L. Giang, Nguyen Thi Hong Yen, Ngo Duy Tan, Nguyen Duc Viet, Tran Thi Ngoat
{"title":"将人工神经网络应用于PMSG风力发电机组机侧变流器的控制器设计","authors":"L. Giang, Nguyen Thi Hong Yen, Ngo Duy Tan, Nguyen Duc Viet, Tran Thi Ngoat","doi":"10.1109/EEEIC/ICPSEUROPE49358.2020.9160633","DOIUrl":null,"url":null,"abstract":"This paper discusses the improvement of the Permanent magnetic synchronous generator (PMSG) wind turbine using an artificial neural network for the controller of the machine side converter. An artificial neural network (ANN) is trained to replace traditional PI controllers, to improve stability, accuracy and control performance. Simulation is performed in MATLAB after training the neural network and it is shown that results are good. The paper also indicates that the ANN controller can be less complicated and less costly as compared to some other proposed schemes.","PeriodicalId":215332,"journal":{"name":"2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Utilization of artificial neural network for the controller design of the machine side converter in the PMSG wind turbine\",\"authors\":\"L. Giang, Nguyen Thi Hong Yen, Ngo Duy Tan, Nguyen Duc Viet, Tran Thi Ngoat\",\"doi\":\"10.1109/EEEIC/ICPSEUROPE49358.2020.9160633\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper discusses the improvement of the Permanent magnetic synchronous generator (PMSG) wind turbine using an artificial neural network for the controller of the machine side converter. An artificial neural network (ANN) is trained to replace traditional PI controllers, to improve stability, accuracy and control performance. Simulation is performed in MATLAB after training the neural network and it is shown that results are good. The paper also indicates that the ANN controller can be less complicated and less costly as compared to some other proposed schemes.\",\"PeriodicalId\":215332,\"journal\":{\"name\":\"2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EEEIC/ICPSEUROPE49358.2020.9160633\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EEEIC/ICPSEUROPE49358.2020.9160633","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Utilization of artificial neural network for the controller design of the machine side converter in the PMSG wind turbine
This paper discusses the improvement of the Permanent magnetic synchronous generator (PMSG) wind turbine using an artificial neural network for the controller of the machine side converter. An artificial neural network (ANN) is trained to replace traditional PI controllers, to improve stability, accuracy and control performance. Simulation is performed in MATLAB after training the neural network and it is shown that results are good. The paper also indicates that the ANN controller can be less complicated and less costly as compared to some other proposed schemes.