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Recently, owing to the high penetration of renewable power generations, random access to dynamical loads and wide application of power-electronic devices, the modern power systems exhibit new features such as high dynamics, low inertia and weak voltage and frequency support. As a powerful tool in revealing the dynamical evolution of the modern power systems, the dynamic state estimation (DSE) scheme gains a surge of research enthusiasms. Note that in actual power systems, various styles of power generations and mixed measurements generated by the supervisory control and data acquisition (SCADA) system and the phasor measurement unit (PMU) pose higher demands on the DSE. In addition, the measurements with anomalies such as gross errors, outliers and incomplete information are often encountered due to the unreliable measurement facilities, constrained communication resources and cyber-threats. Such anomalies, if not tactfully handled, would largely impair the DSE performance. In this paper, a systematic and timely review with respect to DSE for power systems is provided. First, several typical models for power systems include quasi-steady model, synchronous-generator-based multi-machine model and renewable power generation model are introduced. Then the measurement facilities of power systems and their features as well as the measurements with anomalies are reviewed, respectively. Subsequently, the DSE schemes are recalled and discussed from the perspectives of framework, methodology, anomaly-resistant ability and application, respectively. Finally, some possible future research topics are outlined for the DSE of power systems. [ABSTRACT FROM AUTHOR]

As one of the functions integrating energy management systems, state estimation (SE) is instrumental in monitoring power networks, allowing the best possible use of energy resources. It plays a decisive role in debugging if sufficient data are available, ruined if not. Criticality analysis (CA) integrates SE as a module in which elements of the estimation process—taken one-by-one or grouped (tuples of minimal multiple cardinality)—are designated essential. The combinatorial nature of extensive CA (ExtCA), derestricted from identifying only low-cardinality critical tuples, characterizes its computational complexity and imposes defiant limits in implementing it. This paper presents the methodology for ExtCA and compares algorithms to find an efficient solution for expanding the boundaries of this analysis problem. The algorithms used for comparison are one sequential Branch&Bound (a well-known paradigm for combinatorial optimization recently used in ExtCA) and two new parallels implemented on the central processing unit (CPU) and the graphics processing unit (GPU). The conceived parallel architecture favors evaluating massive combinations of diverse cardinality measuring unit (MU) tuples in ExtCA. The acronym MU refers to the aggregate of devices deployed at substations, such as a remote terminal unit, intelligent electronic device, and phasor measurement unit. The numerical results obtained in the paper show significant speed-ups with the novel parallel GPU algorithm, tested on different and real-scale power grids. Since, the visualization of the ExtCA results is still not a well-explored field, this work also presents a novel way of graphically depicting spots of weak observability using MU-oriented ExtCA. [ABSTRACT FROM AUTHOR]

Phasor estimation plays a critical role in island detection, fault detection, voltage stability monitoring, and state estimation within power systems. This paper proposes a phasor estimation method using micro-Phasor Measurement Units (µPMUs) in a Smart Distribution Network. A µPMU model is developed utilizing a recursive Discrete Fourier Transform (DFT) algorithm combined with high-precision filters to enhance accuracy. The optimal placement of µPMUs in the distribution network is determined using a Mixed-Integer Linear Programming (MILP) approach for the IEEE 33 bus test system. The model is tested on the IEEE 33 bus system with Electric Vehicle Charging Stations (EVCS) and Distributed Generators (DGs) under both nominal and off-nominal frequencies. The results comply with the IEEE C37.118.1 standard, achieving phasor magnitude errors as low as + 0.01% and frequency accuracy of 1 mHz with minimal ripple. This study demonstrates the robustness and precision of the µPMU model, highlighting its potential for enhancing real-time monitoring and control in smart grid applications. [ABSTRACT FROM AUTHOR]

Power systems are highly complex, large‐scale engineering systems subject to many uncertainties, which makes accurate mathematical modeling challenging. This article introduces a novel centralized dynamic state estimator designed specifically for power systems where some component models are missing. Including the available dynamic evolution equations, algebraic network equations, and phasor measurements, the least squares criterion is applied to estimate all dynamic and algebraic states recursively. The approach generalizes the iterated extended Kalman filter and does not require static network observability, relying on the network topology and parameters. Furthermore, a topological criterion is established for placing phasor measurement units (PMUs), termed topological estimability, which guarantees the uniqueness of the solution. A numerical study evaluates the performance under short circuits in the network and load changes and shows superior tracking performance compared to robust procedures from the literature with computational times in accordance with the typical PMU sampling rates. [ABSTRACT FROM AUTHOR]

The fall of the public grid is a serious incident in Nuclear Power Plants (NPPs). This article explores the use of Computer-Aided Design (CAD) and Simulation to develop a Hybrid Electrical Energy Backup System (HEEBS) for the Smart Nuclear Grid (SNG) of NPPs. This SNG is controlled by open Industrial Ethernet (IE) PROFINET Protocol automation network and monitored by Phasor Measurement Unit (PMU). The HEEBS includes components such as superconducting magnetic energy storage (SMES), battery bank, super Capacitors, solar photovoltaic panels (PV), hydrogen energy system with a steam generator, SNG, IE PROFINET Protocol, and PMU. This system ensures high-quality electrical energy provision while prioritizing the nuclear and environmental safety. This research work suggests using Hydrogen Energy Storage System (HESS) Technology as a strategic Energy Storage and energy carrier System. The study advocates for the integration of renewable and nuclear energy resources into the electrical sources of NPPs, along with a new platform for the SNG. This SNG can be developed to be the base of a public smart grid. Moreover, the HEEBS can be proposed to be an essential part of the next generation, Gen. V, of the Smart Modular Reactors, SMR-V. • The proposed system, HEEBS, guarantees electricity to the nuclear reactors. • The HEEBS is proposed to be part of Gen. V. Smart Modular Reactors, SMR-V. • The integration of hybrid energy sources is a professional model for smart grids. • HEEBS achieves high quality electricity with cybersecurity to protect SMR-V. • The presented system is a successful model for the Hydrogen Economy. [ABSTRACT FROM AUTHOR]

