IET Smart Grid最新文献

{"title":"Quantitative Analysis Method of Stability Ability for Renewable Electric Energy Delivery System","authors":"Jiawang Xiong,&nbsp;Wei Pei,&nbsp;Guanqi Wang,&nbsp;Wei Deng,&nbsp;Hao Xiao","doi":"10.1049/stg2.70077","DOIUrl":"https://doi.org/10.1049/stg2.70077","url":null,"abstract":"<p>One mainstream approach to alleviate insufficient voltage support in renewable-energy power delivery systems is to introduce a portion of grid-forming (GFM) inverters. However, the distinct characteristics of grid-following (GFL) and grid-forming (GFM) controls complicate the quantitative assessment of system stability capability. To address this issue, this paper proposes an amplitude mapping-based method for quantitatively evaluating stability capability, yielding a simple and practical analytical expression. Specifically, we introduce the idea of assessing system stability through waveform-amplitude stability. Based on this concept, the mathematical model of the renewable-energy delivery system is established, and the expression of the stability capability index (SA) is derived. Next, the impacts of key parameters on SA are analysed, and the critical GFL–GFM power ratio is quantified. Finally, the theoretical findings are validated through case studies.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70077","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696286","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}

{"title":"Optimum Operation of Unbalanced AC Microgrid Using PEVs and DERs Scheduling With Time-Varying Loads","authors":"Zeeshan Yousaf,&nbsp;Muhammad F. Nadeem,&nbsp;Ali Ahmed,&nbsp;Muhammad Nouman,&nbsp;Muhammad Akmal","doi":"10.1049/stg2.70081","DOIUrl":"https://doi.org/10.1049/stg2.70081","url":null,"abstract":"<p>The studies pertaining to scheduling of plug-in electric vehicles (PEVs) and distributed energy resources (DERs) in unbalanced AC microgrids (MGs) usually consider time varying lumped load models. This may lead to unrealistic values and inappropriate system support as unbalanced MGs have different loading levels for each phase. Therefore, in this paper optimal scheduling of DERs and PEVs is performed in 3-phase unbalanced AC MG whilst considering different per-phase loading levels of time-varying voltage dependent (TVVD) load models. At first, the impact of lumped loading and per phase loading approach on DERs scheduling in unbalanced AC MG is investigated for TVVD load models. Then, PEVs are connected in presence of DERs whilst considering per phase loading in case of all TVVD loads to minimise the generation cost and system losses. Enhanced grasshopper optimization algorithm (EGOA) is utilised to evaluate the optimum performance of unbalanced AC MG. The results demonstrate that the values obtained through a per-phase loading approach are more realistic and PEVs integration has a significant impact on cost reduction.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696285","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}

{"title":"Fault Identification Method for Distribution Networks Based on Time–Frequency Spatial Fusion Matrix","authors":"Weiping Liao,&nbsp;Weiping Wang,&nbsp;Aihui Wen,&nbsp;Xinhai Li,&nbsp;Xin Li,&nbsp;Chuang Meng","doi":"10.1049/stg2.70079","DOIUrl":"https://doi.org/10.1049/stg2.70079","url":null,"abstract":"<p>To address the issues of confusing local measurement point features and insufficient generalisation ability of traditional models in distribution network fault identification, this paper proposes a fault identification method integrating an optimisation mechanism and a gradient boosting model. First, based on the spatiotemporal propagation characteristics of fault signals, the method analyses the differences in propagation of travelling wave signals generated by operational disturbances and faults in the power grid. To accurately quantify the aforementioned spatiotemporal propagation laws, it extracts time–frequency domain statistical features from multiple measurement points across the entire network. These features include modal energy, central frequency, spectral entropy, frequency change rate, and sparsity index and constructs an anticonfusion feature matrix. Subsequently, the Whale Optimisation Algorithm is used to dynamically adjust the hyperparameters of XGBoost, thereby establishing the WO-XGBoost identification model. This enhances the accuracy and speed of the model during the identification process, enabling accurate identification of distribution network faults. Experimental results show the proposed method outperforms mainstream existing methods in both identification accuracy and training efficiency, offering reliable technical support for distribution network fault identification.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147666229","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}

