International Journal of Electrical Power & Energy Systems最新文献

{"title":"A novel high-voltage fault-tolerant permanent magnet synchronous generator for far offshore wind turbines","authors":"Pengzhao Wang ,&nbsp;Xiangjun Zeng ,&nbsp;Yiping Luo","doi":"10.1016/j.ijepes.2025.110662","DOIUrl":"10.1016/j.ijepes.2025.110662","url":null,"abstract":"<div><div>Cost-effective and highly reliable wind generator systems are crucial for reducing the levelized cost of energy of far offshore wind farms. However, conventional three-phase wind generators with low output voltages necessitate complex power conversions and expensive offshore converter stations. This study proposes a novel high-voltage fault-tolerant permanent magnet synchronous generator (HVFTPMSG) to address this issue. Benefiting from a specially designed high-voltage coil and modular stator, the HVFTPMSG elevates the output voltage to approach HVDC transmission levels and exhibits excellent magnetic isolation performance. This work highlights the key design considerations of the HVFTPMSG and elaborates on its design and optimization methods using a 10 MW HVFTPMSG design example. A multiphysics coupling numerical model is developed to comprehensively evaluate the electromagnetic characteristics, thermal distribution, and electric field strength distribution of the design example. The design example optimized by the NSGA-III algorithm is compared with conventional generators of the same power rating regarding mass, cost, and efficiency. Furthermore, a scaled-down high-voltage coil prototype is developed to validate its insulation performance. The results indicate that the proposed HVFTPMSG is expected to be a competitive candidate for far offshore wind power applications.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110662"},"PeriodicalIF":5.0,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of distribution grid line parameters using smart meter data with missing measurements","authors":"Shubhankar Kapoor ,&nbsp;Adrian G. Wills ,&nbsp;Johannes Hendriks ,&nbsp;Lachlan Blackhall","doi":"10.1016/j.ijepes.2025.110634","DOIUrl":"10.1016/j.ijepes.2025.110634","url":null,"abstract":"<div><div>Grid models, including line impedances, are crucial for the active management and operation of the distribution grid (DG). This paper introduces a novel approach for estimating DG line parameters using available voltage magnitude and node powers from smart meters (SMs), specifically addressing scenarios with missing measurements. We propose an expectation–maximization (EM) based approach and validate the results on an IEEE 37-node network, achieving accurate estimates for line parameters, voltage magnitude, and active/reactive power at nodes. The method is tested with varying levels of missing measurements and noise. Two cases of missing measurements are considered: random and specific node-based. The latter case is used to infer the optimal placement of measurement devices. Additionally, the proposed method is validated on simulated data and real-world consumer loads, consistently providing accurate results.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110634"},"PeriodicalIF":5.0,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cyber-physical coupled modelling for distributed coordination control of a new distribution system","authors":"Wei Liu ,&nbsp;Yufei Wang ,&nbsp;Wei Gu ,&nbsp;Xi Chen ,&nbsp;Dehu Zou ,&nbsp;Xuechang Yu ,&nbsp;Zhihao Qian","doi":"10.1016/j.ijepes.2025.110644","DOIUrl":"10.1016/j.ijepes.2025.110644","url":null,"abstract":"<div><div>The peer-to-peer neighbour information exchange of the multi-agent system (MAS) and the update of their states may cause communication delays and even control errors. Hence, it is necessary to analyze distributed coordination control (DCC) issues influenced by communication conditions. To address these problems, this study proposes a cyber-physical coupled modelling (CPCM) method for MAS-based new distribution system (NDS). Firstly, the proposed method establishes a unified adjacency matrix model that covers the coupled power system model, communication system model and agent model. It also utilizes MAS-based DCC for processing information of primary and secondary control agents. Then, communication delays in the coupling process of each layer are calculated through hybrid computation, and the impacts of communication delay, congestion, and errors on DCC are analyzed. Finally, a simulation model is built in MATLAB/Simulink to demonstrate the effectiveness of the proposed CPCM and DCC methods.