{"title":"基于改进粒子群算法的多目标分布式发电系统布局与优化,降低了电力损耗,提高了电压稳定指标","authors":"H. Musa, S. S. Adamu","doi":"10.1109/ENERGYTECH.2013.6645315","DOIUrl":null,"url":null,"abstract":"This paper presents an enhanced particle swarm optimization (PSO) algorithm for Distributed Generation (DG) placement and sizing using multi-objective optimization concept. It is based on the combination of Evolutionary Programming (EP) and PSO. The merits of EP and PSO are combined together so as to achieve faster convergence and accuracy of the DG sizes. The quality of the solution is improved by exploring the less crowded area in the existing solution space to obtain more non-dominated solutions. The proposed approach was tested on standard IEEE 33 -Bus test system. Result obtained shows the ability of the proposed algorithm towards production of well-distributed Pareto optimal non-dominated solution of the multi-objective DG sizing problem.","PeriodicalId":154402,"journal":{"name":"2013 IEEE Energytech","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Enhanced PSO based multi-objective distributed generation placement and sizing for power loss reduction and voltage stability index improvement\",\"authors\":\"H. Musa, S. S. Adamu\",\"doi\":\"10.1109/ENERGYTECH.2013.6645315\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an enhanced particle swarm optimization (PSO) algorithm for Distributed Generation (DG) placement and sizing using multi-objective optimization concept. It is based on the combination of Evolutionary Programming (EP) and PSO. The merits of EP and PSO are combined together so as to achieve faster convergence and accuracy of the DG sizes. The quality of the solution is improved by exploring the less crowded area in the existing solution space to obtain more non-dominated solutions. The proposed approach was tested on standard IEEE 33 -Bus test system. Result obtained shows the ability of the proposed algorithm towards production of well-distributed Pareto optimal non-dominated solution of the multi-objective DG sizing problem.\",\"PeriodicalId\":154402,\"journal\":{\"name\":\"2013 IEEE Energytech\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-05-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE Energytech\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ENERGYTECH.2013.6645315\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Energytech","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ENERGYTECH.2013.6645315","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Enhanced PSO based multi-objective distributed generation placement and sizing for power loss reduction and voltage stability index improvement
This paper presents an enhanced particle swarm optimization (PSO) algorithm for Distributed Generation (DG) placement and sizing using multi-objective optimization concept. It is based on the combination of Evolutionary Programming (EP) and PSO. The merits of EP and PSO are combined together so as to achieve faster convergence and accuracy of the DG sizes. The quality of the solution is improved by exploring the less crowded area in the existing solution space to obtain more non-dominated solutions. The proposed approach was tested on standard IEEE 33 -Bus test system. Result obtained shows the ability of the proposed algorithm towards production of well-distributed Pareto optimal non-dominated solution of the multi-objective DG sizing problem.