IEEE Open Access Journal of Power and Energy最新文献_第2页

Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning 推进连贯电网划分：包含机器和深度学习的综述

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-28 DOI: 10.1109/OAJPE.2025.3535709

Mohamed Massaoudi;Maymouna Ez Eddin;Ali Ghrayeb;Haitham Abu-Rub;Shady S. Refaat

{"title":"Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning","authors":"Mohamed Massaoudi;Maymouna Ez Eddin;Ali Ghrayeb;Haitham Abu-Rub;Shady S. Refaat","doi":"10.1109/OAJPE.2025.3535709","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3535709","url":null,"abstract":"With the escalating intricacy and expansion of the interconnected electrical grid, the likelihood of power system (PS) collapse has escalated dramatically. There is an increased emphasis on immunizing renewable-dominated power systems from large-scale cascading failures and cyberattacks through optimal power grid partitioning (PGP). By altering the network’s topology, partitioning aims to create areas within the PS that are not only robust but also have increased flexibility in generation and improved controllability over variable demand. This article provides an updated review of the cutting-edge machine learning and data-driven techniques used for PGP in networked PSs. To this end, an in-depth exploration of the basic principles of PGP and performance quantification is provided. The coherency adequacy and controlled islanding within the power network are comprehensively discussed. Subsequently, state-of-the-art research that envisions the use of clustering-based machine learning and deep learning-based solutions for PGP is presented. Finally, key research gaps and future directions for effective PGP are outlined. This paper provides PS researchers with a bird’s eye view of the current state of mainstream PGP implementations. Additionally, it assists stakeholders in selecting the most appropriate clustering algorithms for PGP applications.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"59-75"},"PeriodicalIF":3.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855832","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IEEE Open Access Journal of Power and Energy Publication Information IEEE电力与能源开放获取杂志出版信息

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IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-23 DOI: 10.1109/OAJPE.2025.3525881

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Synergistic Meta-Heuristic Adaptive Real-Time Power System Stabilizer (SMART-PSS) 协同启发式自适应实时电力系统稳定器（SMART-PSS）

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-23 DOI: 10.1109/OAJPE.2025.3532768

Khaled Aleikish;Jonas Kristiansen Nøland;Thomas Øyvang

{"title":"Synergistic Meta-Heuristic Adaptive Real-Time Power System Stabilizer (SMART-PSS)","authors":"Khaled Aleikish;Jonas Kristiansen Nøland;Thomas Øyvang","doi":"10.1109/OAJPE.2025.3532768","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3532768","url":null,"abstract":"Classical fixed-parameter power system stabilizers (PSS) are typically designed to work well for a limited and specific set of operating conditions. However, the integration of low-inertia, inverter-based renewable energy resources (RES) has led to rapid fluctuations in power dispatch, rendering non-adaptive PSSs obsolete. This paper presents a novel hybrid gray-box modeling approach for real-time adaptation of PSS parameters during operation, thereby enabling the PSS to effectively handle a broader range of operating conditions. In our proposed method, we employ a two-stage process. First, we utilize a modified Heffron-Phillips model and meta-heuristics to synthesize the PSS’s compensating transfer function across a broad spectrum of operating conditions independently of external system parameters. Second, we leverage machine learning techniques to extrapolate the tuning results, thus ensuring adaptability across the full range of operating conditions. The effectiveness of this design methodology is rigorously evaluated in multi-machine power systems. Simulation results demonstrate that the proposed SMART-PSS exhibits robust performance compared to conventional fixed-parameter controllers, reducing the maximum phase deviation by 70% to 96%. This makes it highly suitable for modern power systems, which face diverse and dynamic operational challenges.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"36-45"},"PeriodicalIF":3.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10850756","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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2025 Index IEEE Open Access Journal of Power and Energy Vol. 11 电力与能源学报，第11卷

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IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-23 DOI: 10.1109/OAJPE.2025.3532174

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Information for authors 作者信息

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-23 DOI: 10.1109/OAJPE.2025.3525883

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2024 Best Papers, Outstanding Associate Editors, and Outstanding Reviewers 2024年最佳论文、杰出副编辑和杰出审稿人

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IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-22 DOI: 10.1109/OAJPE.2025.3528699

Fangxing Fran Li

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Asymmetry of Frequency Distribution in Power Systems: Sources, Estimation, Impact and Control 电力系统中频率分布的不对称：来源、估计、影响和控制

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-22 DOI: 10.1109/OAJPE.2025.3532693

Taulant Kërçi;Federico Milano

{"title":"Asymmetry of Frequency Distribution in Power Systems: Sources, Estimation, Impact and Control","authors":"Taulant Kërçi;Federico Milano","doi":"10.1109/OAJPE.2025.3532693","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3532693","url":null,"abstract":"This paper analyses an emerging real-world phenomena in inverter-based renewable-dominated power systems, namely, asymmetry of frequency distribution. The paper first provides a rationale on why asymmetry reduces the “quality” of the frequency control and system operation. Then it provides qualitative theoretical insights that explain asymmetry in terms of the nonlinearity of real-world power systems and associated models. In particular network losses and pitch angle-based frequency control of wind power plants are discussed. Then the paper proposes a nonlinear compensation control to reduce the asymmetry as well as a statistical metric based on the frequency probability distribution to quantify the level of asymmetry in a power system. Real-world data obtained from the Irish and Australian transmission systems serve to support the theoretical appraisal, whereas simulations based on an IEEE benchmark system show the effectiveness of the proposed nonlinear compensation. The case study also shows that, while automatic generation control reduces asymmetry, frequency control limits and droop-based frequency support provided by wind generation using a tight deadband of ±15 mHz, namely active power control, leads to a significant increase in the asymmetry of the frequency probability distribution.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"135-145"},"PeriodicalIF":3.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10849615","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Snake Optimizer Improved Variational Mode Decomposition for Short-Term Prediction of Vehicle Charging Loads 基于变分模态分解的Snake优化器的车辆充电负荷短期预测

