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

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

Asymmetry of Frequency Distribution in Power Systems: Sources, Estimation, Impact and Control

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}

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

Proactive Frequency Stability Scheme Based on Bayesian Filters and Spectral Clustering

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

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

Fair Cost Allocation in Energy Communities Under Forecast Uncertainty

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2024-12-19 DOI: 10.1109/OAJPE.2024.3520418

Michael Eichelbeck;Matthias Althoff

{"title":"Fair Cost Allocation in Energy Communities Under Forecast Uncertainty","authors":"Michael Eichelbeck;Matthias Althoff","doi":"10.1109/OAJPE.2024.3520418","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3520418","url":null,"abstract":"Energy communities (ECs) are an increasingly studied path toward improving prosumer coordination. A central challenge of ECs is to allocate cost savings fairly to members. While many allocation mechanisms have been developed, existing literature does not account for the implications of inaccurate forecasts on the fairness of the allocation. We introduce a set of fairness conditions for imperfect knowledge allocation and show that these conditions constitute a Pareto front. We demonstrate how a well-established allocation scheme, the Shapley value mechanism (SVM), has unfavorable consequences for flexibility-providing community members and generally does not yield solutions on this Pareto front. In contrast, we interpret dispatch cost under imperfect knowledge as being composed of two components. The first represents the cost under perfect knowledge, and the second represents the cost arising from inaccurate forecasts. Our proposed mechanism extends an SVM-based allocation of the perfect knowledge cost by allocating the remaining cost in a way that guarantees finding solutions on the Pareto front. To this end, we formulate a convex multi-objective optimization problem that can efficiently be solved as a linear or quadratic program.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"2-11"},"PeriodicalIF":3.3,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10807294","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106572","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

Event-Type Identification in Power Grids Using a Spectral Correlation Function-Aided Convolutional Neural Network 基于谱相关函数辅助卷积神经网络的电网事件类型识别

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2024-12-11 DOI: 10.1109/OAJPE.2024.3513776

Ozgur Alaca;Ali Riza Ekti;Jhi-Young Joo;Nils Stenvig

{"title":"Event-Type Identification in Power Grids Using a Spectral Correlation Function-Aided Convolutional Neural Network","authors":"Ozgur Alaca;Ali Riza Ekti;Jhi-Young Joo;Nils Stenvig","doi":"10.1109/OAJPE.2024.3513776","DOIUrl":"https://doi.org/10.1109/OAJPE.2024.3513776","url":null,"abstract":"Rapid and accurate identification of events in power grids is critical to ensuring system reliability and security. This study introduces a novel event-type identification method, utilizing a Spectral Correlation Function (SCF)-aided Convolutional Neural Network (CNN). The proposed method employs a six-stage cascaded structure consisting of: (1) data collection, (2) clipping, (3) augmentation, (4) feature extraction (FE), (5) training, and (6) testing. Real-world power grid signals sourced from the Grid Event Signature Library are used for both training and testing. To improve robustness, additive white Gaussian noise (AWGN) is introduced at various signal-to-noise ratio (SNR) levels to augment the dataset. The SCF-based FE method captures distinctive event-type characteristics by exploiting the spectral correlation of signals, allowing the CNN architecture to effectively learn and generalize event patterns. The proposed method is benchmarked against seven conventional techniques, using real-world power grid signals representing four distinct event types: blown fuse, line switching, low amplitude arcing, and transformer energization. Key performance metrics-prediction accuracy, mean absolute error (MAE), precision, recall, F1-score, and confusion matrix—are employed to evaluate the performance. Results demonstrate that the SCF-CNN method outperforms traditional approaches across all metrics and SNR levels, achieving over 99% prediction accuracy and nearly zero error for SNR values above 6 dB. This signifies its efficacy in reliable event-type identification for power grid applications.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"11 ","pages":"653-664"},"PeriodicalIF":3.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10789217","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875107","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