{"title":"同步发电机速度控制的自适应神经控制器","authors":"S. Z. Ao, K. Bollinger","doi":"10.1109/CCECE.1996.548220","DOIUrl":null,"url":null,"abstract":"Neural networks have shown great promise in many areas of engineering. In this paper, we present a newly designed neural control system that consists of three neural networks cascaded together, one representing the inverse model of the speed-governing and turbine system, another identifying the dynamics of the synchronous generator, and a third being part of the controller. The inverse model is achieved with a multilayer feedforward neural network trained in batch mode through back-propagation learning. Once the network is trained, its weights and biases will be fixed. The dynamics of the synchronous generator is identified on-line while the generator is operating. The weights of the neurocontroller are determined by sweeping back the control error. Usually this updating process has a lower frequency than the identification process to ensure the stability of the entire control system. The neurocontroller was applied to a multi-machine power system and some simulated results are presented.","PeriodicalId":269440,"journal":{"name":"Proceedings of 1996 Canadian Conference on Electrical and Computer Engineering","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An adaptive neurocontroller for speed control of a synchronous generator\",\"authors\":\"S. Z. Ao, K. Bollinger\",\"doi\":\"10.1109/CCECE.1996.548220\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Neural networks have shown great promise in many areas of engineering. In this paper, we present a newly designed neural control system that consists of three neural networks cascaded together, one representing the inverse model of the speed-governing and turbine system, another identifying the dynamics of the synchronous generator, and a third being part of the controller. The inverse model is achieved with a multilayer feedforward neural network trained in batch mode through back-propagation learning. Once the network is trained, its weights and biases will be fixed. The dynamics of the synchronous generator is identified on-line while the generator is operating. The weights of the neurocontroller are determined by sweeping back the control error. Usually this updating process has a lower frequency than the identification process to ensure the stability of the entire control system. The neurocontroller was applied to a multi-machine power system and some simulated results are presented.\",\"PeriodicalId\":269440,\"journal\":{\"name\":\"Proceedings of 1996 Canadian Conference on Electrical and Computer Engineering\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-05-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1996 Canadian Conference on Electrical and Computer Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCECE.1996.548220\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1996 Canadian Conference on Electrical and Computer Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCECE.1996.548220","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An adaptive neurocontroller for speed control of a synchronous generator
Neural networks have shown great promise in many areas of engineering. In this paper, we present a newly designed neural control system that consists of three neural networks cascaded together, one representing the inverse model of the speed-governing and turbine system, another identifying the dynamics of the synchronous generator, and a third being part of the controller. The inverse model is achieved with a multilayer feedforward neural network trained in batch mode through back-propagation learning. Once the network is trained, its weights and biases will be fixed. The dynamics of the synchronous generator is identified on-line while the generator is operating. The weights of the neurocontroller are determined by sweeping back the control error. Usually this updating process has a lower frequency than the identification process to ensure the stability of the entire control system. The neurocontroller was applied to a multi-machine power system and some simulated results are presented.