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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]

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]

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]

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]

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.

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.

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]

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]

Bidirectional power flow based smart grid system is implemented in the Distributed Generation (DG) sources using Renewable Energy Generators (REG) like solar, wind, etc. Moreover, the unsuitable connection of a load to a grid and DGs can reduce Power Quality (PQ) and bidirectional power flow. Consequently, the existing power generation system has a limited number of power-generation sources, which are linked as millions of end consumers as well as transmission grid. Also, power generation sources has small controller performance over power loss from producing plants to end customers. Moreover, the power-generation sources are having injection points to transfer the power. In the current and future conditions, power systems must accommodate more power from renewable energy sources, be capable of handling bidirectional power flow with distributed generation, and use automatic metering infrastructure, phasor measurement units, power quality conditioners, electric vehicle charging infrastructure, cyber security, and so on.In recent times, the renewable energy-based distribution system is a challenging task in the Battery Energy Storage System (BESS). However, in many cases, power loss, and harmonic moderation are significant issues. Hence in this research, a novel Intelligent Neural Herd Based Water Drop Optimization (INH-WDO) is proposed to control the bidirectional power flow of the converter. The simulation of these proposed methods is actualized in MATLAB platform; subsequently, the projected performance results such as power loss (0.2MV), THD (2.5%) are compared with existing control techniques for proving the significance of the developed controller design. Moreover, the system efficiency in terms of power quality is based on power loss minimization while reducing the Total Harmonic Distortion (THD). [ABSTRACT FROM AUTHOR]

Due to the increasing share of inverter‐based renewable energy sources and the increased retrofitting of conventional hydropower plants with power electronics, novel possibilities arise for providing damping power in case of occurring inter‐area oscillations in power systems. This paper presents a complete model of a variable‐speed hydropower plant (VSHP) in a single‐machine infinite bus system. The demonstrated analytical model is applied for small‐signal stability analysis that contains all relevant converter dynamics and is verified by an additional non‐linear time‐domain simulation. The damping controller approach located in the full‐size converter (FSC) consists of the classical power system stabilizer with additional phase compensation, to which an appropriate wide‐area input signal is fed via a phasor measurement unit device. Subsequently, the FSC‐VSHP with additional damping algorithm is examined with respect to the significantly higher damping performance with regard to inter‐area modes in a three‐machine model. [ABSTRACT FROM AUTHOR]

When a grid-forming (GFM) inverter is connected to a low- or medium-voltage weak power grid, the line impedance with resistive and inductive characteristics will cause power coupling. Typical GFM decoupling control strategies are designed under nominal line impedance parameters. However, there are deviations between the nominal line impedance and actual parameters, resulting in poor decoupling effects. Aiming at this problem, this paper proposes a power decoupling strategy based on a reduced-order extended state observer (RESO). Firstly, the power dynamic model of the GFM is established based on the dynamic phasor method. Then, the model deviation and power coupling due to line impedance parameter perturbation are estimated as internal disturbances of the system, and the disturbances are compensated on the basis of typical power control strategy and virtual impedance decoupling. Good decoupling performance is obtained under different impedance parameters, improving the control strategy's robustness. Finally, the effectiveness of the proposed method is verified by the results of RT Box hardware-in-the-loop experiments. [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]

Renewable energy resources have increasingly been integrated into electric power grids. Moreover, new challenges constantly arise, showing the need for modernization in monitoring, control, and protection. Transmission and distribution systems have explored the use of phasor measurement units (PMUs) in an attempt to meet these requirements. The decreasing cost of hardware components of PMUs, similar to what occurred with digital relays, is an important factor in terms of their worldwide use in different applications. Low-cost variations of PMUs focused on distribution systems, such as micro- and D-PMUs, are growing in number. Thus, this survey article presents significant and recent research conducted in the field of power systems using PMUs. Firstly, it briefly describes the characteristics of PMUs concerning hardware and phasor estimation algorithms. Afterward, recent studies related to transmission and distribution system protection based on PMUs are presented, such as fault detection and location, stability, and wide-area protection. Finally, the article also provided important guidelines, which certainly can be useful for future research in the years to come. [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]