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110644"},"PeriodicalIF":5.0,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Voltage vulnerability curves: Data-driven dynamic security assessment of voltage stability and system strength in modern power systems","authors":"Aleksandar Boričić ,&nbsp;Marjan Popov","doi":"10.1016/j.ijepes.2025.110636","DOIUrl":"10.1016/j.ijepes.2025.110636","url":null,"abstract":"<div><div>Power systems evolve towards more renewable and less conventional electricity supply. This, however, brings significant technical challenges, as conventional sources naturally provide system resilience. One of the key dimensions of this resilience is system strength, which is rapidly depleted with the phase-out of fossil-based synchronous generation. This paper commences by exploring the intricate steady- and dynamic-state aspects of system strength, and consequently elevated risks of voltage instability. A new holistic definition of system strength is further proposed. Considering the stability challenges of modern power systems, grid operators need to be aware of any vulnerable grid sections and dangerous operating scenarios to always ensure system security and stability. Nevertheless, the rising complexity of modelling and analysis of dynamics in modern power systems makes this task increasingly challenging. The large number of grid locations with complex inverter-based generation and load, paired with parameter uncertainty, make deterministic analytical analyses of voltage stability and system strength increasingly challenging and time-consuming. A novel data-driven voltage stability and system strength assessment method, termed Voltage Vulnerability Curves (VVCs), is hereby proposed to address these challenges. The method is designed to cut through the complexity of modern power systems’ dynamics and provide advanced system strength and voltage vulnerability insights.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110636"},"PeriodicalIF":5.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate detection of critical LLFs and LGFs in PV arrays based on deep reinforcement learning using proximal policy optimization (PPO)","authors":"Sherko Salehpour ,&nbsp;Aref Eskandari ,&nbsp;Amir Nedaei ,&nbsp;Mohammad Gholami ,&nbsp;Mohammadreza Aghaei","doi":"10.1016/j.ijepes.2025.110661","DOIUrl":"10.1016/j.ijepes.2025.110661","url":null,"abstract":"<div><div>Critical line-to-line faults (LLFs) and line-to-ground faults (LGFs) in photovoltaic (PV) systems are the most difficult faults to detect not only by conventional protection devices, but also modern fault detection schemes. The difficulty occurs due to critical mismatch levels and/or high fault impedance values which result in LLFs and LGFs remain undetected thus damaging the PV components, affecting system stability, reliability, and efficiency, and even leading to catastrophic fire hazards. However, challenges persist even in recent studies, including the need for a massive training dataset, disregard of fault severity assessment, and insufficient model accuracy. To address these challenges, the present paper proposes a deep reinforcement learning (DRL)-based model to detect, classify, and assess the severity of all and specifically critical LLFs and LGFs in PV arrays using the proximal policy optimization (PPO) algorithm. Additionally, to carry out the dataset dimensionality reduction, thus simplifying the training process, a two-stage feature engineering process has been implemented, including a feature importance finding stage using the permutation technique and a feature selection stage. To implement the proposed model and verify its capability in real-life condition, a laboratory PV system has been carefully designed. The results of the real-world experiment shows that the proposed model is able to detect LLFs and LGFs, under various environmental (temperature and irradiance), and electrical (mismatch and impedance) conditions with outstanding 100% of accuracy in the test process, using only a small training dataset.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110661"},"PeriodicalIF":5.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SafetyGPT: An autonomous agent of electrical safety risks for monitoring workers’ unsafe behaviors","authors":"Wei Li ,&nbsp;Fuqi Ma ,&nbsp;Zhiyuan Zuo ,&nbsp;Rong Jia ,&nbsp;Bo Wang ,&nbsp;Abdullah M Alharbi","doi":"10.1016/j.ijepes.2025.110672","DOIUrl":"10.1016/j.ijepes.2025.110672","url":null,"abstract":"<div><div>Workers’ unsafe behavior is one of the major causes of accidents in electric power production. Intelligent monitoring of workers’ unsafe behaviors can effectively prevent the expansion of safety risks, thereby blocking the development process of risks to accidents. Electric power production processes are diverse in nature and require the frequent switching of operating scenarios. This makes it difficult to identify what is “unsafe” since worker behaviors within the given electrical context also exhibit variability and diversity. Existing methods have insufficient generalization and adaptability, which makes them