{"title":"Optimisation Operation Strategy for Cascaded Small Hydropower Aggregation Virtual Power Plants Considering Hydro-Logical Processes","authors":"Nianchen Zheng,&nbsp;Yingjing He,&nbsp;Yangqing Dan,&nbsp;Kehao Yang,&nbsp;Yongzhi Zhou","doi":"10.1049/stg2.70049","DOIUrl":"https://doi.org/10.1049/stg2.70049","url":null,"abstract":"<p>Unlike large-scale hydropower plants, small hydropower stations typically have limited or even no reservoir capacity, making their flexibility in regulation highly sensitive to meteorological variability. The hydrological dynamic propagation across cascaded small hydropower stations significantly affects their output and inter-station coordination, which has often been overlooked in existing scheduling models. To address this gap, this paper proposes an optimised dispatch strategy for a virtual power plant (VPP) that aggregates cascaded small hydropower units with explicit modelling of hydrological processes. First, a flexibility quantification framework is developed by integrating reservoir and river channel storage capacities through the Xin'anjiang rainfall–runoff model and the Muskingum flow routing method, capturing the rainfall runoff and hydrological coupling within cascaded systems. Then, considering multiple uncertainties—precipitation, wind and solar power—the net load is used to represent their joint influence, and a Wasserstein distance-based distributionally robust stochastic optimisation model is established to manage these uncertainties. Finally, a case study based on a real river basin demonstrates that the proposed method significantly enhances operational performance: the VPP's total operating cost is reduced by 22.6%, net revenue increases by 57.1%, curtailment and load-shedding costs decrease by 9.5% and flexibility deficit costs are reduced by 68.0% compared with conventional scheduling. These results confirm that the proposed framework effectively leverages the unique flexibility of cascaded small hydropower and provides a robust and tractable solution for VPP operation under uncertainty.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568147","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}

{"title":"Bi-Level Optimisation of Topology and Energy Management for Distributed Coordinated Microgrid Clusters in Port Areas","authors":"Yueyang Xu,&nbsp;Haijun Fu,&nbsp;Qiran Liu,&nbsp;Changli Shi,&nbsp;Jingyuan Yin,&nbsp;Tongzhen Wei","doi":"10.1049/stg2.70053","DOIUrl":"https://doi.org/10.1049/stg2.70053","url":null,"abstract":"<p>High penetration of renewable energy sources in multi-energy port systems leads to significant challenges in scheduling complexity, making it difficult to manage with a single microgrid. To address this issue, this paper focuses on applying distributed coordinated microgrid clusters to port scenarios, developing a port system architecture composed of multiple regions and microgrids. A bi-level optimisation scheme for topology and energy management is proposed to achieve efficient energy utilisation within the port microgrid system. The bi-level optimisation model comprises two layers: the upper-level topology optimisation aims at minimising construction costs and busbar stress, whereas the lower-level optimisation targets capacity allocation and energy management to minimise operational and maintenance costs. An improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilised to solve the optimisation problem. Additionally, the model accounts for the uncertainty associated with renewable energy generation. Simulation results demonstrate that the proposed optimisation approach satisfies local renewable energy integration requirements and meets port load demands. It exhibits notable advantages in terms of economic efficiency, renewable energy resource requirements, equipment capacity configuration, and hydrogen energy storage lifespan. Moreover, the optimised system maintains high self-sufficiency under extreme operational scenarios.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567647","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}

