IEEE Transactions on Sustainable Energy最新文献

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IEEE Transactions on Sustainable Energy Pub Date : 2025-09-29 DOI: 10.1109/TSTE.2025.3606325

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IEEE Transactions on Sustainable Energy Pub Date : 2025-09-29 DOI: 10.1109/TSTE.2025.3611459

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IEEE Transactions on Sustainable Energy Pub Date : 2025-09-29 DOI: 10.1109/TSTE.2025.3606327

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IEEE Transactions on Sustainable Energy Pub Date : 2025-06-20 DOI: 10.1109/TSTE.2025.3576563

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IEEE Transactions on Sustainable Energy Pub Date : 2025-06-20 DOI: 10.1109/TSTE.2025.3576553

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IEEE Transactions on Sustainable Energy Pub Date : 2025-06-20 DOI: 10.1109/TSTE.2025.3576557

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IEEE Transactions on Sustainable Energy Pub Date : 2025-06-20 DOI: 10.1109/TSTE.2025.3576555

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IEEE Transactions on Sustainable Energy Pub Date : 2025-06-20 DOI: 10.1109/TSTE.2025.3576561

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Region of Attraction Estimation for Power Systems With Multiple Integrated DFIG-Based Wind Turbines 多台集成dfig风电机组电力系统的吸引力区域估计

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IEEE Transactions on Sustainable Energy Pub Date : 2025-06-12 DOI: 10.1109/TSTE.2025.3579018

Yang Liu;Huanjin Yao;Pengyu Di;Yingjie Qin;Yiming Ma;Mohammed Alkahtani;Yihua Hu

{"title":"Region of Attraction Estimation for Power Systems With Multiple Integrated DFIG-Based Wind Turbines","authors":"Yang Liu;Huanjin Yao;Pengyu Di;Yingjie Qin;Yiming Ma;Mohammed Alkahtani;Yihua Hu","doi":"10.1109/TSTE.2025.3579018","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3579018","url":null,"abstract":"The lack of suitable modeling methods for power systems with multiple doubly-fed induction generator-based wind turbines (DFIGWTs) integrated has left the analytical description of the boundary of the region of attraction (ROA) of such systems largely unexplored. To address this gap, this paper derives an ordinary differential equation (ODE) model for a power system with multiple DFIGWTs integrated. The proposed electromechanical model is validated in a single-machine-infinite-bus (SMIB) power system and a modified 3 machine 9 bus power system with root mean squared errors (RMSEs) of less than 9.5% for trajectory comparisons with the full model, demonstrating that it accurately captures the low-frequency dynamics of the full DFIGWT model. Subsequently, the ODE model is transformed into a polynomial differential-algebraic equation (DAE) model using a nonlinear coordinate transformation. To estimate the ROA, an enhanced expanding interior algorithm (EIA) based on sum of squares (SOS) programming is applied. The feasibility of the proposed model, along with the appropriate conservativeness of the improved EIA, is validated using two test systems that include multiple DFIGWTs and synchronous generators (SGs). By comparison, it is found that the time cost of the improved EIA is reduced by around 17% while maintaining the accuracy. These results demonstrate that the proposed approach has significant practical implications for the integration of wind farms into power systems, and offers an efficient tool for transient stability analysis.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"3095-3109"},"PeriodicalIF":10.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Extreme Probabilistic Solar Power Prediction via Localized Sample Structure Recognition and Generalized Error Estimation 基于局部样本结构识别和广义误差估计的太阳能发电极值概率预测

IF 1 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2025-06-12 DOI: 10.1109/TSTE.2025.3579335

Jiacheng Liu;Jun Liu;Xinglei Liu;Tao Ding;Guangyao Wang;Xiaoming Liu;Yu Zhao

{"title":"Extreme Probabilistic Solar Power Prediction via Localized Sample Structure Recognition and Generalized Error Estimation","authors":"Jiacheng Liu;Jun Liu;Xinglei Liu;Tao Ding;Guangyao Wang;Xiaoming Liu;Yu Zhao","doi":"10.1109/TSTE.2025.3579335","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3579335","url":null,"abstract":"The fluctuations and uncertainty of solar power constantly threaten the secure operation and economic dispatch of power systems. Existing end-to-end point or probabilistic solar power prediction methods mostly lack effective integration of the two approaches, and the latent error caused by machine learning (ML) techniques is rarely taken into consideration. Hence in this paper, a combined extreme probabilistic solar power prediction (EPSPP) scheme is proposed, by integrating point forecasting with extreme error estimation. Firstly, the localized sample structure recognition (LSSR) is conducted to determine the neighborhood of meteorological conditions, where feature weights of Euclidean distance measurement are allocated with respect to the valid mutual information (MI) derived by two-dimensional diffusion kernel density estimation (2D-DKDE). Secondly, with the neighborhood generated by LSSR, an improved localized generalization error estimation (ILGEE) algorithm is put forward to infer the real-time maximal second-order origin moment of solar power point forecasting error corresponding to designated confidence levels. Finally, the solar power at each temporal moment is deduced as distinct Gaussian distributions, by modifying the mean value and variance according to statistical principles. For the sake of the so-called “extreme”, the proposed scheme could maintain reliability even under circumstances of the worst ML model precision. Cases from a real-world solar power station in Oregon, USA, are used to validate its effectiveness.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"3110-3123"},"PeriodicalIF":10.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0