Chang Ye, Kezheng Jiang, Junjie Wu, Mingye Sun, Xiaotong Ji, Dan Liu
{"title":"The static voltage stability analysis of photovoltaic energy storage systems based on NPU algorithm","authors":"Chang Ye, Kezheng Jiang, Junjie Wu, Mingye Sun, Xiaotong Ji, Dan Liu","doi":"10.3389/fenrg.2024.1443677","DOIUrl":null,"url":null,"abstract":"Although the data-driven static voltage stability problems have been widely studied, most of the classical algorithms focus more on improving the accuracy of the system prediction, ignoring the error classification errors generated during the prediction process. Furthermore, current research ignores the utilization of data-driven voltage stability assessment of energy storage systems. Therefore, this paper proposes a static voltage stability assessment method for photovoltaic energy storage systems based on considering the error classification constraint algorithm using Neyman-Pearson umbrella algorithms. Firstly, the Spearman Correlation Coefficient is employed in the feature selection phase. Secondly, an updated voltage stability assessment (VSA) model is proposed. Compared with the existing data-driven prediction of system static voltage stability in the literature, it can realize voltage stability assessment more quickly. Furthermore, on the basis of rapid voltage stability assessment, the umbrella NP classifier can also effectively limit the first-class error and attenuate the effect of error classification by mirroring the control of the number of cycle splits and the type <jats:inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"><mml:mi mathvariant=\"bold\">I</mml:mi></mml:math></jats:inline-formula> classification error threshold. Finally, the simulation and experimental results show that the effectiveness and robustness of the scheme proposed in this paper in grid-connected photovoltaic energy farms.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":"97 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Energy Research","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3389/fenrg.2024.1443677","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
Although the data-driven static voltage stability problems have been widely studied, most of the classical algorithms focus more on improving the accuracy of the system prediction, ignoring the error classification errors generated during the prediction process. Furthermore, current research ignores the utilization of data-driven voltage stability assessment of energy storage systems. Therefore, this paper proposes a static voltage stability assessment method for photovoltaic energy storage systems based on considering the error classification constraint algorithm using Neyman-Pearson umbrella algorithms. Firstly, the Spearman Correlation Coefficient is employed in the feature selection phase. Secondly, an updated voltage stability assessment (VSA) model is proposed. Compared with the existing data-driven prediction of system static voltage stability in the literature, it can realize voltage stability assessment more quickly. Furthermore, on the basis of rapid voltage stability assessment, the umbrella NP classifier can also effectively limit the first-class error and attenuate the effect of error classification by mirroring the control of the number of cycle splits and the type I classification error threshold. Finally, the simulation and experimental results show that the effectiveness and robustness of the scheme proposed in this paper in grid-connected photovoltaic energy farms.
期刊介绍:
Frontiers in Energy Research makes use of the unique Frontiers platform for open-access publishing and research networking for scientists, which provides an equal opportunity to seek, share and create knowledge. The mission of Frontiers is to place publishing back in the hands of working scientists and to promote an interactive, fair, and efficient review process. Articles are peer-reviewed according to the Frontiers review guidelines, which evaluate manuscripts on objective editorial criteria