inadequate for the case of electric power production. Therefore, this paper proposes Safety Generative Pre-trained Transformers (SafetyGPT), an autonomous agent of safety risk based on a multi-modal large language model, which incorporates a human–machine collaborative monitoring mode for unsafe behaviors of workers. SafetyGPT loads the electric power production video, and the backend supervisors set instructions for SafetyGPT based on task requirements. The model encodes visual and textual features into corresponding tokens, realizes multi-modal feature alignment and fusion through the cross-attention mechanism, and then generates targeted responses through the large language model. Next, the proposed method is applied to real production site data to confirm the effectiveness and superiority through comparison with other methods designed to identify unsafe behaviors. Experimental results show that the accuracy of the proposed method for the identification of unsafe behaviors in complex environments is 96.5%, and that it can generate reasonable recommended plan based on the identification results, assist backend supervisors in making decisions, and effectively improve the safety level of power production.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110672"},"PeriodicalIF":5.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A fast SOC balancing control strategy for distributed energy storage system based on sinusoidal signal injection","authors":"Ling Yang ,&nbsp;Jiahao Luo ,&nbsp;Junhao Liao ,&nbsp;Xutao Wen ,&nbsp;Chongyao Yuan ,&nbsp;Yu Wang ,&nbsp;Dongtao Luo ,&nbsp;Marta Molinas ,&nbsp;Olav Bjarte Fosso","doi":"10.1016/j.ijepes.2025.110678","DOIUrl":"10.1016/j.ijepes.2025.110678","url":null,"abstract":"<div><div>In this paper, a fast state-of-charge balancing strategy for distributed energy storage system based on injected sinusoidal signals is proposed, which solves the problems of unbalanced state-of-charge, unreasonable load current sharing, and unstable direct current bus voltage. Firstly, the state-of-charge of distributed energy storage unit is directly combined with the reference current of the current closed-loop by using the arc-sin function, and two acceleration factors are set to realize rapid state-of-charge balance. Secondly, the frequency of the injected sinusoidal signals is constructed to be inversely proportional to the direct current output current of the distributed energy storage unit, which frees from the constraints of the framework of droop control and overcomes the limitations of conventional droop control. Then, the phase difference between the injected sinusoidal signals forms a reactive power circulation, which enables the output current of the distributed energy storage unit to be proportionally shared by its capacity without communication, reducing the cost of system communication. In addition, the bus voltage can be effectively compensated by designing the limiter link and virtual negative impedance. Finally, the feasibility and effectiveness of the proposed strategy are verified by experiments.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110678"},"PeriodicalIF":5.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GNN assisted frequency constrained unit commitment of multi-region power systems with high penetration of renewable energy sources","authors":"Mingjian Tuo ,&nbsp;Cunzhi Zhao ,&nbsp;Mulan Zhang ,&nbsp;Tao Gao ,&nbsp;Xuequan Shang","doi":"10.1016/j.ijepes.2025.110670","DOIUrl":"10.1016/j.ijepes.2025.110670","url":null,"abstract":"<div><div>The increasing integration of renewable energy resources (RES) into the power grid poses significant challenges in system frequency dynamics. Traditional frequency-constrained unit commitment models simplify the average system frequency and neglect the spatial characteristics of frequency dynamics, potentially underestimating the risk of contingencies. In this paper, we consider a nodal frequency response model to capture the frequency dynamics in the unit commitment problem. Nodal frequency dynamics and rate of change of frequency (RoCoF) expressions are converted into security constraints against worst contingency, which are then incorporated into the proposed muti-region frequency-constrained unit commitment (MR-FCUC) formulations. To improve the efficiency and performance of the MR-FCUC model, a decomposition algorithm is implemented to solve the proposed MR-FCUC efficiently. The subproblem of the original model confirms the frequency dynamics, and sensitivity cuts are refined based on the validation errors. Additionally, a GNN-based voltage phase angle predictor is incorporated to boost the computational efficiency of the FCUC model. Case study involving modified IEEE 24-bus and IEEE 118-bus systems illustrates the effectiveness of the proposed GNN-MR-FCUC model. Simulation results of test systems affirm that the frequency stability is guaranteed: the maximal RoCoF is mitigated within 0.5 Hz/s, and the lowest frequency nadir is maintained above 59.71 Hz.