The challenges facing active distribution networks have highlighted the position of the distribution system state estimation (DSSE) process in the distribution management systems as its most important function. Here, regarding the extensive scale of distribution networks and the weaknesses of centralized methods, the decentralized implementation of the DSSE process has received considerable attention. However, predefined network partitioning is supposed in previous works and zone size effects on the performance of the DSSE process have not been assessed. In response, a method for finding the optimal number of network zones and their size is proposed here. For this purpose, initially, an algorithm is used to partition the network into all possible configurations with different sizes. Subsequently, performance metrics affected by zone sizes, such as execution time, accuracy of the DSSE results, and reliability in achieving the results at the control centre, are modelled. Finally, by applying the decentralized DSSE method across all partitioning scenarios and calculating performance metrics, the most efficient and cost‐effective partitioning scenario can be identified. The performance of the proposed method is evaluated using the modified 77‐bus UK distribution network as an active test case, and the findings are subsequently presented and analysed. [ABSTRACT FROM AUTHOR]

This paper presents a prediction‐based algorithm designed to address out‐of‐step (OOS) conditions in power systems. The algorithm utilizes generator data obtained from phasor measurement units. The transient stability of a multi‐machine power system is evaluated using the equal‐area criterion (EAC). The proposed algorithm calculates the characteristics of the P‐δ curves within the EAC framework after a large disturbance. The critical P‐δ trace is determined by analysing the cumulative energy in the acceleration area following fault clearance. The stability margin of the rotor angle is then computed based on the actual active power and its relationship with the critical curve. The algorithm predicts the occurrence of OOS by comparing the measured active power with the corresponding value on the critical curve. Furthermore, a complementary strategy is proposed to predict the OOS condition in integrated inverter‐based power systems. The effectiveness of the proposed algorithm is validated through simulations conducted on the 73‐bus IEEE test power system. [ABSTRACT FROM AUTHOR]

Due to its vulnerability to a variety of cyber attacks, research on cyber security for power systems has become especially crucial. In order to maintain the safe and stable operation of power systems, it is worthwhile to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker's perspective. The consensus-based distributed state estimation problem is investigated for power systems subject to collaborative attacks. In order to describe such attack behaviors, the denial of service (DoS) attack model for hybrid remote terminal unit (RTU) and phasor measurement unit (PMU) measurements, and the false data injection (FDI) attack model for neighboring estimation information, are constructed. By integrating these two types of attack models, a different consensus-based distributed estimator is designed to accurately estimate the state of the power system under collaborative attacks. Then, through Lyapunov stability analysis theory, a sufficient condition is provided to ensure that the proposed distributed estimator is stable, and a suitable consensus gain matrix is devised. Finally, to confirm the viability and efficacy of the suggested algorithm, a simulation experiment on an IEEE benchmark 14-bus power system is carried out. [ABSTRACT FROM AUTHOR]

The document discusses the application of optimization principles in the planning and operation of active distribution networks (ADNs) to handle distributed generation resources like renewables, battery energy storage systems (BESSs), and electric vehicles (EVs). Various optimization techniques are used to address challenges such as voltage regulation, loss minimization, demand response, and renewable energy integration, aiming to develop efficient, flexible, and sustainable networks transitioning towards smart grids. The document also highlights the importance of multi-objective optimization approaches in balancing conflicting objectives like cost, reliability, and sustainability, making objective functions essential tools for modern energy distribution systems. [Extracted from the article]

With the phasor measurement units (PMUs) being widely utilized in power systems, a large amount of data can be stored. If transient stability assessment (TSA) method based on the deep learning model is trained by this dataset, it requires high computation cost. Furthermore, the fact that unstable cases rarely occur would lead to an imbalanced dataset. Thus, power system transient stability status prediction has the bias problem caused by the imbalance of sample size and class importance. Faced with such a problem, a TSA model based on the sample selection method is proposed in this paper. Sample selection aims to optimize the training set to speed up the training process while improving the preference of the TSA model. The typical samples which can accurately express the spatial distribution of the raw dataset are selected by the proposed method. Primarily, based on the location of training samples in the feature space, the border samples are selected by trained support vector machine (SVM), and the edge samples are selected by the assistance of the approximated tangent hyperplane of a class surface. Then, the selected samples are input to stacked sparse autoencoder (SSAE) as the final classifier. Simulation results in the IEEE 39‐bus system and the realistic regional power system of Eastern China show the high performance of the proposed method. [ABSTRACT FROM AUTHOR]

As renewable energy sources like wind and solar power increasingly replace traditional energy sources and are integrated into the power grid, the issue of insufficient system inertia is becoming more apparent. This paper presents an online adaptive time window inertia constant identification method based on ambient measurements to identify the equivalent inertia constant of the time-varying inertia at Point of Interface (POI) level. The proposed method takes advantage of the online inertia estimation and the data-driven equivalent inertia constant identification techniques to simultaneously achieve online tracking and accuracy. With this regard, this paper first describes the inertia providers in modern system. Then, based on the frequency and power data measured by the Phasor Measurement Unit (PMU), this paper provides an improved data-driven equivalent inertia constant identification method. Subsequently, the paper proposes an ambient data smoothing method to cope with the numerical errors and provides, as a byproduct, an adaptive time window inertia constant identification. The adaptive time window is designed to enhance the accuracy of the method. Finally, the feasibility and accuracy of the proposed method of tracking synthetic inertia are validated by the simulation tests based on a grid in northwest China with high renewable energy penetration and a Virtual Power Plant (VPP). The experimental results show that the accuracy of this method is within 5 % . [ABSTRACT FROM AUTHOR]