{"title":"Interpretable Machine Learning-Based Intelligent High-Frequency Static Security Adjustment Method for Power System Operations","authors":"Shiqi Duan,&nbsp;Xinyu Liu,&nbsp;Yu Lei,&nbsp;Juan Yu,&nbsp;Yanan Yu,&nbsp;Zhifang Yang","doi":"10.1049/stg2.70074","DOIUrl":"https://doi.org/10.1049/stg2.70074","url":null,"abstract":"<p>The high penetration of renewable energy exacerbates stochastic fluctuations on both the generation and demand sides. It results in spatiotemporally frequent static security violations and requires high-frequency assessment and adjustment of the operation mode. However, conventional offline verification and manual adjustment methods fail to meet this requirement. Therefore, this paper proposes an interpretable machine learning-based intelligent high-frequency static security adjustment method for power system operations. First, a multitask XGBoost collaborative framework-based static security analysis model is developed by utilising power flow characteristics under the base case (<i>N</i> state) and various <i>N</i> − 1 contingency scenarios (<i>N</i> − 1 states) as input–output features. It can efficiently and accurately evaluate the power flow violation status under both the <i>N</i> state and various <i>N</i> − 1 states. Second, when operational modes exhibit power flow violation status, an intelligent adjustment methodology based on the SHAP (SHapley Additive exPlanations) interpretability framework is developed to quantify input feature contributions to security risks and identify key factors and their causality-driven mechanisms. Then, it generates transparent decision logic and effective adjustment strategies. Finally, case studies on the IEEE 30-bus system, a practical 341-bus grid and a practical 1834-bus grid validate that the proposed method not only achieves efficient and accurate static security assessment but also provides interpretable adjustment schemes for diverse operating states, thereby offering operators clear decision-making guidance.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566780","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}

{"title":"Optimal Coordination of All-Electric Buildings for Providing Energy Flexibility and Frequency Regulation Services in Electricity Markets","authors":"Sevda Zeinal Kheiri,&nbsp;Mohammad Amin Mirzaei,&nbsp;Masood Parvania","doi":"10.1049/stg2.70078","DOIUrl":"https://doi.org/10.1049/stg2.70078","url":null,"abstract":"<p>Electrified buildings with intelligent systems are becoming controllable energy resources capable of providing flexibility to the grid while supporting larger carbon reduction efforts. This paper presents an optimal coordination model for a community of all-electric buildings to offer energy flexibility and regulation capacity to the electricity market. The proposed model adopts an agent-based framework in which each building independently optimises its resources whilst coordinating with an aggregator agent that interfaces with the electricity market. Each building integrates fully electric air and water heating, through a central cold climate air source heat pump (ccASHP) and a heat pump water heater (HPWH) system. The proposed model prioritises the occupants' comfort whilst optimising cost savings and contributing to grid services. Unit-specific line heat losses are incorporated into the central ccASHP model, enhancing the precision of the estimated available capacity for grid services. A comprehensive study is conducted within 28-unit buildings to assess the performance of the proposed model. The numerical results indicate significant savings in electricity bills by offering energy flexibility and regulation up and down capacity to the market via the community of all-electric buildings.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566836","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}

{"title":"A Novel Physics-Constrained and Nature-Inspired Reinforcement Learning With Adaptive Model Predictive Control for Multi-Machine Renewable Rich Microgrids","authors":"Neelofar Shaukat,&nbsp;Md. Rabiul Islam,&nbsp;Syed Ayaz Ali Shah,&nbsp;Jamil Ahmad Khan","doi":"10.1049/stg2.70075","DOIUrl":"https://doi.org/10.1049/stg2.70075","url":null,"abstract":"&lt;p&gt;This research study proposes a physics-constrained nature-inspired (PCNI) reinforcement learning (RL) and model predictive control (MPC) strategy that simultaneously incorporates a trigonometric Lyapunov function into both RL's training loss functions and reward shaping, that is, dual role of physics. The proposed control synergy features a unique structure, that is, a physics-informed RL supervisor acts as a meta-optimiser, dynamically adjusts the MPC's cost function weights and innovatively its prediction horizon in real-time. The RL agent is trained with a proposed custom loss function, incorporating Lyapunov constraints, system physics, and operational limits for innovative, safe, and physically plausible learning. This means that, unlike employing heuristic reward shaping, this paper introduces a concept where physics is explicitly integrated into the training process. Inspired by the natural species' adaptability, such as birds adjust their flight trajectories and reaction distance under disturbances, the proposed framework dynamically reshapes the prediction horizon and cost function weights to preserve system stability. The robustness of the proposed control architecture is validated on a multi-machine nonlinear microgrid model under severe and recurring disturbances through a rigorous simulation environment with random perturbations every 4 seconds across seven distinct initial conditions. A conventional droop, MPC, and RL controllers are included as a baseline reference for comparative analysis. Simulation results illustrate consistently bounded frequency and voltage responses, and stringent compliance with operational limits for the proposed control scheme. The proposed control methodology achieves: (a) &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;92.90&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $92.90mathit{%}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; stability rate compared with &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;3.8&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $3.8mathit{%}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; for droop, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1.7&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $1.7mathit{%}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; for MPC, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1.3&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $1.3mathit{%}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; for RL, (b) average frequency RMSE of 0.041 pu compared with 0.029 pu for droop, 0.057 pu for MPC and 0.062 pu for RL, (c) voltage RMSE of 0.0062 pu compared with 0.018","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566806","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}

