{"title":"使用RBF的单区和双区LFC","authors":"Sayari Das, Devesh Shukla, S. P. Singh","doi":"10.1109/RDCAPE.2015.7281424","DOIUrl":null,"url":null,"abstract":"The control of operating frequency is of utmost importance in the power system operation and control from stability point of view. Conventionally load frequency control is done by employing PI controllers. Immediate control of system frequency oscillation is very vital to avoid hazardous conditions. Need of the hour is to introduce a fast controller superior to the conventional PI controller. In this paper Artificial Neural Network is used for Load Frequency Control of single and two area load frequency control. Both single area and two area cases are modelled using state space technique. The training data is obtained from time domain analysis of the system using Runge Kutta technique and used for training the Levenberg-Marquardt and Radial Basis based neural network.","PeriodicalId":403256,"journal":{"name":"2015 International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Single and two area LFC using RBF\",\"authors\":\"Sayari Das, Devesh Shukla, S. P. Singh\",\"doi\":\"10.1109/RDCAPE.2015.7281424\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The control of operating frequency is of utmost importance in the power system operation and control from stability point of view. Conventionally load frequency control is done by employing PI controllers. Immediate control of system frequency oscillation is very vital to avoid hazardous conditions. Need of the hour is to introduce a fast controller superior to the conventional PI controller. In this paper Artificial Neural Network is used for Load Frequency Control of single and two area load frequency control. Both single area and two area cases are modelled using state space technique. The training data is obtained from time domain analysis of the system using Runge Kutta technique and used for training the Levenberg-Marquardt and Radial Basis based neural network.\",\"PeriodicalId\":403256,\"journal\":{\"name\":\"2015 International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE)\",\"volume\":\"140 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-03-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RDCAPE.2015.7281424\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RDCAPE.2015.7281424","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The control of operating frequency is of utmost importance in the power system operation and control from stability point of view. Conventionally load frequency control is done by employing PI controllers. Immediate control of system frequency oscillation is very vital to avoid hazardous conditions. Need of the hour is to introduce a fast controller superior to the conventional PI controller. In this paper Artificial Neural Network is used for Load Frequency Control of single and two area load frequency control. Both single area and two area cases are modelled using state space technique. The training data is obtained from time domain analysis of the system using Runge Kutta technique and used for training the Levenberg-Marquardt and Radial Basis based neural network.