Harmonic state estimation, which highly depends on accurate synchronised phasors, is a crucial basis for the location, traceability, and governance of harmonics in power systems. However, most existing synchronous phasor measurement units (PMUs) provide power frequency phasors rather than harmonic phasors. In addition, as the dominant voltage signal provider for PMUs in power systems, capacitive voltage transformers (CVTs) have complex frequency responses and cannot be directly used for harmonic measurement. Therefore, accurate harmonic state estimation is difficult to achieve in applications. A method for harmonic voltage phasor reconstruction and harmonic state estimation based on the measurement data of a CVT is proposed. First, the reasons for harmonic voltage measurement errors were analysed. Second, the scattering parameter method was used to measure the wideband voltage transfer characteristics of CVT. Third, a voltage reconstruction method was proposed to improve the measurement accuracy and expand the frequency range of synchronous phasor measurement. Fourth, this method was applied to harmonic state estimation, and the performance of harmonic state estimation before and after voltage reconstruction was quantitatively compared. Results showed that the proposed method can remarkably improve the accuracy of harmonic state estimation. Finally, some factors, such as state observability, complex error disturbances, CVT structure, and harmonic order, influencing the performance of the proposed method were investigated. Results further demonstrated that the method has high accuracy, strong anti‐interference, and adaptability, and can provide basic information for harmonic localisation and traceability. [ABSTRACT FROM AUTHOR]

In this contribution, the time-domain harmonic state estimation is evaluated using the real-time digital simulation, Kalman filter, an optimal measurement algorithm and a physical scaled-down laboratory implementation. This methodology is implemented using MATLAB/Simulink® and runs on RT-LAB® platform in real time. The optimal placement algorithm of measurement devices is used to obtain full observability of the system. This measurement algorithm finds the optimal placement with a limited number of measurement devices, whose measurements may be contaminated with noise. The measurement model is implemented in the physical test power system. Besides, the state estimator assesses the system inputs (internal generator voltages, nonlinear electrical load variables) whose dynamics are totally unknown. The harmonic content of the waveforms is obtained through the fast Fourier transform. The estimated responses in the case studies using the RT-LAB® platform are validated through direct comparison against those obtained in the physical implementation of the electrical power system, obtaining satisfactory results. [ABSTRACT FROM AUTHOR]

This paper proposes a correction technique for bad data and high-precision analysis based on micro-phasor measurement unit (μPMU) data for a stable and reliable smart substation. First, a high-precision wide-area monitoring system (WAMS) with 35 μPMUs installed at Korea's Yeonggwang substation, which is connected to renewable energy sources (RESs), is introduced. Time-synchronized μPMU data are collected through the phasor data concentrator (PDC). A pre-processing program is implemented and utilized to integrate the raw data of each μPMU into a single comma-separated values (CSV) snapshot file based on the Timetag. After presenting the technique for identification and correction of event, duplicate, and spike bad data of μPMU, causal relationships are confirmed through the voltage and current fluctuations for a total of five states, such as T/L fault, tap-up, tap-down, generation, and generation shutdown. Additionally, the difference in active power between the T/L and the secondary side of the M.Tr is compared, and the fault ride through (FRT) regulations, when the fault in wind power generation (WP), etc., occurred, is analyzed. Finally, a statistical analysis, such as boxplot and kernel density, based on the instantaneous voltage fluctuation rate (IVFR) is conducted. As a result of the simulation evaluation, the proposed correction technique and precise analysis can accurately identify various phenomena in substations and reliably estimate causal relationships. [ABSTRACT FROM AUTHOR]

Phasor-domain (RMS) simulations have become increasingly vital in modern power system analysis, particularly as the complexity and scale of these systems have expanded with the integration of renewable energy sources. ePHASORSIM, an advanced phasor-based simulation tool developed by OPAL-RT, plays a crucial role in this context by enabling real-time phasor-domain simulation and hardware-in-the-loop testing. To keep pace with these evolving needs, continuous efforts are being made to further improve the accuracy, efficiency, and reliability of ePHASORSIM-based simulations. These efforts include automating model conversion processes for enhanced integration with ePHASORSIM, extending ePHASORSIM's simulation range with custom models, developing hybrid co-simulation techniques involving ePHASORSIM and an EMT simulator, enhancing simulation scalability, and refining HIL testing to achieve more precise validation of control and protection systems. This paper provides a comprehensive review of these recent advances. Additionally, the paper discusses the conversion of models from PowerFactory—a widely used and comprehensive modeling environment—to ePHASORSIM through both automated tools and manual methods using Excel workbooks, which has been discussed little in the literature. Furthermore, as ePHASORSIM is a relatively new tool with limited cross-validation studies, the paper aims to contribute to this area by presenting a comparative validation against DIgSILENT PowerFactory, with a specific emphasis on its application in electric vehicle charging management systems. [ABSTRACT FROM AUTHOR]

Due to the increasing demand for electricity, competitive electricity markets, and economic concerns, power systems are operating near their stability margins. As a result, power systems become more vulnerable following disturbances, particularly from a dynamic point of view. To maintain the stability of power systems, operators need to continuously monitor and analyze the grid's state. Since modern power systems are large-scale, non-linear, complex, and interconnected, it is quite challenging and computationally demanding to monitor, control, and analyze them in real time. State Estimation (SE) is one of the most effective tools available to assist operators in monitoring power systems. To enhance measurement redundancy in power systems, employing multiple measurement sources is essential for optimal monitoring. In this regard, this paper, following a brief explanation of the SE concept and its different categories, highlights the significance of Hybrid State Estimation (HSE) techniques, which combine the most used data resources in power systems, traditional Supervisory Control and Data Acquisition (SCADA) system measurements and Phasor Measurement Units (PMUs) measurements. Additionally, recommendations for future research are provided. [ABSTRACT FROM AUTHOR]

The development of microgrid automation depends on information and communication technologies, which are vulnerable to cyber-attacks. Recent advancements in MGs enhance power systems' efficacy and reliability, but cybersecurity remains a significant concern, especially with false data injection attacks (FDIAs) posing serious threats. FDIAs can compromise measurement devices and tamper with State Estimation (SE), risking the seamless operation of MGs. To address this, this paper proposes an efficient Iterative Free Detection of False Data (IFDFD) scheme for detecting FDIAs in microgrid state estimation. The IFDFD scheme uses complex Micro Phasor Measurement Unit (μPMU) measurements and computes nodal power injections to detect FDIAs. Furthermore, the proposed scheme integrates an S-Estimator to eliminate noise errors caused by environmental factors and the component lifespan, making IFDFD robust against sophisticated attackers. The proposed IFDFD scheme has been tested and validated on the modified IEEE 14 bus test system, integrating Distributed Generations (DGs). False data was injected into the measurements to test the scheme's effectiveness. The efficacy of proposed IFDFD scheme has been validated by comparing it to existing method of FDIAs. The obtained result clearly validates the efficacy of the proposed IFDFD scheme. • Addresses cybersecurity concerns in MG automation dependent on Information and Communication Technology. • Proposed IFDFD scheme detects FDIAs using μPMU measurements and nodal power injections. • IFDFD integrates S-Estimator to eliminate noise errors from environmental factors. [ABSTRACT FROM AUTHOR]

