{"title":"基于阻抗轨迹智能识别的发电机失步保护","authors":"Zhenxing Li, Yangze Wang, Cong Hu, Yi Zhu, D. Cui","doi":"10.1109/AICIT55386.2022.9930321","DOIUrl":null,"url":null,"abstract":"In order to improve the selectivity and quickness of generator out-of-step protection, a method of generator out-of-step protection based on support vector machine (SVM) for intelligent trajectory recognition is presented. Firstly, the motion feature is extracted from the measured impedance trajectory at the generator terminal, and the extracted feature sequence is calculated with statistical parameters to form 140-dimensional features. Secondly, the principal component analysis method is used to reduce the dimension of the feature to form the corresponding training input feature space, and the particle swarm algorithm is used to optimize the parameters of SVM. Finally, the simulation samples verify that the method can accurately identify the out-of-step oscillations. Compared with traditional out-of-step protection, this method improves the reliability and speed of generator out-of-step protection.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Out-of-Step Protection in Generator Based on Intelligent Identification of Impedance Trajectory\",\"authors\":\"Zhenxing Li, Yangze Wang, Cong Hu, Yi Zhu, D. Cui\",\"doi\":\"10.1109/AICIT55386.2022.9930321\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to improve the selectivity and quickness of generator out-of-step protection, a method of generator out-of-step protection based on support vector machine (SVM) for intelligent trajectory recognition is presented. Firstly, the motion feature is extracted from the measured impedance trajectory at the generator terminal, and the extracted feature sequence is calculated with statistical parameters to form 140-dimensional features. Secondly, the principal component analysis method is used to reduce the dimension of the feature to form the corresponding training input feature space, and the particle swarm algorithm is used to optimize the parameters of SVM. Finally, the simulation samples verify that the method can accurately identify the out-of-step oscillations. Compared with traditional out-of-step protection, this method improves the reliability and speed of generator out-of-step protection.\",\"PeriodicalId\":231070,\"journal\":{\"name\":\"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AICIT55386.2022.9930321\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AICIT55386.2022.9930321","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Out-of-Step Protection in Generator Based on Intelligent Identification of Impedance Trajectory
In order to improve the selectivity and quickness of generator out-of-step protection, a method of generator out-of-step protection based on support vector machine (SVM) for intelligent trajectory recognition is presented. Firstly, the motion feature is extracted from the measured impedance trajectory at the generator terminal, and the extracted feature sequence is calculated with statistical parameters to form 140-dimensional features. Secondly, the principal component analysis method is used to reduce the dimension of the feature to form the corresponding training input feature space, and the particle swarm algorithm is used to optimize the parameters of SVM. Finally, the simulation samples verify that the method can accurately identify the out-of-step oscillations. Compared with traditional out-of-step protection, this method improves the reliability and speed of generator out-of-step protection.