International Transactions on Electrical Energy Systems最新文献

{"title":"Postdisaster Distribution Network Repair Strategy Based on an Improved Artificial Bee Colony Algorithm","authors":"Jian Liu,&nbsp;Junying Gao,&nbsp;Huanhui Guo,&nbsp;Wei Wu,&nbsp;Rongyu Zhang,&nbsp;Changyun Li","doi":"10.1155/etep/6920114","DOIUrl":"https://doi.org/10.1155/etep/6920114","url":null,"abstract":"<p>Addressing the challenge of coordinated repair for multiple fault types in distribution networks during extreme natural disasters, this paper proposes an improved artificial bee colony algorithm integrating cooperative evolution and simulated annealing mechanisms. A multifault repair model is constructed by comprehensively considering weighted power loss from outages and repair team costs, while incorporating fault demand matrices and repair team collaboration relationships. The collaborative evolution mechanism decomposes the optimization task into two subproblems: resource allocation and scheduling optimization, thereby reducing complexity. Simultaneously, the simulated annealing mechanism enhances global exploration capabilities during the employed bee phase. Finally, simulations on an improved IEEE 33-node distribution system validate the effectiveness of the proposed algorithm and repair strategy.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/6920114","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safety-Aware Conditional Reinforcement Learning for Supervisory PI Gain Scheduling in Multiarea Load Frequency Control","authors":"N. V. A. Ravi Kumar,&nbsp;Dasyam Chandra Mouli,&nbsp;Malleti Sreedhar,&nbsp;Praveen Kumar Yadav Kundala,&nbsp;Vasupalli Manoj,&nbsp;Vijaya Krishna Rayi,&nbsp;S. Ramana Kumar Joga,&nbsp;Theophilus A. T. Kambo Jr.","doi":"10.1155/etep/9730957","DOIUrl":"https://doi.org/10.1155/etep/9730957","url":null,"abstract":"<p>This study presents a safety-aware conditional reinforcement learning (RL)–based supervisory proportional–integral (PI) gain scheduling scheme for robust load frequency control (LFC) of a three-area interconnected power system. Different from most RL-based LFC schemes that either replace the existing controllers or provide direct control action to actuators, the proposed scheme keeps the conventional PI controller as the only actuator-driving unit and utilizes RL as a steady-state supervisory gain tuner. A detailed nonlinear three-area LFC model, which includes a full tie-line coupling scheme, is considered. Multistep load disturbances are applied to one control area, and robustness is examined against simultaneous ±30% uncertainty in inertia, damping, governor, and turbine parameters. The proposed supervisory RL policy is implemented using an actor–critic RL algorithm, and the policy is activated only when both area control error (ACE) and its derivative are below a certain prespecified threshold. All gain changes are bounded and low-pass filtered to ensure the nonintrusive nature of the controller. Simulation results, including single-run and Monte Carlo analysis, demonstrate that the proposed controller retains the transient response and control effort characteristics of the particle swarm optimization (PSO)–tuned PI baseline while achieving an improved steady-state frequency distribution with a lower spread under uncertainty. It is demonstrated that RL can provide useful benefits in LFC when it is used as a conditional steady-state supervisory tool instead of a direct transient controller.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/9730957","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Multihead Self-Attention Mechanism in Convolutional Neural Networks for Enhanced Vibration-Based Prediction of Transformer Fault","authors":"Fatemeh Ghanadian,&nbsp;Amin Zollanvari,&nbsp;Venera Nurmanova,&nbsp;Mehdi Bagheri","doi":"10.1155/etep/7093491","DOIUrl":"https://doi.org/10.1155/etep/7093491","url":null,"abstract":"<p>Transformer faults can cause power outages, equipment damage, and costly repairs, making early detection critical. Traditional diagnostic methods like dissolved gas analysis (DGA) and frequency response analysis (FRA) are useful, but they often struggle with noisy, overlapping signals, and varying operating conditions. Vibration analysis provides a noninvasive, real-time alternative for monitoring transformer health although analyzing these signals is difficult because of external noise and complex fault patterns. The present study explores using deep learning models to predict faults from vibration time-series data, with a focus on a hybrid architecture that combines convolutional neural networks (CNNs) and multihead self-attention (MHSA). CNNs effectively extract local signal features, while MHSA captures long-range dependencies and global patterns. This combination enables the model to attend to the most relevant parts of the signal and suppress noise. Interestingly, our results also reveal that when CNNs are used as feature extractors, adding the Transformer Encoder’s