Hybrid estimation architectures make it possible to combine the widespread availability of conventional SCADA data with the improved accuracy and higher sampling rates of synchronized phasor measurements. A particularly appealing hybrid scheme is the so-called APSI architecture, which comprises two sequential estimation stages: a conventional SCADA-based state estimator, whose outcome is then treated as a priori information by a second stage, where available phasor measurements are cumulatively processed. As desirable features, this architecture preserves existing SCADA-based estimators and waives PMU-observability requirements. On the other hand, bad data processing for APSI estimators is a topic that still deserves additional research efforts. In this paper, we set out to improve the resilience of APSI estimators against bad data occurring both within synchrophasor measurements and SCADA data. The proposed strategy comprises two distinct actions: (a) replacing conventional algorithms in the second APSI stage by a more robust one, capable of automatic suppressing outliers in both data classes, and (b) adopting a method for tracing down SCADA gross measurements from a priori data previously identified as erroneous. The performance of the proposed methodology is assessed through tests conducted on the IEEE 14-bus, 57-bus and 118-bus test systems. [ABSTRACT FROM AUTHOR]

With the growing concern over climate change and energy security, the Government of India expedited enhancing the share of renewable energy (RE) derived from solar, wind and biomass sources within the energy blend. In this paper, a techno-economic and environmental analysis of a microgrid-integrated electric vehicle charging stations fueled by renewable energy is proposed for a typical area in the State of Karnataka, South India. The power transaction with the grid and the sell-back price to the national grid were investigated. Carbon emissions were also assessed, and 128,406 CO2 kg/Yr can be saved in the grid-connected mode. Also, in this work, different scenarios such as injecting active power, reactive power, and active and reactive power, and injecting active and absorbing reactive power to the grid are comprehensively assessed. Out of four types, type 3 (inject real and reactive power) provides significant reduction in power losses by up to 80.99%. The synchrophasor-technology-based monitoring method is adopted in order to enhance the microgrid system's overall performance. The execution times for different cases with distributed generators (DGs) and electric vehicle charging stations (EVCSs) for conventional systems and micro-phasor measurement units (µPMU) were observed to be 19.07 s and 5.64 s, respectively, which is well accepted in the case of online monitoring. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a prediction‐based algorithm designed to address out‐of‐step (OOS) conditions in power systems. The algorithm utilizes generator data obtained from phasor measurement units. The transient stability of a multi‐machine power system is evaluated using the equal‐area criterion (EAC). The proposed algorithm calculates the characteristics of the P‐δ curves within the EAC framework after a large disturbance. The critical P‐δ trace is determined by analysing the cumulative energy in the acceleration area following fault clearance. The stability margin of the rotor angle is then computed based on the actual active power and its relationship with the critical curve. The algorithm predicts the occurrence of OOS by comparing the measured active power with the corresponding value on the critical curve. Furthermore, a complementary strategy is proposed to predict the OOS condition in integrated inverter‐based power systems. The effectiveness of the proposed algorithm is validated through simulations conducted on the 73‐bus IEEE test power system.

Abstract The demand for electric power has consistently been on the rise, owing to urbanisation and technological improvements. On the generation side, renewable sources have been favoured over their polluting, exhaustible, non‐renewable counterparts. These changes in the power system have necessitated a system for maintaining the supply‐demand balance, to maintain system stability. A complex power system is also more prone to blackouts and grid failure. Islanding helps in provision of supply to consumers in a microgrid, reducing the possibility of a blackout. Depending on the power demand and generation, loads need to be shed or restored to mitigate power imbalances. A wide area distribution management system (WADMS) is proposed to dynamically shed and restore loads in the islanded mode, with the aid of micro phasor measurement units (µPMUs). A priority and consumer‐based load shedding and restoration (PCLS) algorithm is proposed in the WADMS that preferentially sheds or restores loads based on their assigned load priority indices and number of consumers. The algorithm has been tested on a modified IEEE 13 bus system, incorporated with a solar photovoltaic (PV) system, diesel generators (DGs) and an energy storage system (ESS) in MATLAB Simulink.

Aiming at the noise that may exist in the data collection and transmission of power system synchronous phasor measurement unit (PMU), as well as the imbalance between stable and unstable samples, making the model likely to tilt to most kinds of samples, thus affecting the evaluation results of power system transient stability model, a transient stability assessment method integrating Convolutional Block Attention Module (CBAM) and deep residual shrinkage network is proposed. First, the mapping relationship between the input values and the labels of the stability results is established, and the electrical quantities are transformed into the form of convolutional feature maps to be input into the model. Second, channel attention module and spatial attention module are introduced, and soft threshold function is used to automatically learn the noise threshold and reduce the interference of the noise. As the model tends to favor the majority of the samples during training, gradient harmonized mechanism (GHM) loss function is introduced to improve the attention of the minority of samples as well as the training effect and evaluation performance of the model. Finally, the model is tested by simulation on the IEEE39 node system. [ABSTRACT FROM AUTHOR]

An online non‐model‐based procedure is presented for estimating the synchronous generator (SG) dynamic parameters using practical phasor measurement unit (PMU) signals in the presence of uncertainty and noisy data. For this purpose, considering 8th‐order approximation, the SGs model is estimated in which, based on evaluating voltage and current phasors achieved from PMU data, dynamic parameters are estimated online. The proposed approach is a generalised concept of the Heffron–Philips model in which the variables and the gain factors are adaptable according to operating conditions. The proposed scheme is an online and non‐model‐based method in which the SG magnetic saturation behaviours are modelled through multivariable non‐linear definition to extend the accurate controlling structure. In this case, two different studies are carried out. In the first study, considering a single SG is connected to the infinite bus, the ability of the proposed method through simulation studies is evaluated. In the second study, the proposed scheme is developed practically in the laboratory whereby performing the experimental structure on different types through real‐time working mode, validation of the proposed estimated model through different operating points is evaluated. Experimental results show the effectiveness of the proposed practical scheme for estimating the generator's detailed model and non‐linear dynamic parameters through real‐time evaluations. [ABSTRACT FROM AUTHOR]