Distribution system voltage profile management at each bus and fault detection and classification are often challenged by complex and changing network configurations. The distribution system voltage profile improvement issue is addressed by placing distributed generation (DG) units at different locations in the network. By placing the DG units at appropriate places in IEEE 33 bus radial distribution networks by a proposed reinforcement learning (RL) algorithm, the voltage profile of each node is improved and power loss in the network is minimized. There is a 69% reduction in active power losses compared to losses without DG. Furthermore, an innovative method for fault detection and classification is developed that uses a convolutional neural network (CNN) cascaded with a long short-term memory network (LSTM) and attention mechanisms (AMs). To extract dynamic information from the data, phasor measurement units (PMUs) placed on different buses are used as input for the CNN architecture. AM strengthens important information. A mapping weight and parameter learning approach allows AM to assign different weights to concentrate on LSTM characteristics and improve learning accuracy. Low and high impedance faults are tested as well as various non-faulty events. The scheme's performance is compared with that of other deep learning techniques through reliability analysis, and the time taken for fault detection (FD) is also determined. [ABSTRACT FROM AUTHOR]

This paper proposes a synchrophasor measurement-assisted integrated scheme to enhance power grid stability via transient stability assessment and emergency control of power systems in real-time. The synchrophasor measurements are time-stamped network information available from Phasor Measurement Units (PMUs). The Transient Stability Assessment (TSA) is based on measured generator bus angles obtained directly by PMUs at the control center. These synchronized angle measurements are utilized to construct features, and a Random Forest classifier is implemented to infer the TSA in real-time. The TSA results are determined within the first three cycles following the fault clearance. For operating scenarios that are unstable, an effective emergency control scheme is developed to avoid system degradation. The control strategy utilizes wide-area measurements to formulate a proportional sharing principle-based methodology in real-time. The composite scheme estimates the location and magnitude of the emergency measures to enhance grid stability. The performance of the proposed composite scheme is tested on an IEEE 39-bus test system. The results highlight that the scheme is computationally efficient and robust to topological changes. [ABSTRACT FROM AUTHOR]

Managing the output of renewable energy sources considering their uncertainty and variability is crucial for resilience in power system operation. In addition, analyzing stability issues that may arise at the maximum output is important to ensure power system stability. Therefore, the authors propose a method for estimating the maximum non‐synchronous generation (Max NSG) of renewable energy based on the minimum inertia of the power system. The minimum inertia is determined through the correlation between the available and required quantity of inertia and governor resources, satisfying the frequency standards in a South Korean power system. The Max NSG of renewable energy sources at that system inertia level is estimated based on the derived minimum inertia. The proposed method was applied to 22,612 operation data extracted from the Korea energy management system (K‐EMS). The authors estimated a linear relationship between demand levels and Max NSG, ranging from 52.6 to 3.83 GW. The study shows that Max NSG, which is difficult to estimate in many power system operating conditions, can be estimated based on minimum inertia considering the frequency stability in South Korean power systems. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms are increasingly used in power systems applications. One important application is the classification and localization of various types of transmission line faults. Using voltage and current measurements from phasor measurement units (PMUs), a number of useful features can be extracted, which can form the basis of a ML-based prediction of the fault type, line, and distance on the line. This paper proposes a technique to find the optimal number and placement of PMUs by performing thorough feature selection. The features are selected to maximize the accuracy of the ML classification and regression algorithms. The results show that for the IEEE 14 bus system, the use of only five PMUs is sufficient to obtain high levels of accuracy. For example, a testing accuracy of 99.0% and 97.1% can be achieved for the fault type and fault line location, respectively. As for the fault distance along the line, the testing MAE of 3.1% can be obtained along with an R 2 score of 94.4%. Adding more PMUs does not provide any additional value in terms of accuracy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

