Stability Of Electric Power System Research Articles

An advanced quantum support vector machine for power quality disturbance detection and identification

Quantum algorithms have demonstrated extraordinary potential across numerous fields, offering significant advantages in solving practical problems. Power Quality Disturbances (PQDs) have always been a critical factor affecting the stability and safety of electrical power systems, and accurately detecting and identifying PQDs is crucial for ensuring reliable system operation. This paper explores the application of quantum algorithms in the field of power quality and proposes a novel method using Quantum Support Vector Machines (QSVM) to detect and identify PQDs, which marks the first application of QSVM in PQD analysis. The QSVM model employed involves three main stages: quantum feature mapping, quantum kernel computation, and model training. Quantum feature mapping uses quantum circuits to map classical data into a high-dimensional Hilbert space, enhancing feature separability. Quantum kernel computation calculates the inner products between features for model training. Rigorous theoretical and experimental analyses validate our approach. This method achieves a time complexity of O(N2log(N))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$O(N^{2} \\log (N))$\\end{document}, superior to classical SVM algorithms. Simulation results show high accuracy in PQDs detection, achieving a 100% detection rate and a 96.25% accuracy rate in single PQD identification. Experimental outcomes demonstrate robustness, maintaining over 87% accuracy even with increased noise levels, confirming its effectiveness in PQDs detection and identification.

Application of Hybridized Model of Shunt and Series Facts Controllers for Improvement of Generator Oscillation Damping Stability of Electrical Power System

Application of Hybridized Model of Shunt and Series Facts Controllers for Improvement of Generator Oscillation Damping Stability of Electrical Power System

Analysing the influence of temperature dependence of active resistances on transmitted power in electric power systems

Subject of research: in this paper, the static stability of electric power systems is assessed taking into account the temperature dependence of active resistances. Purpose of research: to show how the consideration of the temperature dependence of active resistances will affect the static stability reserves of electric power systems. Methods and objects of research: calculations of the regime with account of influence of thermal processes in elements of power system are made. The main parameter to take into account thermal processes is the ambient air temperature, so the days with maximum and minimum average daily air temperature were determined. All parameters except ambient air temperature and wind speed were selected in such a way as to provide the worst conditions for thermal exchange. The results of the calculations show that taking into account the thermal processes in the elements of the system significantly affects the transmitted power and the reserves of static aperiodic stability in the power system. Main results of research: practical calculations have shown that the account of thermal processes allows to take into account more accurately the limits of the transmitted power through the elements of the power system, and at the same time the reserve coefficients of static stability.

Real-time control action formation for predicting post-accident electrical modes considering permissible stability margins

The object of the research is an emergency control system for ensuring the stability of electric power systems (EPS) in case of emergency unbalances. The relevance of the problem of ensuring EPS stability is due to the need to improve the efficiency of emergency control to reduce the risk of system accidents with significant damage. To solve this problem, we propose an algorithm for selecting the volume of control actions based on the principles of adaptive control for predicting the post-emergency mode with an acceptable stability margin. The algorithm of forming the volume of control actions is based on the dependence of the value of control actions on the value of stability reserves estimation by the value of the Jacobi determinant. To build this dependence, the algorithm of searching for the limiting mode by the trajectory of change in the equilibrium position of the steady state of the system from the initial to the limiting one is used. In contrast to the existing algorithms, the proposed algorithm establishes a functional dependence of the control value on the current parameters of the regime or the stability margin, which increases the efficiency of calculations for selecting control actions. Realization of the proposed algorithm is carried out on the basis of the functional scheme according to the data of the vector measurement system. The advantage and novelty of the proposed algorithm is the possibility of eliminating the disadvantages of existing systems of mode automation, the main of which are: – the necessity to perform numerous variant calculations for selecting the volume of control actions; – possible excessive volume of control actions in case of a mismatch of the actual mode with the calculated one

Determining the Impact of Coefficients of Forced Excitation of Two Different Excitation Control Systems of Large Synchronous Generators on the Transient Stability of Electric Power Systems

Determining the Impact of Coefficients of Forced Excitation of Two Different Excitation Control Systems of Large Synchronous Generators on the Transient Stability of Electric Power Systems

New Electric Power System Stability Evaluation Based on Game Theory Combination Weighting and Improved Cloud Model

