{"title":"基于人工神经网络控制器的DFIM速度控制","authors":"Brahim Dahhou, A. Bouraiou","doi":"10.1109/ICAEE53772.2022.9961983","DOIUrl":null,"url":null,"abstract":"Nonlinear characteristics and parameters variation of the Doubly Fed Induction Motor (DFIM) posed a serious problem during operation. For this purpose, it is necessary to use control laws insensitive to variations in parameters, disturbances, and non-linarites. In this paper, a speed controller of a DFIM by the application of a PI controller based on Artificial Neural Network (ANN) is proposed. The results obtained with ANNPI are compared with AFLC-PI. This controller is then designed and trained online using a back propagation network algorithm. The performance of the proposed controller is adopted using Matlab / Simulink. Simulation results show a fast dynamic response and good performance in tracking speed and torque.","PeriodicalId":206584,"journal":{"name":"2022 2nd International Conference on Advanced Electrical Engineering (ICAEE)","volume":"191 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Speed Control Of DFIM Using Artificial Neural Network Controller\",\"authors\":\"Brahim Dahhou, A. Bouraiou\",\"doi\":\"10.1109/ICAEE53772.2022.9961983\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nonlinear characteristics and parameters variation of the Doubly Fed Induction Motor (DFIM) posed a serious problem during operation. For this purpose, it is necessary to use control laws insensitive to variations in parameters, disturbances, and non-linarites. In this paper, a speed controller of a DFIM by the application of a PI controller based on Artificial Neural Network (ANN) is proposed. The results obtained with ANNPI are compared with AFLC-PI. This controller is then designed and trained online using a back propagation network algorithm. The performance of the proposed controller is adopted using Matlab / Simulink. Simulation results show a fast dynamic response and good performance in tracking speed and torque.\",\"PeriodicalId\":206584,\"journal\":{\"name\":\"2022 2nd International Conference on Advanced Electrical Engineering (ICAEE)\",\"volume\":\"191 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 2nd International Conference on Advanced Electrical Engineering (ICAEE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAEE53772.2022.9961983\",\"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 2nd International Conference on Advanced Electrical Engineering (ICAEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAEE53772.2022.9961983","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Speed Control Of DFIM Using Artificial Neural Network Controller
Nonlinear characteristics and parameters variation of the Doubly Fed Induction Motor (DFIM) posed a serious problem during operation. For this purpose, it is necessary to use control laws insensitive to variations in parameters, disturbances, and non-linarites. In this paper, a speed controller of a DFIM by the application of a PI controller based on Artificial Neural Network (ANN) is proposed. The results obtained with ANNPI are compared with AFLC-PI. This controller is then designed and trained online using a back propagation network algorithm. The performance of the proposed controller is adopted using Matlab / Simulink. Simulation results show a fast dynamic response and good performance in tracking speed and torque.