{"title":"基于贝叶斯正则化反向传播神经网络的传输损耗分配","authors":"N. Choudhury, S. Goswami","doi":"10.1109/INDCON.2010.5712685","DOIUrl":null,"url":null,"abstract":"Allocating losses due to transmission in deregulated power market has become an important issue due to the changed operating mode of restructured power system. The difficulty with the job of loss allocation to the participating players lies in the fact that transmission losses have mutual couplings thus having no acceptable engineering solutions. Game theoretic approach might be an acceptable approach as they are developed based on the satisfaction of the individual players. Applying game theoretic approach on the other hand, as an independent solution tool is also difficult as it needs handling of huge data to solve a single case. A combination of the game theory and neural network thus is proposed here as an alternative solution.","PeriodicalId":109071,"journal":{"name":"2010 Annual IEEE India Conference (INDICON)","volume":"510 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Transmission loss allocation using Bayesian regularization backpropagation ANN\",\"authors\":\"N. Choudhury, S. Goswami\",\"doi\":\"10.1109/INDCON.2010.5712685\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Allocating losses due to transmission in deregulated power market has become an important issue due to the changed operating mode of restructured power system. The difficulty with the job of loss allocation to the participating players lies in the fact that transmission losses have mutual couplings thus having no acceptable engineering solutions. Game theoretic approach might be an acceptable approach as they are developed based on the satisfaction of the individual players. Applying game theoretic approach on the other hand, as an independent solution tool is also difficult as it needs handling of huge data to solve a single case. A combination of the game theory and neural network thus is proposed here as an alternative solution.\",\"PeriodicalId\":109071,\"journal\":{\"name\":\"2010 Annual IEEE India Conference (INDICON)\",\"volume\":\"510 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Annual IEEE India Conference (INDICON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INDCON.2010.5712685\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Annual IEEE India Conference (INDICON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDCON.2010.5712685","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Transmission loss allocation using Bayesian regularization backpropagation ANN
Allocating losses due to transmission in deregulated power market has become an important issue due to the changed operating mode of restructured power system. The difficulty with the job of loss allocation to the participating players lies in the fact that transmission losses have mutual couplings thus having no acceptable engineering solutions. Game theoretic approach might be an acceptable approach as they are developed based on the satisfaction of the individual players. Applying game theoretic approach on the other hand, as an independent solution tool is also difficult as it needs handling of huge data to solve a single case. A combination of the game theory and neural network thus is proposed here as an alternative solution.