The development of new electric power system is a requirement for China to realize its “carbon peaking and carbon neutrality goals”, so it is of great importance that we assess the stability of a new electric power system under certain conditions. Firstly, according to the factors affecting the stability of the new power system, the characteristics of the new electric power system are analyzed in depth, so as to establish a stability evaluation index system including four first-level indices of safety, adequacy, flexibility, and adaptability. Secondly, a stability evaluation model is proposed. Based on the game theory, the entropy weight method, criteria importance though intercrieria correlation (CRITIC) method, and coefficient of variation method are combined and assigned, and the cloud model is improved through the combination of weights, which is used for evaluating the stability of the new electric power system. Finally, the applicability of the proposed evaluation model is verified by an arithmetic example analysis, which can identify the vulnerability of the new electric power system and provide suggestions for improving its stability. The model can provide a theoretical basis for promoting energy transition and sustainable development.

Сравнение характеристик линий высокого и сверхвысокого напряжений при выборе параметров управляемого устройства продольной компенсации

The use of series compensation devices leads to an increase of the transmission line capacity. However, it also increases the risk of an oscillatory violation of the stability of the electric power system. Cumulative hunting and self-excitation of the system is observed in the systems with controlled series compensation devices with a high degree of capacitive compensation. It is known that automatic excitation control of generators partially eliminates the negative effect of series capacitive compensation on oscillatory stability. Therefore, when choosing the tuning parameters of controlled devices, it is necessary to consider their combined effect on the system modes. The developed methods to study steady-state stability make it possible to analyze the modes of power systems with controlled power transmission of various voltage levels. Thus, it is advisable to conduct a comparative analysis of the influence of the adjustable parameters of the series compensation device on the steady-state stability of systems of various voltage levels, considering synchronous generators automatic excitation control. Methods of mathematical modeling of the electric power system, the theory of long-distance power lines and electromechanical transients, methods of analyzing the stability of electric power systems are used. The original software in the C ++ programming language has been used as a modeling tool. The choice of controlled series compensation device and the automatic excitation control regulation laws parameters is made, provided that steady-state stability is maintained. The stability regions of the studied electric power-engineering system are constructed depending on the tuning parameters of the considered devices of various voltage levels. A comparative analysis of the characteristics of the modes of high and extra-high voltage lines has been carried out. The regulation coefficients values of controlled series compensation device and automatic excitation of generators have been determined, under which there is no violation of oscillatory stability for different voltage levels. The conducted comparative analysis of the characteristics of high and extra-high voltage power lines demonstrates the need to consider the power transmission rated voltage when comprehensively selecting the tuning parameters of the controlled series compensation device and the automatic excitation control regulator.

Interconnected Smart Transactive Microgrids—A Survey on Trading, Energy Management Systems, and Optimisation Approaches

The deployment of isolated microgrids has witnessed exponential growth globally, especially in the light of prevailing challenges faced by many larger power grids. However, these isolated microgrids remain separate entities, thus limiting their potential to significantly impact and improve the stability, efficiency, and reliability of the broader electrical power system. Thus, to address this gap, the concept of interconnected smart transactive microgrids (ISTMGs) has arisen, facilitating the interconnection of these isolated microgrids, each with its unique attributes aimed at enhancing the performance of the broader power grid system. Furthermore, ISTMGs are expected to create more robust and resilient energy networks that enable innovative and efficient mechanisms for energy trading and sharing between individual microgrids and the centralized power grid. This paradigm shift has sparked a surge in research aimed at developing effective ISTMG networks and mechanisms. Thus, in this paper, we present a review of the current state-of-the-art in ISTMGs with a focus on energy trading, energy management systems (EMS), and optimization techniques for effective energy management in ISTMGs. We discuss various types of trading, architectures, platforms, and stakeholders involved in ISTMGs. We proceed to elucidate the suitable applications of EMS within such ISTMG frameworks, emphasizing its utility in various domains. This includes an examination of optimization tools and methodologies for deploying EMS in ISTMGs. Subsequently, we conduct an analysis of current techniques and their constraints, and delineate prospects for future research to advance the establishment and utilization of ISTMGs.

