{"title":"Distribution systems copper and iron loss minimization by genetic algorithm","authors":"K. Nara, M. Kitagawa","doi":"10.1109/ANN.1993.264298","DOIUrl":null,"url":null,"abstract":"This paper proposes a new GA method to minimize distribution system losses including power transformer iron loss. Since the transformer iron loss is approximately proportional to the square of a transformer's primary voltage, one can minimize the sum of transformer iron loss and line resistive loss by adjusting line voltages and line currents appropriately. Since the problem is formulated as a complex combinatorial optimization problem, it is solved by applying a genetic algorithm (GA) in this paper. Several numerical examples are shown to demonstrate the proposed method.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1993-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ANN.1993.264298","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper proposes a new GA method to minimize distribution system losses including power transformer iron loss. Since the transformer iron loss is approximately proportional to the square of a transformer's primary voltage, one can minimize the sum of transformer iron loss and line resistive loss by adjusting line voltages and line currents appropriately. Since the problem is formulated as a complex combinatorial optimization problem, it is solved by applying a genetic algorithm (GA) in this paper. Several numerical examples are shown to demonstrate the proposed method.<>