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-20 DOI: 10.1109/OAJPE.2025.3529944

Quanxue Guan;Qinhe Liu;Shaocong Tao;Yunjian Xu;Di Zhou;Haoyong Chen;Xiaojun Tan

{"title":"Snake Optimizer Improved Variational Mode Decomposition for Short-Term Prediction of Vehicle Charging Loads","authors":"Quanxue Guan;Qinhe Liu;Shaocong Tao;Yunjian Xu;Di Zhou;Haoyong Chen;Xiaojun Tan","doi":"10.1109/OAJPE.2025.3529944","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3529944","url":null,"abstract":"The rapid proliferation of electric vehicles (EVs) significantly impacts the power grid, necessitating effective forecasting of charging loads. For ultra short-term load prediction, this paper proposes a Snake Optimization (SO)-Variational Mode Decomposition (VMD)-Long Short-Term Memory (LSTM) algorithm trained by only the historical charging data. Before the prediction starts, the VMD method is utilized to minimize the data complexity, yielding several multiple Intrinsic Mode Functions (IMFs) that correspond to the charging load features at different time scales. The VMD parameters are automatically adjusted using the SO method, instead of the trial-and-error method, to trade off the prediction accuracy against computational overhead. Once the parameters of the VMD are determined, the same number of LSTM networks are employed to forecast the corresponding charging loads from these IMFs, with one LSTM for each IMF. Due to the VMD, IMFs with spanned center frequencies containing few irregularities make the prediction simple. These LSTM outcomes are then summed to obtain the overall load prediction. Experiments are carried out to show that the proposed parallel structure of multiple LSTM networks can achieve high prediction accuracy without requiring complex model structures. Our proposed algorithm outperforms the traditional prediction methods including Gate Recurrent Unit, Extreme Learning Machine, LSTM, and their combination with VMD, significantly reducing the Root Mean Square Error and the Mean Absolute Error by 30.1% and 32.9% in comparison with the optimal VMD-LSTM approach, and by 59.3% and 62.6% with respect to the baseline LSTM method.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"76-87"},"PeriodicalIF":3.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10846941","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Proactive Frequency Stability Scheme Based on Bayesian Filters and Spectral Clustering 基于贝叶斯滤波和谱聚类的主动频率稳定方案

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-17 DOI: 10.1109/OAJPE.2025.3531240

Gian Paramo;Mario D. Baquedano-Aguilar;Arturo Bretas;Sean Meyn

{"title":"Proactive Frequency Stability Scheme Based on Bayesian Filters and Spectral Clustering","authors":"Gian Paramo;Mario D. Baquedano-Aguilar;Arturo Bretas;Sean Meyn","doi":"10.1109/OAJPE.2025.3531240","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3531240","url":null,"abstract":"This work presents a proactive distributed model for power system frequency stability. High-level penetration of renewable energy sources into the grid have introduced unforeseen and unmodeled system dynamics. Underfrequency load shedding state-of-the-art solutions are reactive in design, with efficiency constrained by the modeling error. Being able to detect unstable conditions early makes it possible to generate optimized corrective actions. In this work, phasor measurement units are used to predict frequency values. When a disturbance is detected, the state of frequency is predicted a few seconds into the future via a particle filter algorithm. Corrective actions are modeled through a mixed integer linear programming algorithm within system areas established through spectral clustering. The solution is implemented on Matlab, considering IEEE test systems. The proactive design of the method combined with its multiple layers of optimization deliver results that outperform state-of-the-art solutions. Easy-to-implement model, without hard-to-derive parameters, highlights potential aspects towards real-life implementation.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"100-110"},"PeriodicalIF":3.3,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10844303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graphon Neural Networks-Based Detection of False Data Injection Attacks in Dynamic Spatio-Temporal Power Systems 基于Graphon神经网络的动态时空电力系统虚假数据注入攻击检测

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-16 DOI: 10.1109/OAJPE.2025.3530352

Rachad Atat;Abdulrahman Takiddin;Muhammad Ismail;Erchin Serpedin

{"title":"Graphon Neural Networks-Based Detection of False Data Injection Attacks in Dynamic Spatio-Temporal Power Systems","authors":"Rachad Atat;Abdulrahman Takiddin;Muhammad Ismail;Erchin Serpedin","doi":"10.1109/OAJPE.2025.3530352","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3530352","url":null,"abstract":"Cyberattacks on power systems have doubled due to digitization, impacting healthcare, social, and economic sectors. False data injection attacks (FDIAs) are a significant threat, allowing attackers to manipulate power measurements and transfer malicious data to control centers. In this paper, we propose the use of graphon neural networks (WNNs) for detecting various FDIAs. Unlike existing graph neural network (GNN)-based detectors, WNNs are efficient as they make use of the non-parametric graph processing method known as graphon, which is a limiting object of a sequence of dense graphs, whose family members share similar characteristics. This allows to leverage the learning by transference on the graphs to address the computational complexity and environmental concerns of training on large-scale systems, and the dynamicity resulting from the spatio-temporal evolution of power systems. Through experimental simulations, we show that WNN significantly improves FDIAs detection, training time, and real-time decision making under topological reconfigurations and growing system size with generalization and scalability benefits compared to conventional GNNs.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"24-35"},"PeriodicalIF":3.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843273","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0