State estimation (SE) is an important energy management system application for power system operations. Linear state estimation (LSE) is a variant of SE based on linear relationships between state variables and measurements. LSE estimates system state variables, including bus voltage magnitudes and angles in an electric power transmission network, using a network model derived from the topology processor and measurements. Phasor measurement units (PMUs) enable the implementation of LSE by providing synchronized high-speed measurements. However, as the size of the power system increases, the computational overhead of the state-of-the-art (SOTA) LSE grows exponentially, where the practical implementation of LSE is challenged. This paper presents a distributed linear state estimation (D-LSE) at the substation and area levels using a hierarchical transmission network topology processor (H-TNTP). The proposed substation-level and area-level D-LSE can efficiently and accurately estimate system state variables at the PMU rate, thus enhancing the estimation reliability and efficiency of modern power systems. Network-level LSE has been integrated with H-TNTP based on PMU measurements, thus enhancing the SOTA LSE and providing redundancy to substation-level and area-level D-LSE. The implementations of D-LSE and enhanced LSE have been investigated for two benchmark power systems, a modified two-area four-machine power system and the IEEE 68 bus power system, on a real-time digital simulator. The typical results indicate that the proposed multilevel D-LSE is efficient, resilient, and robust for topology changes, bad data, and noisy measurements compared to the SOTA LSE. [ABSTRACT FROM AUTHOR]

Synchrophasor data contain a trove of information on the power system and its dynamics. These measurements have a high potential to unlock our ability to cope with changing system conditions and challenges posed by distributed and intermittent energy sources. While Phasor Measurement Units (PMUs) have seen a large deployment in the grid, their applications are limited by the software platforms that are deployed in control centers to monitor the grid. In this paper, we present an open source toolkit that enables fast prototyping of PMU applications. The toolkit is akin to a software development kit (SDK) for synchrophasor applications, providing a number of functionalities that enable high-level PMU application development within the LabVIEW environment. This Smart-grid Synchrophasor SDK (S3DK) proposes a paradigm based on the concept of distributed applications, which allows development and deployment to be independent of the existing software stack deployed in control centers and to leverage PMU data at any level of a synchrophasor system hierarchy. This paper serves to introduce the S3DK, which is released as open source software to facilitate broader and fast prototyping of synchrophasor applications. [ABSTRACT FROM AUTHOR]

The paper presents the performance enhancement of band-pass filter-based M-class phasor estimation. The performance of the M-class phasor estimation using a band-pass FIR filter is enhanced with a magnitude correction factor for the off-nominal frequency range. The band-pass filter is implemented for the phasor estimation with various window functions Tukey, Rife Vincent class-I order-2 (RV2), Hamming, Hanning, Blackman, and Flat-top (FT) and its performance are evaluated under the steady and dynamic state tests prescribed by the standard. A rigorous performance analysis of enhanced phasor estimation using band-pass FIR filter with various window functions is presented with the accuracy indices of total vector error (TVE), frequency error (FE), and rate of change of frequency error (RFE). The result analysis reveals that the performance enhanced phasor estimation using band-pass FIR filter offers significantly less error than reported work at the low sampling rate. The design and performance analysis of the phasor estimation using the band-pass FIR filter is performed in the MATLAB platform. [ABSTRACT FROM AUTHOR]

Methods for short-term voltage stability (STVS) assessment based on phasor measurement unit (PMU) data have become quite abundant. However, most of them are challenged to deal with the commonly encountered incomplete PMU data. Existing models for STVS assessment of incomplete PMU data mostly have the disadvantages of high computational complexity and large impact from the level of data missing. This paper proposes a multi-task learning method that performs stability assessment and missing data completion in parallel. The Transformer Encoder is used as the shared feature extractor, a GRU structure is used to output complete PMU data sequences, and a BP neural network is used to output stability assessment results. The loss function used for the missing completion task considers the accuracy in both the shape and time domains. A comparison with relevant models shows that the model proposed in this paper achieves the best performance in all indicators and is minimally affected by the observation windows and the level of data loss, demonstrating good robustness. [ABSTRACT FROM AUTHOR]

Abstract Reliability is a fundamental requirement of wide‐area backup protection (WABP) systems, and it is one of the most crucial performance indicators for such a WABP system. Two definitions are introduced: the reliability of WABP systems and the undetectable probability of a power line. They are determined by the specific placement of communication links (CLs) and phasor measurement units (PMUs). A simultaneous optimization model was developed to minimize the construction cost of WABP systems by optimizing the partitioning and placement of PMUs, protection centers, and CLs. The model considers several constraints, including latency, regional balance, and system reliability. To reduce the computational complexity, a cluster‐based genetic algorithm was developed to determine the optimal solution. Finally, numerical simulations were conducted using IEEE test cases. The results demonstrate that the proposed method can minimize the construction costs of WABP systems while increasing their reliability.

