Liping Qu, Tailu Gao, Jie Zhang, Bin Liu, Wenchao Cui
{"title":"An Improved Hybrid Method for Power System Reliability Assessment","authors":"Liping Qu, Tailu Gao, Jie Zhang, Bin Liu, Wenchao Cui","doi":"10.1109/DCABES57229.2022.00013","DOIUrl":null,"url":null,"abstract":"In order to improve the sampling efficiency of the non-sequential Monte Carlo simulation method, an improved hybrid method combining the analytical method and the significant Latin hypercube sampling method is proposed based on the idea of state space partitioning. The method partitions the system state space based on the determination of the significant state subspace to avoid sampling the zero-fault state of the system. The reliability index of the significant state subspace is efficiently solved by the analytical method, and the remaining state subspace is sampled by the significant Latin hypercube sampling method. Finally, the correctness and efficiency of the proposed algorithm is verified by evaluating the reliability of the IEEE-RTS system.","PeriodicalId":344365,"journal":{"name":"2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"49 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DCABES57229.2022.00013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In order to improve the sampling efficiency of the non-sequential Monte Carlo simulation method, an improved hybrid method combining the analytical method and the significant Latin hypercube sampling method is proposed based on the idea of state space partitioning. The method partitions the system state space based on the determination of the significant state subspace to avoid sampling the zero-fault state of the system. The reliability index of the significant state subspace is efficiently solved by the analytical method, and the remaining state subspace is sampled by the significant Latin hypercube sampling method. Finally, the correctness and efficiency of the proposed algorithm is verified by evaluating the reliability of the IEEE-RTS system.