{"title":"基于粒子群优化的分布式发电安装","authors":"L. Y. Wong, S. Rahim, M. Sulaiman, O. Aliman","doi":"10.1109/PEOCO.2010.5559168","DOIUrl":null,"url":null,"abstract":"This paper presents a particle swarm optimization approach for the placement of distributed generation (DG) in the distribution system. DG installation in the distribution system is very useful in reducing the line losses, as well as improving the voltage profiles. The proposed method combines particle swarm optimization and the Newton-Raphson load flow method to determine the location and size of the DG. The objective function to be minimized in this problem is the total power losses of the system. The proposed approach has been tested on IEEE 69-bus distribution test system and the program was simulated using MATLAB software. Test results show the effectiveness of the developed algorithm.","PeriodicalId":379868,"journal":{"name":"2010 4th International Power Engineering and Optimization Conference (PEOCO)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"45","resultStr":"{\"title\":\"Distributed generation installation using particle swarm optimization\",\"authors\":\"L. Y. Wong, S. Rahim, M. Sulaiman, O. Aliman\",\"doi\":\"10.1109/PEOCO.2010.5559168\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a particle swarm optimization approach for the placement of distributed generation (DG) in the distribution system. DG installation in the distribution system is very useful in reducing the line losses, as well as improving the voltage profiles. The proposed method combines particle swarm optimization and the Newton-Raphson load flow method to determine the location and size of the DG. The objective function to be minimized in this problem is the total power losses of the system. The proposed approach has been tested on IEEE 69-bus distribution test system and the program was simulated using MATLAB software. Test results show the effectiveness of the developed algorithm.\",\"PeriodicalId\":379868,\"journal\":{\"name\":\"2010 4th International Power Engineering and Optimization Conference (PEOCO)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"45\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 4th International Power Engineering and Optimization Conference (PEOCO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PEOCO.2010.5559168\",\"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 4th International Power Engineering and Optimization Conference (PEOCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PEOCO.2010.5559168","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Distributed generation installation using particle swarm optimization
This paper presents a particle swarm optimization approach for the placement of distributed generation (DG) in the distribution system. DG installation in the distribution system is very useful in reducing the line losses, as well as improving the voltage profiles. The proposed method combines particle swarm optimization and the Newton-Raphson load flow method to determine the location and size of the DG. The objective function to be minimized in this problem is the total power losses of the system. The proposed approach has been tested on IEEE 69-bus distribution test system and the program was simulated using MATLAB software. Test results show the effectiveness of the developed algorithm.
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