Available transfer capability (ATC) is an important measurement index to evaluate the security margin of interconnected power grids and serve as a reference for the transmission right transaction. In modern power systems, ATC is affected by the transmission network topology, renewable power output uncertainty, and load demand uncertainty. Traditional works usually model the power source-load uncertainty by using robust optimization, interval optimization, or chance-constraint optimization, which cannot fully reflect the probabilistic distribution of the daily source-load uncertainty. This paper proposes an ATC assessment methodology based on the typical stochastic scenarios of renewable output and load demand of multiarea power systems. Furthermore, the conditional generative adversarial network (CGAN) algorithm is adopted to generate and select representative scenario sets based on historical raw data, which can fully reflect the usual operating condition of a system with high renewable energy penetration. The scenario set that is fed into the ATC assessment model can fully characterize the impact of source-load uncertainty on daily ATC. Finally, the proposed method is verified by a modified three-area IEEE 9-bus system and a real-world provincial power system. [ABSTRACT FROM AUTHOR]

In the context of the rising prevalence of renewable energy, the need for precise and synchronized measurements in urban distribution networks has become increasingly critical. Conventional synchronization techniques, which predominantly depend on Global Navigation Satellite System (GNSS) timing signals, are often plagued by significant sampling time errors. Addressing this challenge, this study introduces an innovative automatic synchronization measurement method employing proportional-integral (PI) control. This method is composed of three integral components: the monitoring of local clock phases via pulses per second (PPS) signals, modulation of the clock phase through a PI controller, and the estimation of grid phasors from the synchronized sampled data. The test results of this method on a hardware platform demonstrate its capability to significantly reduce the sampling error from 2 x 10-3V to an impressive 1 x 10-5V, while also meeting the requirements of the synchronized phasor measurement standards outlined in C37.118.1. These advancements underscore the significant potential of PI-controlled simultaneous sampling in enhancing the operational efficiency and reliability of urban distribution networks, particularly in environments with a high integration of renewable energy sources. [ABSTRACT FROM AUTHOR]

Ensuring the swift and precise tracking of power system signal parameters, especially the frequency, is imperative for the secure and stable operation of power grids. In instances of faults within the distribution network, abrupt changes in frequency may occur, presenting a challenge for existing algorithms that struggle to effectively track such signal variations. Addressing the need for enhanced performance in the face of frequency mutations, this paper introduces an innovative approach--the Covariance Reconstruction Extended Kalman Filter (CREKF) algorithm. Initially, the dynamic signal model of electric power is meticulously analyzed, establishing a dynamic signal relationship based on high-precision time source sampling tailored to the signal model's characteristics. Subsequently, the filter gain, covariance matrix, and variance iteration equation are determined based on the signal relationship among three sampling points. In a final step, recognizing the impact of the covariance matrix on algorithmic tracking ability, the paper proposes a covariance matrix reset mechanism utilizing hysteresis induced by output errors. Through extensive verification with simulated signals, the results conclusively demonstrate that the CREKF algorithm exhibits superior measurement accuracy and accelerated tracking speed when confronted with mutating signals. [ABSTRACT FROM AUTHOR]

Wide Area Monitoring System (WAMS) using Phasor Measurement Unit (PMU) have been widely implemented in power systems. Due to its voltage and current measurements accuracy and precision, PMU can be adopted into power system defense plan as well as WAMS. However, there are considerations of the PMU data acquisition to be able to deliver performing analysis of the electrical power system, i.e.: the synchronization of data acquisition time, reporting rate effect, data sample acquisition, and data acquisition range. This paper addresses problems in PMU data collection and method to solve them prior to carrying out analysis to achieve reliable results. Further, the proposed method has been exercised and implemented in actual power systems accordingly the data collection issues in PMU for WAMS analysis have been solved. In contrast to other studies, this research offers insight into data collections and its solutions before they are examined. [ABSTRACT FROM AUTHOR]