The optimal functioning of the power system is crucially dependent upon the sound protection of its major stakeholder, i.e., the transmission line, as it is prone to fault. To maintain the integrity of the power system and protect costly power system equipment, protective relaying is necessary to provide a steady and affordable supply of electricity. Relays recognize, classify, and identify transmission line faults using input signals of voltage and current. Many artificial intelligent methods based on Expert Systems, Artificial Neural Networks, Fuzzy Logic, Support Vector Machines, Wavelet-based systems, and deep learning techniques are being investigated to improve modern digital relays' consistency, speed, and accuracy. This paper is a comprehensive and all-inclusive survey that reviews and incorporates Phasor Measurement Unit (PMU) and Global Positioning System (GPS) approaches together with all of these intelligent transmission line safety strategies and concepts. Initial investigators will benefit from this study by being able to examine, evaluate, and analyze a variety of approaches with references for all relevant contributions. [ABSTRACT FROM AUTHOR]

The increase in renewable energy sources (RESs) in power systems is causing significant changes in their dynamic behavior. To ensure the safe operation of these systems, it is necessary to develop new methods for preserving transient stability that follow the new system dynamics. Fast-response devices such as flexible AC transmission systems (FACTSs) can improve the dynamic response of power systems. One of the most frequently used FACTS devices is the Static Var Compensator (SVC), which can improve a system's transient stability with a proper control strategy. This paper presents a reactive power control strategy for an SVC using synchronized voltage phasor measurements and particle swarm optimization (PSO) to improve the transient stability of a multimachine power system. The PSO algorithm is based on the sensitivity analysis of bus voltage amplitudes and angles to the reactive power of the SVC. It determines the SVC reactive power required for damping active power oscillations of synchronous generators in fault conditions. The sensitivity coefficients can be determined in advance for the characteristic switching conditions of the influential part of the transmission network, and with the application of the PSO algorithm, enable quick and efficient finding of a satisfactory solution. This relatively simple and fast algorithm can be applied in real time. The proposed control strategy is tested on the IEEE 14-bus system using DIgSILENT PowerFactory. The simulation results show that an SVC with the proposed control strategy effectively minimizes the rotor angle oscillations of generators after large disturbances. [ABSTRACT FROM AUTHOR]

The demand for electric power has consistently been on the rise, owing to urbanisation and technological improvements. On the generation side, renewable sources have been favoured over their polluting, exhaustible, non‐renewable counterparts. These changes in the power system have necessitated a system for maintaining the supply‐demand balance, to maintain system stability. A complex power system is also more prone to blackouts and grid failure. Islanding helps in provision of supply to consumers in a microgrid, reducing the possibility of a blackout. Depending on the power demand and generation, loads need to be shed or restored to mitigate power imbalances. A wide area distribution management system (WADMS) is proposed to dynamically shed and restore loads in the islanded mode, with the aid of micro phasor measurement units (µPMUs). A priority and consumer‐based load shedding and restoration (PCLS) algorithm is proposed in the WADMS that preferentially sheds or restores loads based on their assigned load priority indices and number of consumers. The algorithm has been tested on a modified IEEE 13 bus system, incorporated with a solar photovoltaic (PV) system, diesel generators (DGs) and an energy storage system (ESS) in MATLAB Simulink. [ABSTRACT FROM AUTHOR]

Subsynchronous oscillation (SSO) is the resonance between a new energy generator set and a weak power grid, and the resonance frequency is usually the sub-/super-synchronous frequency. The eigensystem realization algorithm (ERA) is a classic algorithm for extracting modal parameters based on matrix decomposition. By leveraging the ERA's simplicity and low computational cost, an enhanced methodology for identifying the key parameters of SSO is introduced. The enhanced algorithm realizes SSO angular frequency extraction by constructing an angular frequency fitting equation, enabling efficient identification of SSO parameters using only a 200 ms synchrophasor sequence. In the process of identification, the fitting-based ERA effectively addresses the limitation of the existing ERA. The accuracy of SSO parameter identification is improved, thereby realizing that SSO parameter identification can be carried out using a 200 ms data window. The fitting-based ERA is verified using synthetic and actual data from synchrophasor measurement terminals. The research results show that the proposed algorithm can accurately extract fundamental and subsynchronous or supersynchronous oscillation parameters, effectively realizing dynamic real-time monitoring of subsynchronous oscillations. [ABSTRACT FROM AUTHOR]

The reliable operation of power systems while integrating renewable energy systems depends on Optimal Power Flow (OPF). Power systems meet the operational demands by efficiently managing the OPF. Identifying the optimal solution for the OPF problem is essential to ensure voltage stability, and minimize power loss and fuel cost when the power system is integrated with renewable energy resources. The traditional procedure to find the optimal solution utilizes nature-inspired metaheuristic optimization algorithms which exhibit performance drop in terms of high convergence rate and local optimal solution while handling uncertainties and nonlinearities in Hybrid Renewable Energy Systems (HRES). Thus, a novel hybrid model is presented in this research work using Deep Reinforcement Learning (DRL) with Quantum Inspired Genetic Algorithm (DRL-QIGA). The DRL in the proposed model effectively combines the proximal policy network to optimize power generation in real-time. The ability to learn and adapt to the changes in a real-time environment makes DRL to be suitable for the proposed model. Meanwhile, the QIGA enhances the global search process through the quantum computing principle, and this improves the exploitation and exploration features while searching for optimal solutions for the OPF problem. The proposed model experimental evaluation utilizes a modified IEEE 30-bus system to validate the performance. Comparative analysis demonstrates the proposed model's better performance in terms of reduced fuel cost of $620.45, minimized power loss of 1.85 MW, and voltage deviation of 0.065 compared with traditional optimization algorithms. [ABSTRACT FROM AUTHOR]

