{"title":"基于pso的CPSO参数调整方法","authors":"I. Ziari, A. Jalilian","doi":"10.1109/ICHQP.2010.5625405","DOIUrl":null,"url":null,"abstract":"This paper introduces a modified particle swarm optimization (MPSO) algorithm which gets benefit from all remarkable advantages of conventional PSO (CPSO) in addition to lower possibility of catching in premature convergence and higher accuracy. In this paper, influence of CPSO parameters changes on the output accuracy is firstly represented and studied; then, a modified PSO called MPSO is studied to calculate these parameters optimally and improve the premature convergence problem along with the accuracy. In the proposed approach, CPSO parameters are determined using another CPSO algorithm in which parameters are selected typically. To evaluate the proposed MPSO, a 6-bus power system is considered in which two nonlinear loads are located as harmonics generators. A Comparison between the results of MPSO and those of CPSO and genetic algorithm (GA) is used to demonstrate the applicability and effectiveness of the MPSO-based algorithm and its superiority over other techniques.","PeriodicalId":180078,"journal":{"name":"Proceedings of 14th International Conference on Harmonics and Quality of Power - ICHQP 2010","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A PSO-based approach to adjust CPSO parameters\",\"authors\":\"I. Ziari, A. Jalilian\",\"doi\":\"10.1109/ICHQP.2010.5625405\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces a modified particle swarm optimization (MPSO) algorithm which gets benefit from all remarkable advantages of conventional PSO (CPSO) in addition to lower possibility of catching in premature convergence and higher accuracy. In this paper, influence of CPSO parameters changes on the output accuracy is firstly represented and studied; then, a modified PSO called MPSO is studied to calculate these parameters optimally and improve the premature convergence problem along with the accuracy. In the proposed approach, CPSO parameters are determined using another CPSO algorithm in which parameters are selected typically. To evaluate the proposed MPSO, a 6-bus power system is considered in which two nonlinear loads are located as harmonics generators. A Comparison between the results of MPSO and those of CPSO and genetic algorithm (GA) is used to demonstrate the applicability and effectiveness of the MPSO-based algorithm and its superiority over other techniques.\",\"PeriodicalId\":180078,\"journal\":{\"name\":\"Proceedings of 14th International Conference on Harmonics and Quality of Power - ICHQP 2010\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 14th International Conference on Harmonics and Quality of Power - ICHQP 2010\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICHQP.2010.5625405\",\"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 of 14th International Conference on Harmonics and Quality of Power - ICHQP 2010","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICHQP.2010.5625405","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper introduces a modified particle swarm optimization (MPSO) algorithm which gets benefit from all remarkable advantages of conventional PSO (CPSO) in addition to lower possibility of catching in premature convergence and higher accuracy. In this paper, influence of CPSO parameters changes on the output accuracy is firstly represented and studied; then, a modified PSO called MPSO is studied to calculate these parameters optimally and improve the premature convergence problem along with the accuracy. In the proposed approach, CPSO parameters are determined using another CPSO algorithm in which parameters are selected typically. To evaluate the proposed MPSO, a 6-bus power system is considered in which two nonlinear loads are located as harmonics generators. A Comparison between the results of MPSO and those of CPSO and genetic algorithm (GA) is used to demonstrate the applicability and effectiveness of the MPSO-based algorithm and its superiority over other techniques.