feedforward network (FFN) does not improve performance and can even slightly degrade it. Overall, our experiments show that the CNN–MHSA model consistently outperforms other architectures, including standalone CNNs, MHSA, Transformer Encoders, and CNN–Transformer Encoder hybrids. In terms of relative absolute error (RAE), the CNN–MHSA model achieves consistent improvements over the baseline models. In particular, the proposed model achieved an average reduction in RAE across all evaluated CNN configurations within the considered hyperparameter space, ranging from 0.5% to 1.3% for excitation voltage prediction and up to 3.7% for load current prediction—these improvements are statistically significant (<i>p</i> &lt; 0.05), as determined by a one-sided paired <i>t</i>-test. In addition to model development, this study provides a systematic and comprehensive comparative evaluation of multiple architectures, including standalone CNNs, MHSA, Transformer Encoders, and hybrid models under consistent experimental settings. Overall, our findings demonstrate that integrating CNNs with attention mechanisms provides a powerful approach for accurate and reliable transformer fault prediction.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/7093491","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-Stage Stochastic Unit Commitment for AC–DC Hybrid Grids With Vehicle-to-Grid Flexibility and Renewable Integration","authors":"Aamir Nawaz,&nbsp;Rizwan Ahmed,&nbsp;Aftab Ahmed Almani,&nbsp;Suhail Ahmed Almani,&nbsp;Zahid Javid,&nbsp;William Holderbaum","doi":"10.1155/etep/7560415","DOIUrl":"https://doi.org/10.1155/etep/7560415","url":null,"abstract":"<p>The connection of multiterminal HVDC systems to AC networks, along with growing penetration of renewable energy sources (RES) and vehicle-to-grid (V2G) flexibility, creates new challenges for power system operation. This paper presents a two-stage stochastic unit commitment model that jointly integrates AC and DC network operations with EV aggregator flexibility, minimizing expected system cost under renewable uncertainty. The model is formulated as a Mixed Integer Linear Program (MILP) based on linearized DC power flow with a piecewise-linear HVDC converter loss representation. The first stage determines day-ahead commitment, reserve allocation, and converter scheduling; the second stage optimizes real-time dispatch recourse for each renewable scenario. N-1 security constraints ensure postcontingency feasibility. An enhanced Benders decomposition with Magnanti–Wong cut strengthening and adaptive regularization is developed as a solution framework intended for transmission-scale instances; for the distribution-scale test cases studied here, the deterministic-equivalent MILP is solved directly to a 0.5% gap. The scenario reduction technique based on the Wasserstein distance ensures faithful uncertainty representation. Numerical experiments on modified IEEE 33-bus and 123-bus AC–DC hybrid distribution test systems demonstrate cost reductions of 16.4% and 28.5% relative to a deterministic AC baseline, while an ablation study decomposes contributions from stochastic modeling (4.4%), AC–DC coordination (11.0%), and EV flexibility (1.0%). For the 123-bus system, HVDC coordination dominates with a 22.9% cost reduction. A congested-network sensitivity analysis confirms nonzero curtailment under tight line limits, and a tightened-reserve study isolates the V2G contribution as a genuine reserve substitution mechanism rather than slack absorption. An additional scenario-count sensitivity and a head-to-head comparison against deterministic, robust, and Wasserstein distributionally robust formulations confirm the validation robustness of the proposed approach.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/7560415","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Current Control of Three-Phase Inverter Using a Data-Driven Model-Free Adaptive Controller for Power Quality Improvement","authors":"Behnam Amini,&nbsp;Ahmad Hajipour,&nbsp;Reza Roshanfekr","doi":"10.1155/etep/7269223","DOIUrl":"https://doi.org/10.1155/etep/7269223","url":null,"abstract":"<p>This paper introduces a data-driven model-free adaptive controller (DDMFAC) as a powerful, practical, and suitable alternative to improve the limitations, challenges, and inherent problems of conventional model–based methods. The proposed method, using input–output data processing independent of the mathematical and systematic model, acts as an effective solution to overcome the limitations and problems caused by complex system modeling and handling large volumes of data. By employing this controller, the overall design becomes significantly simplified and requires no special modeling efforts. The proposed DDMFAC offers several distinctive advantages, including the absence of system identification, online parameter estimation, or state prediction requirements. It features a simple, stable, robust, and adaptive structure; offers relatively low