{"title":"An improved particle swarm optimization algorithm for reactive power optimization","authors":"Tuo Xie, Gang Zhang, Jiancang Xie, Yin Liu","doi":"10.1109/IMSNA.2013.6743322","DOIUrl":null,"url":null,"abstract":"Reactive power optimization of power system is a complicated multi-objective, multi-constraint combination optimization problem, particle swarm optimization (PSO) algorithm is the most commonly used algorithm to solve this problem. Aiming at the disadvantages of PSO algorithm, this paper came up with an improved particle swarm optimization (IPSO) algorithm. Firstly, it improved the particle population and initial position, and introduced weight coefficient in iterative process of evolution, which made the particles search process more reasonable and avoided premature convergence, secondly, it introduced the mutation operation to prevent particle swarming into local optimum, and enhanced the global optimization ability of the algorithm. Through the simulation calculation of the IEEE 6 nodes system, the results showed that IPSO algorithm is more effective than PSO algorithm.","PeriodicalId":111582,"journal":{"name":"2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMSNA.2013.6743322","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Reactive power optimization of power system is a complicated multi-objective, multi-constraint combination optimization problem, particle swarm optimization (PSO) algorithm is the most commonly used algorithm to solve this problem. Aiming at the disadvantages of PSO algorithm, this paper came up with an improved particle swarm optimization (IPSO) algorithm. Firstly, it improved the particle population and initial position, and introduced weight coefficient in iterative process of evolution, which made the particles search process more reasonable and avoided premature convergence, secondly, it introduced the mutation operation to prevent particle swarming into local optimum, and enhanced the global optimization ability of the algorithm. Through the simulation calculation of the IEEE 6 nodes system, the results showed that IPSO algorithm is more effective than PSO algorithm.