{"title":"基于时间衰减权值的人工神经网络计算台湾电力公司输电系统损耗","authors":"W. Chu, Bin Chen, Pao-Chang Mo","doi":"10.1109/EMPD.1995.500704","DOIUrl":null,"url":null,"abstract":"For energy conservation and improvement of power system operation efficiency, how to reduce the transmission system losses becomes an important topic of grave concern. To understand the cause, and to evaluate the amount, of the losses are the prior steps to diminish them. To simplify the evaluation procedure without losing too much accuracy, this paper adopts the artificial neural network, which is a model free network, to analyze the transmission system losses. As the artificial neural network with time decayed weight has the capability of learning, memorizing, and forgetting, it is more suitable for a power system with gradually changing characteristics. By using this artificial neural network, the estimation of transmission system losses will be more precise. In this paper, comparison is made between the results of artificial neural network analysis and polynomial loss equations analysis.","PeriodicalId":447674,"journal":{"name":"Proceedings 1995 International Conference on Energy Management and Power Delivery EMPD '95","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Calculation of transmission system losses for the Taiwan Power Company by the artificial neural network with time decayed weight\",\"authors\":\"W. Chu, Bin Chen, Pao-Chang Mo\",\"doi\":\"10.1109/EMPD.1995.500704\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For energy conservation and improvement of power system operation efficiency, how to reduce the transmission system losses becomes an important topic of grave concern. To understand the cause, and to evaluate the amount, of the losses are the prior steps to diminish them. To simplify the evaluation procedure without losing too much accuracy, this paper adopts the artificial neural network, which is a model free network, to analyze the transmission system losses. As the artificial neural network with time decayed weight has the capability of learning, memorizing, and forgetting, it is more suitable for a power system with gradually changing characteristics. By using this artificial neural network, the estimation of transmission system losses will be more precise. In this paper, comparison is made between the results of artificial neural network analysis and polynomial loss equations analysis.\",\"PeriodicalId\":447674,\"journal\":{\"name\":\"Proceedings 1995 International Conference on Energy Management and Power Delivery EMPD '95\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 1995 International Conference on Energy Management and Power Delivery EMPD '95\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EMPD.1995.500704\",\"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 1995 International Conference on Energy Management and Power Delivery EMPD '95","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EMPD.1995.500704","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Calculation of transmission system losses for the Taiwan Power Company by the artificial neural network with time decayed weight
For energy conservation and improvement of power system operation efficiency, how to reduce the transmission system losses becomes an important topic of grave concern. To understand the cause, and to evaluate the amount, of the losses are the prior steps to diminish them. To simplify the evaluation procedure without losing too much accuracy, this paper adopts the artificial neural network, which is a model free network, to analyze the transmission system losses. As the artificial neural network with time decayed weight has the capability of learning, memorizing, and forgetting, it is more suitable for a power system with gradually changing characteristics. By using this artificial neural network, the estimation of transmission system losses will be more precise. In this paper, comparison is made between the results of artificial neural network analysis and polynomial loss equations analysis.