implementation cost; imposes a lower computational burden compared to conventional controllers; and operates independently of the system’s structure and dynamics. These characteristics ensure stable control performance and make it a highly practical solution for industrial systems, devices, and processes. The proposed DDMFAC was implemented in a real-time testbench DSP-based three-phase inverter configured as a shunt active power filter (SAPF) for current control. Also, to validate its effectiveness, several simulation and experimental results are discussed. Using the compensation of the presented method, the total harmonic distortion (THD) of the grid side current was reduced significantly from 21.8% to 2.7%. Also, various verified results of tests demonstrate the effectiveness and superiority of the proposed approach.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/7269223","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finite-Time Disturbance Observer–Based Generalized Predictive Control of a SEPIC Converter in DC Microgrids Under Constant Power Load","authors":"Ehsan Allahmoradi","doi":"10.1155/etep/7920700","DOIUrl":"https://doi.org/10.1155/etep/7920700","url":null,"abstract":"<p>This paper presents a generalized predictive control (GPC) strategy for a SEPIC converter designed for DC microgrid applications. Unlike conventional model predictive controllers that rely on online optimization—often resulting in significant computational complexity—the proposed control framework leverages an offline optimization approach, substantially reducing the computational burden while preserving predictive performance. Considering the challenges posed by constant power loads (CPLs), including their nonlinear negative impedance behavior, as well as dynamic uncertainties such as input voltage disturbances, unknown converter dynamics, and parameter variations due to thermal effects in inductors and capacitors, a finite-time disturbance observer (FTDO) is integrated into the control loop. This observer enables accurate and fast estimation of external disturbances and unmodeled dynamics within a finite time, thereby enhancing the robustness and stability of the predictive control framework without significantly increasing the computational load. The closed-loop stability of the overall system is rigorously verified using Lyapunov-based analysis. The effectiveness of the proposed control strategy is validated through comprehensive MATLAB/Simulink simulations under various operating scenarios. Comparative evaluations against classical model predictive control (MPC) and sliding mode control (SMC) demonstrate the superiority of the proposed approach in terms of dynamic performance, robustness, and disturbance rejection.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/7920700","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148467287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Sizing of Vanadium Redox Flow Batteries for Microgrids: A Joint Consideration of Energy and Power Ratings","authors":"Mahmoud Mollayousefi Zadeh,&nbsp;Mohammadreza Toulabi,&nbsp;Seyed Mohammad Taghi Bathaee","doi":"10.1155/etep/9530972","DOIUrl":"https://doi.org/10.1155/etep/9530972","url":null,"abstract":"<p>The integration of renewable energy into microgrids is crucial for sustainability and resilience, but its variability requires robust energy storage. This study investigates the optimal sizing of vanadium redox flow battery (VRFB) systems, simultaneously considering both energy and power ratings. Our analysis demonstrates that an optimally sized VRFB (800 kW/6.5 MWh) reduces annual microgrid costs from $224,528 to $221,450 through enhanced energy arbitrage and better renewable utilization. Furthermore, we examine how varying storage sizes impact costs. The results show a trade-off: While investment costs rise linearly with storage size, operational costs decrease due to improved efficiency. However, these operational savings saturate when generation units are power-limited, underscoring the necessity of a balanced power–energy capacity. This research provides key insights for designing cost-effective and resilient VRFB-supported microgrids.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/9530972","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148467316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Temporal Observability on the Identifiability and Validity of 2RC Battery Model Parameters for INR21700-45E Li-Ion Cell","authors":"Arvind Yadav,&nbsp;Mustafa Abdullah,&nbsp;Anand Kr Shukla,&nbsp;Spoorthi B.,&nbsp;Tanmoy Prida,&nbsp;Gaganjot Kaur,&nbsp;Gurumurthy Ramaiah","doi":"10.1155/etep/7807141","DOIUrl":"https://doi.org/10.1155/etep/7807141","url":null,"abstract":"<p>This paper investigates parameter identification for second-order equivalent circuit models of lithium-ion batteries using test protocols of different durations. A commercially available INR21700-45E (NMC) cell is characterized using two segments from hybrid