Reliability is a fundamental requirement of wide‐area backup protection (WABP) systems, and it is one of the most crucial performance indicators for such a WABP system. Two definitions are introduced: the reliability of WABP systems and the undetectable probability of a power line. They are determined by the specific placement of communication links (CLs) and phasor measurement units (PMUs). A simultaneous optimization model was developed to minimize the construction cost of WABP systems by optimizing the partitioning and placement of PMUs, protection centers, and CLs. The model considers several constraints, including latency, regional balance, and system reliability. To reduce the computational complexity, a cluster‐based genetic algorithm was developed to determine the optimal solution. Finally, numerical simulations were conducted using IEEE test cases. The results demonstrate that the proposed method can minimize the construction costs of WABP systems while increasing their reliability. [ABSTRACT FROM AUTHOR]

The high proportion of renewable energy sources in the power grid increases the failure probability of the system, which becomes a new challenge for the safe and stable operation of the regional power grid. To ensure stable control of the power network with substantial renewable energy integration, this article proposes a new method that combines the long short‐term memory (LSTM) neural networks and adaptive cubature Kalman filter (ACKF) to improve the prediction accuracy of mutation data inherited from the renewable generation. Four abnormal scenarios, including low voltage ride‐through (LVRT), high voltage ride‐through (HVRT), continuous fault ride‐through and bad data injection of the regional power grid are investigated through extensive case studies. The proposed method is implemented on the IEEE 30‐node system for performance verification. The simulation results demonstrate that the proposed method has considerably higher robustness than the traditional Kalman filter algorithm and can effectively improve the overall state estimation accuracy of the renewable energy power system under different scenarios. [ABSTRACT FROM AUTHOR]

This study aims to perform fast fault diagnosis and intelligent protection in an active distribution network (ADN) with high renewable energy penetration. Several time-domain simulations are carried out in EMTP-RV to extract time-synchronized current and voltage data. The Stockwell transform (ST) was used in MATLAB/SIMULINK to preprocess these input datasets to train the adaptive fault diagnosis deep convolutional neural network (AFDDCNN) for fault location identification, fault type identification, and fault phase-detection for different penetration levels. Based on the AFDDCNN output, the intelligent protection scheme (IDOCPS) generates the signal for isolating a faulty section of the ADN. An intelligent fault diagnosis scheme that combines ST and deep learning methods aids the artificial intelligence-based protection scheme in isolating the faulty section. This study uses the PyTorch framework to build both the AFDDCNN and IDOCPS. The proposed protection technique classifies and isolates faults and coordinates protection with minimum operating time in the IEEE 13-bus ADN. It consistently gives high accuracy for fault diagnosis and minimum operating time for the IDOCPS even when the network's topology is modified to the IEEE 34-bus ADN. The experimental results indicate that the proposed model is more accurate and provides faster fault diagnosis and isolation than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper proposes a damping torque index (DTI) to determine the optimal installation location of the power system stabilizer (PSS) in the multi-machine power network. The proposed index is based on the classical damping torque analysis (CDTA) method for power system low-frequency electro-mechanical (LFEO) analysis. The selection criteria of the PSS location is the maximum magnitude of DTI under normal operating conditions. The CDTA method is used to design PSS, and the phase compensation technique subsequently adjusts the associated parameters. Furthermore, to analyze the power system's critical oscillation mode, a wide-area damping controller (WADC) is design for the synchronous generator where optimal location of PSS is install. The reduced-order model-based WADC includes time-varying delay in the wide-area signal. The WADC's input signals from the phasor measurement unit (PMU) and the location of the PMU are selected using geometric measures of observability and WADC parameters design based on the residue approach. The proposed approach is validated in two separate test systems, and modal analysis is carried out on MATLAB software. The simulated results have been validated on the real-time digital simulator (RTDS) RSCAD 5.012. Results obtained on RTDS show that the proposed approach efficiently damps low-frequency electro-mechanical oscillations. [ABSTRACT FROM AUTHOR]

