IEEE Open Access Journal of Power and Energy最新文献

Coherency-Constrained Spectral Clustering for Power Network Reduction

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-02-04 DOI: 10.1109/OAJPE.2025.3538619

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

{"title":"Coherency-Constrained Spectral Clustering for Power Network Reduction","authors":"Mario D. Baquedano-Aguilar;Sean Meyn;Arturo Bretas","doi":"10.1109/OAJPE.2025.3538619","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3538619","url":null,"abstract":"This paper presents a methodology for reducing the complexity of large-scale power network models using spectral clustering, aggregation of electrical components, and cost function approximation. Two approaches are explored using unconstrained and constrained spectral clustering to determine areas for effective system reduction. Once the system areas are determined, both loads and generators by type are aggregated, and their new cost function is approximated through polynomial curve-fitting or statistical methods. The performance of reduced networks is evaluated in terms of their ability to follow the true daily cost of the original system over a 24-hour period considering a set of several days. Two test systems are taken as test beds. Application of the methodology to a modified version of the IEEE 39-bus system reduces it from 17 generators to a 4-bus system and 9 generators with about 93% of accuracy. Similarly, the IEEE 118-bus system is reduced from 19 generators to a 3-bus system with three aggregated units achieving over 99% of accuracy. These findings address scalability challenges and enhance accuracy for high and mid-loading level conditions, and by aggregating thermal units with similar cost functions.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"88-99"},"PeriodicalIF":3.3,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870381","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422842","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

Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning

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}

引用次数: 0

Synergistic Meta-Heuristic Adaptive Real-Time Power System Stabilizer (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}

引用次数: 0

IEEE Open Access Journal of Power and Energy Publication Information

IF 3.3

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

引用次数: 0

Information for authors

IF 3.3

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

引用次数: 0

2025 Index IEEE Open Access Journal of Power and Energy Vol. 11

IF 3.3

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

引用次数: 0

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

IF 3.3

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

Fangxing Fran Li

引用次数: 0

Snake Optimizer Improved Variational Mode Decomposition for Short-Term Prediction of Vehicle Charging Loads

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}

引用次数: 0

Graphon Neural Networks-Based Detection of False Data Injection Attacks in Dynamic Spatio-Temporal Power Systems

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

Physics-Informed Kolmogorov-Arnold Networks for Power System Dynamics

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-15 DOI: 10.1109/OAJPE.2025.3529928

Hang Shuai;Fangxing Li

{"title":"Physics-Informed Kolmogorov-Arnold Networks for Power System Dynamics","authors":"Hang Shuai;Fangxing Li","doi":"10.1109/OAJPE.2025.3529928","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3529928","url":null,"abstract":"This paper presents, for the first time, a framework for Kolmogorov-Arnold Networks (KANs) in power system applications. Inspired by the recently proposed KAN architecture, this paper proposes physics-informed Kolmogorov-Arnold Networks (PIKANs), a novel KAN-based physics-informed neural network (PINN) tailored to efficiently and accurately learn dynamics within power systems. PIKANs offer a promising alternative to conventional Multi-Layer Perceptrons (MLPs) based PINNs, achieving superior accuracy in predicting power system dynamics while employing a smaller network size. Simulation results on test power systems underscore the accuracy of the PIKANs in predicting rotor angle and frequency with fewer learnable parameters than conventional PINNs. Specifically, PIKANs can achieve higher accuracy while utilizing only 50% of the network size required by conventional PINNs. Furthermore, simulation results demonstrate PIKANs’ capability to accurately identify uncertain inertia and damping coefficients. This work opens up a range of opportunities for the application of KANs in power systems, enabling efficient dynamic analysis and precise parameter identification.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"46-58"},"PeriodicalIF":3.3,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843279","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106684","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