pulse power characterization (HPPC) testing: a 1 h relaxation window and a 6 min discharge window. Parameters are extracted through particle swarm optimization combined with direct resistance calculation. Validation is performed under independent operating conditions, including 2C HPPC pulses and C/2 constant-current discharge. Comparative results indicate that the 1 h relaxation window yields parameters with significantly lower validation errors, achieving RMSE values of 21.2 mV (2C HPPC) and 29.9 mV (C/2 discharge), with <i>R</i>² &gt; 0.98. In contrast, the 6 min discharge window produces parameters with substantially higher errors, particularly under continuous discharge (RMSE = 100.8 mV, <i>R</i>² = 0.8045), revealing condition-dependent limitations. The findings highlight the role of test duration in influencing parameter estimation and model performance, offering quantitative insights for battery test protocol design. This is an open access article under the terms of the Creative Commons Attribution-Noncommercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/7807141","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148467317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Investigation of Dust Deposition Patterns and Electric Field Distortion on Insulators Based on Finite Element Simulation","authors":"Fangxing Lyu,&nbsp;Jun Yang,&nbsp;Jian Ding,&nbsp;Liangzhi Su,&nbsp;Xin Fang,&nbsp;Lan Shen","doi":"10.1155/etep/5526613","DOIUrl":"https://doi.org/10.1155/etep/5526613","url":null,"abstract":"<p>In dust-prone regions such as Northwest China, dust accumulation on insulators distorts the electric field, which is a key cause of flashovers and performance degradation. However, existing studies have mainly focused on the chemical properties of contaminants or equivalent salt deposit density. As a result, the physical mechanism by which the geometric morphology of dust deposits independently affects the electric field distribution remains poorly understood. To address this gap, this study employs finite element simulation to systematically investigate the contact angle <i>θ</i> at the dust–insulator interface as a key geometric parameter governing electric field distortion. An electrostatic field model of the XP-70 disc-type suspension insulator was established to simulate and analyze electric field distributions under different dust accumulation morphologies. Findings indicate that when <i>θ</i> approaches 90°, severe electric field distortion occurs in the contact region; conversely, when <i>θ</i> approaches 0° or 180°, the electric field distribution becomes more uniform with significantly reduced distortion. This study provides a quantitative analysis of the correlation between contact angle and electric field distortion in dust accumulation patterns, offering a new perspective on the distortion mechanism caused by dust contamination. It offers a potential theoretical basis for antiflashover design and structural optimization of insulators in severe dust environments, holding significant value for ensuring power grid operational safety.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/5526613","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148467108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hierarchical Multimodal AI Framework for Intelligent Alarm Handling in Smart Power Grid Monitoring Systems","authors":"Tairan Li,&nbsp;Qingpeng Wang,&nbsp;Gao Yang,&nbsp;Qiang Huang,&nbsp;Qingqing Ren,&nbsp;Hao Yang","doi":"10.1155/etep/5528391","DOIUrl":"https://doi.org/10.1155/etep/5528391","url":null,"abstract":"<p>Recent developments and the rapid expansion of modern power grids have led to alarm flooding. This means that operators receive such a large volume of alarms that they become overwhelmed and stall in inquiry, resulting in mistakes. To better address this issue, an innovative, multifaceted, and hierarchical framework is proposed that combines stability signals and failure event information for intelligent alarm classification and screening. The UCI electrical grid stability and the power system faults datasets, both open source datasets, are used in conjunction with the synthetic multimodal fusion dataset for comprehensive performance evaluation. We benchmark baseline models, including random forest, extreme gradient boosting, and CNN–LSTM, on unimodal datasets. The CNN–LSTM gives us the best temporal stability prediction. At the same time, the XGB has the best heterogeneous fault classification. The proposed hierarchical multimodal model achieves an accuracy of 0.97, an F1 score of 0.96, and an ROC-AUC of 0.98 for the combination of anomaly detection and hybrid CNN–LSTM–XGB fusion. These results demonstrate that hierarchical multimodal learning provides a scalable, intelligent solution for alarm classification and screening in next-generation cyberphysical power grid systems.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/5528391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}