A fuzzy approach to the regional electric power system's stability monitoring based on socially available information

Subject. This article deals with the issues related to the stability of the region's electricity system. Objectives. The article aims to develop an original approach to monitoring the stability of the region's electric power system. Methods. For the study, we used a fuzzy logic approach. Results. The article proposes an algorithm for monitoring the stability of the region's electric power system based on socially accessible information, based on a fuzzy approach. The proposed forecasting research algorithm consists of five successive steps. The result of the forecasting was a polynomial function reflecting the change in the parameter of the load on the system over time. Conclusions and Relevance. The consumption indicator over time is unstable, prone to sharp changes both negatively and positively, which may be due to the specifics of the formation of demand for electricity, where the consumer's decision is of key importance. The results of the study can be used to develop strategies for regional electricity consumption systems, and can also be implemented in the practice of specific electric power enterprises as part of making forecasts for energy consumption.

Improving Power Quality Using D-STATCOM with Capacitor as Energy Storage System

Power electronics are used more frequently to raise awareness of power quality which must be considered when designing modern power systems for effective operation and continuous power supply to various load centers. Otherwise, it will be hazardous for residential and commercial customers, reducing the production of their work due to faults and equipment damage. Electronic technology known as Flexible AC Transmission System (FACTS) may improve power stability to make the electric power system efficient. Oscillation damping in the Power system is still a big concern for operating a safe and dependable power system. The static synchronous compressor (STATCOM) is connected to assist power networks with poor power quality and voltage regulation. Although there are several uses, voltage stability is the most frequently used. An average model of the D-STATCOM control approach is presented in this work to improve the transmission of power quality flow and voltage stability in electric power systems. An increase in system voltage stability, reactive power flow control for different loads, and dynamic response of STATCOM are the primary goals of this research work.

Power Smoothing Strategy for Wind Generation Based on Fuzzy Control Strategy with Battery Energy Storage System

This work discusses the use of a battery energy storage system applied to the smoothing of power generated at the output of wind turbines based on a fuzzy logic power control. The fuzzy control logic proposed can perform the aforementioned activity while the state of charge of the energy storage system is maintained within operational limits. In order to assess the fuzzy logic power control’s effectiveness at maintaining the state of charge levels within the allowed range, two operating situations are explored: one in which the state of charge is above the upper limit allowed, and another in which the state of charge is in the minimum value allowed. The numerical results show that, when using the battery energy storage system in conjunction with the control logic proposed, the active power provided as the point of common coupling by the wind turbines can be smoothed, thus contributing to the Electric Power System reliability and stability. The main results of this paper are based on measurements of wind and active power associated with a wind generation plant installed in the northeast region of Brazil and equipped with 2.1 MW wind turbines.

Review of Methods for Reducing Power Losses in Electric Grids Containing Renewable Energy Sources

In recent decades, there has been an increase in the use of renewable energy sources, which serve as the basis for the concept of distributed generation, that is, facilities that produce electricity at the location of consumers. The large-scale introduction of distributed generation affects the reliability and stability of electric power systems, including by reducing power losses in them by reducing the current in the lines. Taking into account the forecasts of many experts on the growth of distributed energy resources, the issue of studying methods for reducing electrical energy losses in distribution grids containing renewable energy sources is relevant. (Research purpose) The research purpose is determining methods for reducing electricity losses in electric grids containing renewable energy sources. (Materials and methods) Presented an information search, including an overview of Internet sources and sources of scientific literature devoted to methods of reducing electricity losses in electric grids. (Results and discussion) They used Yandex and Google search engines, Scopus, Web of Science and Google Scholar scientific databases, Cyberlenink electronic libraries as sources of information, eLIBRARY.ru , Elsevier, Springer, IEEE Xplore. It was shown that the literary analysis included 59 sources most suitable for the research topic, of which 8 sources of information are given in Russian, 51 in English. In the course of the information search, we determined methods for reducing electricity losses both in traditional electric grids and in electric grids containing renewable energy sources. (Conclusions) It was found out that the considered methods inherent in traditional power supply grids are also relevant for networks with renewable energy sources. It was revealed that the use of renewable energy sources in itself also allows to significantly reduce electricity losses in electric grids due to distributed generation

