algorithm is divided into two sections 1) approximate parameter evaluation through regression, to extract the partial topology information and 2) complete network topology identification through the ADMM framework. This algorithm accomplishes the objectives of identifying the network configuration, branch parameters (e.g., conductance and susceptance), and change in branch parameters. Simulation results demonstrate the effectiveness of the presented algorithm on the benchmarked IEEE 13-bus and IEEE 123-bus feeders under various operating scenarios. Furthermore, the presented framework illustrates excellent network identification even with the presence of the stochastic nature of renewable power generation. The presented algorithm exhibits an excellent performance even with the consideration of noise in both measurements. In addition, the comparative performance is carried out on the benchmarked unbalanced IEEE 13-bus and balanced IEEE 33-bus feeders to highlight the efficacy of the presented framework over the state-of-art framework. [ABSTRACT FROM AUTHOR]

Voltage sags are unavoidable and seriously harmful to sensitive loads. Reasonable configuration of limited monitors can reduce monitoring costs,provide data support for voltage sag management and reduction of voltage sag hazards. Therefore,the optimal configuration of voltage sag monitors is important. Aiming at the traditional method that ignores the inconsistency of the degree of voltage sag damage in different areas,a joint optimal configuration model of voltage sag monitor considering the monitoring reliability of sag sensitive area and the location of phasor measurement unit (PMU) is proposed. Considering the considerable sag as the constraint condition,the minimum number of monitors and the widest coverage of the sag sensitive area are used as the goals to form an optimized configuration model. In this model,the PMU is configured to assist in monitoring voltage sags. In addition,the Zeroin method is used to improve the accuracy of the exposed area calculation. The IEEE 30-bus system is used in simulation to prove that the difficulty of balancing the monitoring cost with the monitoring capability of sensitive areas is overcome by the proposed method. The proposed method can ensure the minimum number of monitors and high redundancy coverage of sensitive loads,thus the economic benefits.