From time to time, a series of unpredictable and conflicting contingencies can lead to angular instability of the power system and even blackouts if not adequately handled by an out-of-step (OOS) protection system. The key contribution of this research work, to the theory of out-of-step protection, is the identification and isolation after a given disruption of many unstable swings. This paper presents a proposed method to avoid false operation for distance function by out-of-step blocking to improve the system stability by using optimally placed PMUs for the fast detection of system analogue quantities. The studies were performed on a modified Eskom transmission network in the Western Cape with 765 kV and 400 kV voltage levels. The aim is to investigate the IEC 61850-90-5 standard for predictive dynamic stability maintaining systems using PMUs for out-of-step conditions of synchronous generators. The power system modelling and simulation are performed in the RSCAD-FX for the proposed multi-area power system network. An experimental lab-scale implementation is built to test the proposed out-of-step algorithm in a real-time digital simulator. Software-based PMU is incorporated to test and validate the IEC 61850-90-5 standard sampled values. Simulation and experimental results are presented. [ABSTRACT FROM AUTHOR]

Recent advances in distribution networks, driven by the integration of renewable energy sources, have spurred the emergence of microgrids, elevating concerns regarded reliability and stability. In this context, precise monitoring of events, particularly those elusive to detection like high-impedance faults (HIFs), becomes imperative. The development of phasor measurement units (PMUs) with their harmonic synchronized measurements has enhanced the monitoring task and fostering the application of synchrophasors even on microgrids. This work introduces a novel method for event classification in microgrids, utilizing combined low-rate PMU data and harmonic synchrophasor time series. Central to our approach is the usage of a state-of-the-art transformer neural network, based on the attention mechanism, to effectively discern HIFs from other faulty and non-fault events. Notably, this methodology accounts for prevalent PMU data quality issues, including noise, missing data, and synchronism errors. Results from real-world HIF data demonstrate a robust performance, with an accuracy rate of approximately 98% in event classification. This harmonic synchrophasor-based strategy showcases promise as an original approach for handling commercial PMU data, offering sufficient robustness for deployment in real-world applications. [ABSTRACT FROM AUTHOR]

The hybrid AC/DC grid, based on a significant share of renewable energy sources, is gradually becoming an essential aspect of the modern energy system. The integration of intermittent renewable generators into contemporary energy systems is accompanied by the decommissioning of power plants containing synchronous generators. Consequently, this leads to a reduction in system inertia and an increase in the risk of stability disruption. The abrupt disconnection of the primary generator or power line can result in an unanticipated mismatch between power generation and consumption. This discrepancy can trigger substantial and swiftly evolving alterations in power distribution, angular speed, load flow, and the frequency of generators. The risks of an energy system collapse can be mitigated through automation, enabling rapid adjustments to generation and load capacities, as well as power flows, in the electrical network. This article justifies the utilisation of a power control method for high-voltage power line interconnections. The technology of hydro storage power plants and measurements of voltage phasors are employed. The potential for easing power flow restrictions and realising substantial economic benefits is supported by the results obtained using simplified dynamic model of the Baltic power system and Nord Pool electricity market model. [ABSTRACT FROM AUTHOR]

The healthy operation of a microgrid is a more challenging task due to the inclusion of various distribution generation (DG) sources. The incorporation of a grid with various distributed sources alters the radial structure of the network, making it challenging to manage this type of microgrid system, particularly in the event of a fault. A new protection scheme is designed to operate the microgrid and detect faults. This scheme is designed for detecting internal faults as well as external faults. The proposed scheme also determines the location of the fault. The proposed technique utilizes the cumulative sum of the differential positive-sequence impedance angle (DPSIA) as a constraint for fault detection, triggering the trip signal. The proposed scheme is based on the phasor measurement unit, which helps to calculate the positive sequence impedance angle. The setup, comprising 15 buses, is formulated using the real-time structured computer-aided design (RSCAD) platform in conjunction with the real-time digital simulator (RTDS). Phasor Measurement Units (PMUs) compliant with the IEEE C37.118.1 standard are strategically placed at key locations to accurately and reliably measure phasor quantities. This ensures the precise detection of faulted conditions with a high level of accuracy. The pre-fault and postfault phasor components of voltage and current are assessed in proximity to the bus nearest to the fault location. The simulation results clearly indicate waveforms for internal and external faults at different locations of the feeder. [ABSTRACT FROM AUTHOR]

Monitoring and controlling of transmission lines become easier in real time with synchrophasor measurements technology. Phasor Measurement Units (PMUs) provide the time-stamped synchrophasor data of the system buses and transmission lines with a high reporting rate. In this paper, a mathematical model is implemented that allows the simulation of the characteristics of the spread of transmission line switching with load variations. In the model, the transmission lines are divided into three parts defined as susceptible, infected and recovered transmission lines inspired from epidemic model. In this paper, Reliability Test System (RTS − 1979) is divided into three zones to simulate the line switching spread behaviour with load variation for reliability and vulnerability analysis of the synchrophasor smart grid network. The anomaly exists with synchrophasor measuring devices due to cyber intrusion in communication network or failure of devices makes the system observable with the characterization of vulnerable behaviour of transmission lines. [ABSTRACT FROM AUTHOR]

Accurate parameters are crucial in modern energy systems to ensure the reliable operation of all components. Given the substantial volume of data in monitored systems, high-performance methods are necessary. This paper proposes a new Bayesian multi-output regressor for estimating the parameters of a three-phase transmission line. The presented approach achieves acceptable accuracy in parameter estimation using only one end of the line. The Bayesian regressor is developed using information derived from the data themselves, eliminating the need to explicitly model the system. This capability allows the method to estimate parameters while accommodating different noise models, even in the presence of systematic errors and non-Gaussian random noise. The methodology was validated on various systems, including a two-bus system, IEEE 14-bus, IEEE 39-bus, and IEEE 118-bus, under diverse conditions such as varying sample sizes, loads, and noise levels. These tests demonstrate the robustness of the proposed approach. [ABSTRACT FROM AUTHOR]

