IEEE Open Access Journal of Power and Energy最新文献

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

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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

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

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

Graph Neural Network-Based Approach for Detecting False Data Injection Attacks on Voltage Stability

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-01-06 DOI: 10.1109/OAJPE.2024.3524268

Shahriar Rahman Fahim;Rachad Atat;Cihat Kececi;Abdulrahman Takiddin;Muhammad Ismail;Katherine R. Davis;Erchin Serpedin

{"title":"Graph Neural Network-Based Approach for Detecting False Data Injection Attacks on Voltage Stability","authors":"Shahriar Rahman Fahim;Rachad Atat;Cihat Kececi;Abdulrahman Takiddin;Muhammad Ismail;Katherine R. Davis;Erchin Serpedin","doi":"10.1109/OAJPE.2024.3524268","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3524268","url":null,"abstract":"The integration of information and communication technologies into modern power systems has contributed to enhanced efficiency, controllability, and voltage regulation. Concurrently, these technologies expose power systems to cyberattacks, which could lead to voltage instability and significant damage. Traditional false data injection attacks (FDIAs) detectors are inadequate in addressing cyberattacks on voltage regulation since a) they overlook such attacks within power grids and b) primarily rely on static thresholds and simple anomaly detection techniques, which cannot capture the complex interplay between voltage stability, cyberattacks, and defensive actions. To address the aforementioned challenges, this paper develops an FDIA detection approach that considers data falsification attacks on voltage regulation and enhances the voltage stability index. A graph autoencoder-based detector that is able to identify cyberattacks targeting voltage regulation is proposed. A bi-level optimization approach is put forward to concurrently optimize the objectives of both attackers and defenders in the context of voltage regulation. The proposed detector underwent rigorous training and testing across different kinds of attacks, demonstrating enhanced generalization performance in all situations. Simulations were performed on the Iberian power system topology, featuring 486 buses. The proposed model achieves 98.11% average detection rate, which represents a significant enhancement of 10-25% compared to the cutting-edge detectors. This provides strong evidence for the effectiveness of proposed strategy in tackling cyberattacks on voltage regulation.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"12-23"},"PeriodicalIF":3.3,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10824826","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106688","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

Harmonic and Supra-Harmonic Emissions of Electric Vehicle Chargers: Modeling and Cumulative Impact Indices 电动汽车充电器谐波与超谐波排放：建模与累积影响指标

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2024-12-23 DOI: 10.1109/OAJPE.2024.3521030

Antonio Bracale;Pierluigi Caramia;Giovanni Mercurio Casolino;Pasquale de Falco;Iqrar Hussain;Pietro Varilone;Paola Verde

{"title":"Harmonic and Supra-Harmonic Emissions of Electric Vehicle Chargers: Modeling and Cumulative Impact Indices","authors":"Antonio Bracale;Pierluigi Caramia;Giovanni Mercurio Casolino;Pasquale de Falco;Iqrar Hussain;Pietro Varilone;Paola Verde","doi":"10.1109/OAJPE.2024.3521030","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3521030","url":null,"abstract":"The analysis of power quality disturbances in distribution systems has gained significance with the diffusion of electric vehicles (EVs). Waveform distortions are interesting since EV currents introduce distortions with spectral components in both low and high-frequency bands. This paper develops specific indices to assess cumulative emissions from single-phase EV on-board chargers, extending the aggregation and diversity factors to the supra-harmonic range. The methodology accounts for variables such as EV charging powers, upstream network impedance, and number of EVs. A simplified time-domain model of a low-power unidirectional converter, commonly used for EV battery charging, is employed to balance circuit complexity and computational effort. This model allows for sensitivity analyses of key parameters influencing charger emissions. Numerical applications are carried out for both individuals and groups of EV chargers at a charging station. Results highlight the need for careful quantification of aggregated EV emissions, showing that supra-harmonic emissions are highly sensitive to variations in the power absorbed by EV chargers. Notably, their cumulative impact is much lower when chargers operate at different power levels than when all chargers operate at the same power level. These findings underscore the importance of accurately assessing the impact of EV charging on power quality.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"11 ","pages":"690-702"},"PeriodicalIF":3.3,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10811949","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912416","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