Equivalent Transformations of Complex Circuits in Calculations of Power Supply Systems of Agrocomplex

he tasks at the stage of planning and operation of power supply systems are related to the calculation of the parameters of their operation modes. With the massive nature of such complex calculations, their execution is possible only with the help of appropriate software and computing tools, that is, using an electronic computer. (Research purpose) The research purpose is to substantiate the algorithm of equivalence of power supply systems or their individual sections as an integral part of the algorithms complex for calculating electrical systems. (Materials and methods) It has been shown that at present there are many agricultural consumers, interruptions in the power supply of which lead to significant damage, in this regard, closed power supply systems are becoming increasingly used, as the requirements for reliability, uninterrupted power supply and the quality of electricity supplied to the consumer are increasing. It was determined that the calculation of closed systems is more complicated than open ones. It was revealed that when solving a wide range of tasks related to the creation and modernization of power supply systems, there is a need to simplify the systems or their individual sections. (Results and discussion) The equivalence rule was formed and methods for calculating equivalent resistances and electromotive force of the converted sections of the replacement circuits were given, which simplifies the circuit and accelerates the process of calculating and analyzing the modes of power supply systems. (Conclusions) It was established that the proposed equivalence algorithm is based on universal formulas for calculating intrinsic and mutual resistances, which were previously obtained by the authors for use in problems of calculating steady-state modes of power supply systems. It was stated that the calculation algorithm is also applicable for the equivalence of forward and reverse sequence circuits in the calculation of currents of asymmetric short circuits and in the analysis of the dynamic stability of electric power systems

Monitoring Transient Stability in Electric Power Systems Using a Hybrid Model of Convolutional Neural Network and Random Forest

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

Leveraging Deep Learning Algorithms for Predicting Power Outages and Detecting Faults: A Review

Power outage prediction and fault detection play crucial roles in ensuring the reliability and stability of electrical power systems. Traditional methods for predicting power outages and detecting faults rely on rule-based approaches and statistical analysis, which often fall short of accurately capturing the complex patterns and dynamics of power systems. Deep learning algorithms, with their ability to learn automatically representations from large amounts of data, have emerged as promising solutions for addressing these challenges. In this literature review, we present an overview of deep learning algorithms applied to power outage prediction and fault detection. The purpose of this literature review is to explore the uses, effectiveness and advantages and disadvantages of utilizing deep learning algorithms in this domain. Various deep-learning models were explored in the context of power outage prediction and fault detection. Convolutional Neural Networks (CNNs) are effective in analyzing spatial dependencies and patterns in power system data, such as voltage levels and load distributions. Recurrent Neural Networks (RNNs), particularly Long Short Term Memory (LSTM) networks, excel in capturing temporal dependencies and patterns in time series data such as power demand and line currents. Generative Adversarial Networks (GANs) offer a unique approach by generating synthetic power system data for training purposes. This literature review involve collecting historical power system data from various sites. Deep-learning algorithms demonstrated promising results in power outage prediction and fault detection. It achieved a high accuracy rate of 95% in predicting power outages and accurately classified various fault types with an average precision of 92%. The findings highlight the advantages of deep learning algorithms in power outage prediction and fault detection. This literature review provides a comprehensive review of deep learning algorithms in the context of power outage prediction and fault detection.

Предел передаваемой мощности управляемой линии электропередачи высокого напряжения для режимов на границе статической устойчивости

The use of series compensation devices is one of the methods to increase the capacity of a power transmission line. In addition, the use of controlled series compensation devices affects the steady-state stability of the electric power system. It is also widely known that automatic excitation control of generators has a positive effect on the stability of the system. Thus, it is reasonable to analyze the influence of the adjustable parameters of the controlled series compensation device on the line capacity when considering modes at the boundaries of the stability regions, taking into account automatic excitation controllers on synchronous generators of the power plant. Methods of mathematical modeling of the electric power system, the theory of long-distance power lines and electromechanical transients, methods of analyzing the stability of electric power systems are used. The original software in the C ++ programming language is used as a modeling tool. The authors have selected the regulation laws parameters of controlled series compensation device and automatic excitation controller, provided that steady-state stability is maintained. The stability regions of the electric power-engineering system under study are constructed depending on the tuning parameters of the considered devices. A technique is proposed to select the coefficients of complex regulation of the devices to increase the capacity of a high-voltage power transmission line. The proposed method of the complex selection of regulation coefficients of controlled devices provides the possibility of obtaining their optimal values in a controlled electric power system to ensure stability.