A smart grid protection system based on phasor information at circuit breakers is proposed in this paper. Phasor data for fault diagnosis is obtained from phasor measurement units or intelligent electronic devices installed near circuit breakers, without the need for additional measurement equipment. When a fault occurs, the protection system first locates the core protection circuit breaker closest to the fault point. It then identifies the faulty component by analyzing the current phasor differences between this breaker and adjacent ones. This scheme offers high computational efficiency, enabling rapid identification of faulty equipment and its precise location. The proposed protection system structure can be divided into two types: centralized and decentralized, and the most suitable scheme should be selected based on the actual situation. Finally, the feasibility of the proposed scheme was verified through the IEEE 39-bus system and a simulated actual area power grid model, demonstrating its applicability to practical smart grid scenarios. The simulation network validates the implementation method of the proposed protection strategy. [ABSTRACT FROM AUTHOR]

The development of digital techniques in control engineering leads to the creation of innovative algorithms for measuring specific parameters. In the field of electric power engineering these parameters may be amplitude, phase and frequency of voltage or current occurring in the analyzed electric grid. Thus, the algorithms mentioned, applied in relation to the quoted parameters, may provide precise and reliable measurement results in the electric grid as well as ensure better grid monitoring and security. Signal analysis regarding its identification due to the type of interference is very difficult because the multitude of information obtained is very large. In order to indicate the best method for determining errors in measuring synchronous parameters of the measured current or voltage waveforms, the authors propose in this paper a new form of one error for all testing functions, which is called an equivalent error. This error is determined for each error's value defined in the applicable standards for each of selected 15 methods. The use of the equivalent error algorithm is very helpful in identifying a group of methods whose operation is satisfactory in terms of measurement accuracy for various types of disturbances (both in the steady state and in the dynamic state) that may occur in the power grid. The results are analyzed for phasor measurement unit (PMU) devices of class P (protection) and M (measurement). [ABSTRACT FROM AUTHOR]

This manuscript proposes a hybrid technique for determining the optimal positioning of phasor measurement units (PMUs) in power systems. The PMUs play a crucial role in power system control, wide-area monitoring, and protection. The proposed hybrid method is the joint execution of the Lichtenberg algorithm (LA) and the heap-based optimization (HBO) technique. Hence, it is named the LA-HBO technique. The objective of the proposed method is to place the PMUs in the power system for observability. The goal is to enhance the efficiency and accuracy of PMU placement, ensuring optimal positioning for improved grid monitoring capabilities. The Lichtenberg Algorithm (LA) enhances PMU placement by addressing system observability challenges and ensuring that selected locations provide comprehensive coverage of the power grid. The heap-based approach optimizes PMU placement by efficiently managing the selection process, considering factors like computational efficiency and scalability. The proposed hybrid technique is implemented in IEEE-30 and -14 bus systems. The MATLAB-based simulation results are compared with the various existing methods, such as Sea Lion Optimization (SLO), Particle Swarm Optimization (PSO), and Ant Bee Colony Optimization (ABC). By then, the outcome reveals the efficacy of the proposed method for defining the optimum PMU locations. The proposed method shows a low computational time of 0.02348 sec for the IEEE-14 bus, and 0.03565 sec for the IEEE-30 bus compared with other existing methods. [ABSTRACT FROM AUTHOR]

This paper introduces a reinforcement learningbased controller, utilising the deep deterministic policy gradient (DDPG) method, to mitigate low-frequency disturbances in electrical grids with renewable energy sources. It features a novel reward function inversely related to the control error and employs a state vector comprising absolute and integral errors to enhance error reduction. The controller, tested on a dualregion system with solar power, utilises phasor measurement unit (PMU) data for global inputs. Its performance is validated through time-domain simulations, pole-zero mapping, modal analysis, frequency response, and participation factor mapping, using a custom MATLAB and Simulink toolkit. The design accounts for communication delays and adapts to variable conditions, which proves to be effective in reducing oscillations and improving system stability. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy increases to a large scale and power electronic devices become widespread, power systems are becoming prone to synchronous oscillations (SO). This event has a major impact on the stability of the power grid. The recent research has been mainly concentrated on identifying the parameters of sub-synchronous oscillation. Sub/Super synchronous oscillations (Sub/Sup-SO) simultaneously occur, increasing the difficulty in accurately identify the parameters of SO. This work presents a novel method for parameter identification that effectively handles the Sub/Sup-SO components by utilizing the Rife-Vincent window and discrete Fourier transform (DFT) simultaneously. To mitigate the impact of spectral leakage and the fence effect of DFT, we integrate the tri-spectral interpolation algorithm with the Rife–Vincent window. We use the instantaneous data of the phasor measurement unit (PMU) to identify Sub/Sup-SO-related parameters (Sub/Sup-SO damping ratio, frequency, amplitude and phase). First, the spectrum of the Sub/Sup-SO signals is analyzed after incorporating the Rife-Vincent window, and the characteristics of the Sub/Sup-SO signal are determined. Then, the signal spectrum is identified using a three-point interpolation algorithm, and the damping ratio, amplitude, frequency, and phase of the Sub/Sup-SO signals are obtained. In addition, we consider the identification accuracy of the algorithm under various complex conditions, such as the effect of Sub/Sup-SO parameter variations on parameter identification in the presence of a non-nominal frequency and noise. The proposed algorithm accurately identifies the parameters of multiple Sub/Sup-SO components and two Sub-SO components that are in close proximity. Testing with synthetic and real data demonstrates that the proposed algorithm outperforms existing methods in terms of identification accuracy, identification bandwidth, and adaptability. • Sub/Sup-SO parameter identification is proposed via tri-spectral line interpolation. • This method obtains wide identification band range and high measurement accuracy. • Parameter identification is proposed for parameters containing Sub-SO and Sup-SO frequencies. • The deviation of parameter identification was less than 1 % in most cases. [ABSTRACT FROM AUTHOR]

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Abstract Modern power systems have several measurement devices for reliable, safe, and improved operation and control. Due to the high infiltration of inverter‐based resources, protecting modern power systems has become challenging. This paper proposes a new protection scheme based on the computation of ‘discrepant impedance’ using real‐time protocol services for data exchange. The sampled values of voltages and currents are transmitted to a central processing unit to implement the protection scheme. With the development of digital instrument transformers (IEC 61869‐9), the current and voltage samples are directly interfaced wired or wirelessly to the IEC 61850‐9‐2 process bus. The proposed protection scheme is validated with grid forming and grid following inverters on Consortium for Electric Reliability Technology Solutions (CERTS) microgrid network using a real‐time digital simulator. The protection scheme is independent of the type of energy sources and their control philosophy, operation modes and topology of the microgrid network.