{"title":"Meteorology-Informed Coordinated Operation of Low-Carbon Park Energy System Considering Electric Vehicle Clusters and Energy Storage","authors":"Honghui Zhang,&nbsp;Dejie Zhao,&nbsp;Limin Jia","doi":"10.1049/stg2.70065","DOIUrl":"https://doi.org/10.1049/stg2.70065","url":null,"abstract":"<p>With the increasing integration of electric vehicles (EVs) and renewable energy in industrial parks, achieving coordinated low-carbon operation of park-level energy systems has become a critical challenge. Meteorological conditions introduce significant uncertainty in renewable generation, making its effective management pivotal to system operation. This paper proposes a meteorology-informed price-based bilevel optimisation framework. In the upper level, the operator minimises the total operating cost and carbon emissions of the park by considering fuel expenditure, grid transactions and carbon trading. The optimised price is endogenously determined to reflect the marginal supply cost together with the carbon shadow price. In the lower level, the aggregated EV cluster and energy storage system (ESS) respond to this price by scheduling their charging and discharging behaviours to achieve both economic efficiency and carbon reduction. The model jointly optimises economic dispatch, carbon mitigation and price coordination among multiple energy resources. Case studies based on an actual industrial park verify the effectiveness of the framework. The results demonstrate that it significantly reduces total carbon emissions, enhances renewable energy utilisation and improves flexibility in energy scheduling. Overall, the proposed framework provides a practical and scalable solution for intelligent and low-carbon operation of modern industrial parks.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564705","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}

{"title":"An Adaptive Forecasting Framework for EV Charging Demand Using Variational Mode Decomposition and Louvain Community Detection","authors":"Qiong Wang,&nbsp;Linru Jiang,&nbsp;Qinghe Sun,&nbsp;Chenjie Yan,&nbsp;Jiawei Li,&nbsp;Xi Zhang","doi":"10.1049/stg2.70062","DOIUrl":"https://doi.org/10.1049/stg2.70062","url":null,"abstract":"<p>Accurate short-term forecasting of electric vehicle (EV) charging demand is crucial for demand-side management and grid stability in modern power systems, especially within the context of virtual power plant (VPP) operations. However, EV load profiles exhibit strong stochasticity and multi-scale variability, making traditional single-model predictors prone to overfitting, mode mixing, or degraded performance under shifting operating conditions. This study proposes a hybrid decomposition–clustering–adaptive forecasting framework that integrates variational mode decomposition (VMD), Louvain community detection and a lightweight adaptive model pool. First, VMD decomposes the raw load signal into mode components with reduced frequency overlap. Second, a correlation-based similarity graph is constructed and processed by the Louvain algorithm to automatically group modes with coherent temporal characteristics. Finally, an adaptive prediction mechanism selects or refines models for each community based on a normalised MSE threshold. Experiments on three real-world charging-station datasets show that the proposed method significantly improves forecasting performance, achieving a 60.78%–75.18% reduction in MAPE compared with conventional single-model baselines, and a 40.55%–53.70% improvement compared with nonadaptive VMD–LSTM schemes, while maintaining manageable computational cost. These results demonstrate the framework's robustness and its potential applicability to real-time EV charging management and other nonstationary energy forecasting tasks.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147563769","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}