E. Nascimento, P. Goswami, E. Kasenally, B. Cory, D. Macdonald
{"title":"Security assessment of a turbine generator using H/sup infinity / control based on artificial neural networks and expert systems","authors":"E. Nascimento, P. Goswami, E. Kasenally, B. Cory, D. Macdonald","doi":"10.1109/ANN.1991.213496","DOIUrl":null,"url":null,"abstract":"The authors describe a preliminary framework for real time security assessment of turbine generators that integrates artificial neural networks (ANN) and knowledge-based expert systems (KBES). The authors also present the transient stability assessment of a turbine generator using a back propagation artificial neural network. Additional signals have been added to the AVR and governor loops of the turbine generator using H/sup infinity / control. The ANN's ability to learn, interpolate and reproduce behaviour is presented, showing how the stability of a high order nonlinear system can be obtained without the prior solution of the state equations.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"9 2-4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ANN.1991.213496","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
The authors describe a preliminary framework for real time security assessment of turbine generators that integrates artificial neural networks (ANN) and knowledge-based expert systems (KBES). The authors also present the transient stability assessment of a turbine generator using a back propagation artificial neural network. Additional signals have been added to the AVR and governor loops of the turbine generator using H/sup infinity / control. The ANN's ability to learn, interpolate and reproduce behaviour is presented, showing how the stability of a high order nonlinear system can be obtained without the prior solution of the state equations.<>