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110670"},"PeriodicalIF":5.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental evaluation of an advanced predictive control technique for variable-speed wind turbine systems","authors":"Farah Echiheb ,&nbsp;Btissam Majout ,&nbsp;Ismail EL Kafazi ,&nbsp;Badre Bossoufi ,&nbsp;Abdelhamid Rabhi ,&nbsp;Nicu Bizon ,&nbsp;Anton Zhilenkov ,&nbsp;Saleh Mobayen","doi":"10.1016/j.ijepes.2025.110668","DOIUrl":"10.1016/j.ijepes.2025.110668","url":null,"abstract":"<div><div>Wind energy control plays a crucial role in optimizing the performance of Doubly Fed Induction Generators (DFIGs) by maximizing power extraction while ensuring stable grid integration. To achieve this, a Maximum Power Point Tracking (MPPT) strategy is employed to determine the optimal mechanical speed and reference power, enabling efficient wind energy conversion. However, maintaining precise control over the active and reactive power exchange remains a challenge, especially under varying operating conditions. This paper presents an experimental study on the application of deadbeat predictive control to a DFIG-based wind energy system, integrating MPPT for optimal power tracking. The study, conducted using a DSPACE DS1104 test bench, includes the development of a comprehensive mathematical model, an analysis of the deadbeat control strategy, and its implementation in MATLAB/Simulink. Experimental validation demonstrates that the proposed control method achieves faster response time (0.0504 s), reduced Total Harmonic Distortion (THD) to 0.5 %, and enhanced robustness against parameter variations, ensuring both maximum power extraction and high-quality power injection into the grid. These results confirm the superiority of the MPPT-integrated deadbeat predictive control over conventional methods in terms of efficiency, power quality, and system stability. However, while this method shows promising results, its implementation in real-world, large-scale systems requires further investigation to address challenges such as grid stability under fluctuating conditions and the scalability of the control strategy. In terms of practical implications, the proposed control method offers potential for improving the performance and efficiency of DFIG-based wind energy systems, contributing to more sustainable and reliable energy production. The research also holds social implications by advancing renewable energy technologies, which are essential for reducing dependency on fossil fuels and mitigating the effects of climate change.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal charging station placement of electric vehicles in the smart distribution network based on the mixed integer linear programming","authors":"Mehdi Veisi ,&nbsp;Hossein Naderian ,&nbsp;Mazaher Karimi","doi":"10.1016/j.ijepes.2025.110675","DOIUrl":"10.1016/j.ijepes.2025.110675","url":null,"abstract":"<div><div>This article discusses the optimal placement of electric vehicle charging stations in the distribution network. The proposed approach is an optimization problem with the objective function equal to minimizing the cost of building charging stations and energy costs. Inevitably, minimizing the voltage deviation from the desired (reference) value is also considered in the objective function. The constraints of this problem include the equations of power flow, the restrictions governing electric vehicles and charging stations, and the limitations of network indicators. The mentioned problem can be described as mixed integer nonlinear programming (MINLP). Nevertheless, the MINLP optimization problem tends to run very slowly when the dimension of the grid increases significantly, and that’s why it is unlikely that we obtain an absolute optimal solution. Consequently, a mixed integer linear programming (MILP) formulation that resembles the main problem is developed. Ultimately, a distribution network consisting of 69 buses is modeled in GAMS to evaluate the proposed formulation. In the proposed plan with the appropriate placement of electric vehicle charging stations, initially a favorable economic cost is obtained for the aforementioned stations. In the following, charging management at the aforementioned stations has caused the network operation status to improve. When EVs are absent, the maximum voltage drop is approximately 0.092p.u. and energy losses reach 2.08  MW. In contrast, with EVs present the voltage drop falls to about 0.037p.u. and energy losses drop to roughly 1.23  MW.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110675"},"PeriodicalIF":5.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}