In this paper, a new method based on the Tufts–Kumaresan (TK) algorithm is used to process nonstationary signals and extract system modes. This new algorithm for mode estimation is proposed to improve estimation accuracy under dynamic conditions. The key contribution in TK is the use of multiple orthogonal sliding windows rather than a single pair of sliding windows. Simulation results under various scenarios, such as different types of faults and load changes are used to evaluate the performance of the proposed method. The proposed method will accurately extract system modes. This method will also reduce the required memory and the calculation time of estimation in nonstationary signals. In complement to successful applications in studying the dynamic behavior of the power system, identifying and analyzing low-frequency electromechanical oscillations, and removing signal noise, this method can accurately estimate the modes of a power system. To validate the accuracy of the proposed method, the Wavelet and the Prony methods have been used for comparison. The proposed method is implemented using simulated ringdown data of standard two-area power system and real measurement data of the WSCC system breakup on 10 August 1996. [ABSTRACT FROM AUTHOR]

This paper introduces a novel hybrid filtering algorithm that leverages the advantages of Phasor Measurement Units (PMU) to address state estimation challenges in power systems. The primary objective is to integrate the benefits of PMU measurements into the design of traditional power system dynamic estimators. It is noteworthy that PMUs and Supervisory Control and Data Acquisition (SCADA) systems typically operate at different sampling rates in power system estimation, necessitating synchronization during the filtering process. To address this issue, the paper employs a predictive interpolation method for SCADA measurements within the framework of the Extended Kalman Filter (EKF) algorithm. This approach achieves more accurate estimates, closer to real observation data, by averaging the KL distribution. The algorithm is particularly well-suited for state estimation tasks in power systems that combine traditional and PMU measurements. Extensive simulations were conducted on the IEEE-14 and IEEE-30 test systems, and the results demonstrate that the fused estimator outperforms individual estimators in terms of estimation accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic-phasor-based, average-value modeling method for power systems with extensive converter-tied subsystems. In the proposed approach, the overall system model is constructed using modular functions, interfacing both conventional and converter-tied resources. Model validation is performed against detailed Electro-Magnetic Transient (EMT) simulations. The analytical capabilities offered by the proposed modeling method are demonstrated on a modified IEEE 9-bus system. A Graphics Processing Unit (GPU)-based parallel computing approach for the solution of the resulting model is presented and exemplified on a modified IEEE 118-bus system, showing significant improvements in computing efficiency over EMT solvers. A co-simulation approach using a Central Processing Unit (CPU) and a GPU is also presented and exemplified using a modified version of the IEEE 118-bus system, demonstrating the model's parallelization. [ABSTRACT FROM AUTHOR]

This work presents a model for solving the Economic-Environmental Dispatch (EED) challenge, which addresses the integration of thermal, renewable energy schemes, and natural gas (NG) units, that consider both toxin emission and fuel costs as its primary objectives. Three cases are examined using the IEEE 30-bus system, where thermal units (TUs) are replaced with NGs to minimize toxin emissions and fuel costs. The system constraints include equality and inequality conditions. A detailed modeling of NGs is performed, which also incorporates the pressure pipelines and the flow velocity of gas as procedure limitations. To obtain Pareto optimal solutions for fuel costs and emissions, three optimization algorithms, namely Fractional-Order Fish Migration Optimization (FOFMO), Coati Optimization Algorithm (COA), and Non-Dominated Sorting Genetic Algorithm (NSGA-II) are employed. Three cases are investigated to validate the effectiveness of the proposed model when applied to the IEEE 30-bus system with the integration of renewable energy sources (RESs) and natural gas units. The results from Case III, where NGs are installed in place of two thermal units (TUs), demonstrate that the economic dispatching approach presented in this study significantly reduces emission levels to 0.4232 t/h and achieves a lower fuel cost of 796.478 USD/MWh. Furthermore, the findings indicate that FOFMO outperforms COA and NSGA-II in effectively addressing the EED problem. [ABSTRACT FROM AUTHOR]

Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events, causing surveillance and energy loss. This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters. Initially, the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes. Subsequently, it delves into communication and data processing mechanisms, specifically focusing on central data processing (CDP), communication routers (CRs), and phasor measurement units (PMUs), to re-establish an equipment recovery model based on these data transmission methodologies. Furthermore, it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution. The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system. The findings suggest that the proposed branchand-bound algorithm significantly augments the observational capabilities of a power system with limited resources, thereby bolstering its stability and emergency response mechanisms. [ABSTRACT FROM AUTHOR]

The growing presence of monitoring systems based on phasor measurement units (PMU) in distribution systems has strengthened the development of data-based solutions. The correct identification of events in distribution networks is strictly benefited from large data availability, especially those challenging to detect, such as high-impedance faults (HIFs). In this sense, this work presents the usage of harmonic synchrophasors on HIF classification in active distribution networks. A total of 2800 simulated events from a microgrid are considered, and six classic machine learning classification models are evaluated. The performed analyses focus on establishing how the amount and quality of the PMU data can influence the distinction of common microgrid events. Results show that low-resolution PMU data, like one phasor estimated per cycle, is sufficient to discriminate the events accurately. Regarding synchrophasors, the classification corroborates that odd harmonic contents present better discriminant potential when compared to even, and the usage of angle information subtly enhances outcomes. When dealing with noisy data, the classification stays stable for mild scenarios of signal-to-noise ratio. By validating the classification task with real HIF data, 99% of the events are correctly classified. This harmonic synchrophasor-based strategy is a promising and original approach for commercial PMU data, and its robustness is adequate for distribution-level applications. [ABSTRACT FROM AUTHOR]

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]