Integrating Green Renewable Energy Sources (GRES) as substitutes for fossil fuel-based energy sources is essential for reducing harmful emissions. The GRES are intermittent and their integration into the conventional IEEE 30 bus configuration increases the complexity and nonlinearity of the system. The Grey Wolf optimizer (GWO) has excellent exploration capability but needs exploitation capability to enhance its convergence speed. Adding particle swarm optimization (PSO) with excellent convergence capability to GWO leads to the development of a novel algorithm, namely a Grey Wolf particle swarm optimization (GWPSO) algorithm with excellent exploration and exploitation capabilities. This study utilizes the advantages of the GWPSO algorithm to solve the optimal power flow (OPF) problem for adaptive IEEE 30 bus systems, including thermal, solar photovoltaic (SP), wind turbine (WT), and small hydropower (SHP) sources. Weibull, Lognormal, and Gumbel probability density functions (PDFs) are employed to forecast the output power of WT, SP, and SHP power sources after evaluating 8000 Monte Carlo possibilities, respectively. The multi-objective green economic optimal solution consisted of 11 control variables to reduce the cost, power losses, and harmful emissions. The proposed method to address the OPF problem is validated using an adaptive IEEE bus system. The proposed GWPSO algorithm is evaluated by comparing it with PSO and GWO optimization algorithms in terms of achieving an optimal green economic solution for the adaptive IEEE 30 bus system. This evaluation is conducted within the confines of the same test system using identical system constraints and control variables. The integration of a small SHP with WT and SP sources, along with the proposed GWPSO algorithm, led to a yearly cost reduction ranging from $ 19,368 to $ 30,081. Simulation findings endorsed that the proposed GWPSO algorithm executes fruitfully compared to alternative algorithms regarding a consistent convergence curve and robustness, proving its potential as a viable choice for achieving cost-effective solutions in power systems incorporating GRES. [ABSTRACT FROM AUTHOR]

In recent decades, because of the rapid population growth of the world, considerable changes in climate, the reduction of fossil fuel sources to consume the traditional power plants and their high depreciation, and the increase in fuel prices. Due to the increased penetration of DG units which have a random nature into the power system, the ordinary equations of power flow must be changed. For the power system to operate in a stable condition estimating future demand and calculating the important and operational indexes such as losses of the power system is an important duty that must be done precisely and rapidly. In this paper, the Improved Taguchi method and phasor measurement unit are used to model the uncertainties of DGs and estimate the error of voltage, respectively. The results show that the magnitude error and the angle error of voltage are decreased using PMU. The applied optimal power flow and state estimations are analyzed and verified using standard IEEE 30-bus and 14-bus test power systems by MATLAB, and MINITAB softwares. The Made Strides Taguchi strategy appears to have modeled the DG units precisely and successfully, and using the PMU, the mistake of the point and greatness estimation is exceptionally moot. The values that were evaluated are very close to the values that were done by the Newton-Raphson stack stream. [ABSTRACT FROM AUTHOR]

The application of traditional synchronous measurement methods is limited by frequent fluctuations of electrical signals and complex frequency components in distribution networks. Therefore, it is critical to find solutions to the issues of multifrequency parameter estimation and synchronous measurement estimation accuracy in the complex environment of distribution networks. By utilizing the multifrequency sensing capabilities of discrete Fourier transform signals and Taylor series for dynamic signal processing, a multifrequency signal estimation approach based on HT-IpDFT-STWLS (HIpST) for distribution networks is provided. First, by introducing the Hilbert transform (HT), the influence of noise on the estimation algorithm is reduced. Second, signal frequency components are obtained on the basis of the calculated signal envelope spectrum, and the interpolated discrete Fourier transform (IpDFT) frequency coarse estimation results are used as the initial values of symmetric Taylor weighted least squares (STWLS) to achieve high-precision parameter estimation under the dynamic changes of the signal, and the method increases the number of discrete Fourier. Third, the accuracy of this proposed method is verified by simulation analysis. Data show that this proposed method can accurately achieve the parameter estimation of multifrequency signals in distribution networks. This approach provides a solution for the application of phasor measurement units in distribution networks. [ABSTRACT FROM AUTHOR]