Maiden application of colliding bodies optimizer for load frequency control of two‐area nonreheated thermal and hydrothermal power systems

AbstractThis article presents a novel load frequency control (LFC) approach using colliding bodies optimizer (CBO) for frequency stabilization of interlinked multiarea electric power systems. The optimal parameters of the suggested CBO‐based proportional–integral–derivative‐filter controller ascertain an effective LFC solution. First, a well‐known and widely used linearized two‐area nonreheated thermal power system is examined to demonstrate the efficacy of the proposed approach. The effectiveness of the proposed method is analyzed by comparing the outcomes of several recently presented LFC schemes. The performance analysis shows a settling time improvement of 3.10%, 14.29%, and 18.66% in the case of area‐1 and area‐2 frequencies and tie‐line power deviations compared with an imperialist competitive algorithm (ICA)‐based controller. The robustness of the proposed scheme is also evaluated in the presence of various operating scenarios. Additionally, the work is extended to a two‐area nonreheated hydrothermal power system. The proposed method shows an improvement of over 60% in the performance index compared with several existing techniques‐based controllers such as optics‐inspired optimization, gray‐wolf optimization, quasi‐oppositional differential search algorithm, bacterial foraging optimization algorithm, and ICA.

Rapid hydrophobicity recovery of contaminated silicone rubber using low-power microwave plasma in ambient air

The loss of hydrophobicity of silicone rubber (SR) insulators due to contamination accumulation will increase the risk of flashover and threaten the stability of electrical power system. To solve this problem, a highly efficient and environmentally friendly microwave plasma surface treatment method is developed, which only consumes low-power electricity and ambient air without any chemical agent or rare gas. The hydrophobicity of seriously contaminated SR can be recovered on the order of 10 s, with a nice long-term aging performance under natural transfer. The underlying mechanism is revealed via carefully designed control experiments and systematic surface analysis. It is illustrated that the plasma-generated active species break the long chain of SR into non-crosslinked low-molecule-weight siloxanes (LMWs) and accelerate their transfer to contamination layer, while the bombing and etching effects of plasma manufacture a porous structure and increase the surface roughness. All of these factors will contribute to the increase of hydrophobicity. On the contrary, the oxidation effect of plasma will turn LMWs into polar groups and destroy the hydrophobicity, which should be restricted in practice. The microwave plasma treatment method developed in this work has the on-line application potential in power system for hydrophobicity recovery under severe contamination conditions.

Short-term multi-step wind power forecasting based on spatio-temporal correlations and transformer neural networks

Spatio-temporal wind power forecasting is significant to the stability of electric power systems. However, the accuracy of power forecasting results is easily impaired by the insufficient capacity of sequence modeling and misleading information from distinct wind turbines. In this paper, a novel method is proposed to resolve the mentioned problem. Specifically, the reliability of wind condition knowledge is enhanced by considering the spatial information of surrounding wind turbines. Moreover, two metrics based on the distance and correlation are developed to evaluate the quality of spatial information. To learn sequential dependencies regardless of the distance, wind power modeling is achieved by transformer neural networks based on the multi-head attention mechanism. Furthermore, experiments are conducted to assess the performance of the proposed method with real-world measurements. Results show that the proposed method outperforms several baseline and state-of-the-art approaches, and the superiority is particularly prominent with large steps. In two experiments, the average values of mean absolute error of forecasting results generated by the proposed method are only 0.0914 and 0.0911, respectively, which is significantly better than other approaches. With accurate results of short-term multi-step forecasting, this work makes contributions to the effective utilization of wind energy resources.

Using Solar Photovoltaic Systems, Battery Energy Storage Systems, and Underfrequency Load-Shedding to Improve the Frequency Stability of Power Systems

Electrical power systems operate at 50 Hz. However, generation loss, a sudden increase in loads, or faults in the system cause disturbances and deviations that destabilize the frequency of electrical power systems. Therefore, there is a need to study and improve the frequency stability of electrical power systems during disturbances. The present study examines improving the frequency stability of electrical power system, using a solar photovoltaic (PV) system, a battery energy storage system (BESS), and underfrequency load-shedding (UFLS) to estimate and control the frequency.The proposed method was tested on a standard Institute of Electrical and Electronics Engineers' (IEEE®) 9-bus system that was simulated in MATLAB® Simulink. The simulation results indicate that the used method significantly stabilizes the frequency of electrical power system.