{"title":"Intelligent current controller for an HVDC transmission link","authors":"K. Narendra, K. Khorasani, V. Sood, R. Patel","doi":"10.1109/PICA.1997.599379","DOIUrl":null,"url":null,"abstract":"This paper describes an intelligent current controller for the fast and flexible control of an HVDC transmission link using artificial neural network (ANN) and fuzzy logic (FL) paradigms. A simple yet effective ANN architecture is presented with online adaptation of the activation function and learning parameters. Two methods of adapting the learning parameters are presented. In the first method, a heuristic approach to evaluate the learning rate as a polynomial of an energy function is considered. In the second method, a FL based online adaptation of the learning parameters is discussed. Performance of ANN, ANN-FL based and PI controllers are compared. A feasibility analysis is carried out to implement the proposed neural controller algorithm in real-time.","PeriodicalId":383749,"journal":{"name":"Proceedings of the 20th International Conference on Power Industry Computer Applications","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 20th International Conference on Power Industry Computer Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PICA.1997.599379","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 24
Abstract
This paper describes an intelligent current controller for the fast and flexible control of an HVDC transmission link using artificial neural network (ANN) and fuzzy logic (FL) paradigms. A simple yet effective ANN architecture is presented with online adaptation of the activation function and learning parameters. Two methods of adapting the learning parameters are presented. In the first method, a heuristic approach to evaluate the learning rate as a polynomial of an energy function is considered. In the second method, a FL based online adaptation of the learning parameters is discussed. Performance of ANN, ANN-FL based and PI controllers are compared. A feasibility analysis is carried out to implement the proposed neural controller algorithm in real-time.