{"title":"基于粒子群算法和人工蜂群算法的供电系统无功电源优化配置","authors":"S. Kokin, V. Manusov, P. Matrenin","doi":"10.1109/EPE.2017.7967231","DOIUrl":null,"url":null,"abstract":"The reduction of active power losses in transmission lines of modern power supply systems by solving the problem of reactive power compensation is an important task. The efficiency of the compensation depends on the choice of powers and placement of compensation units in nodes of a network. The task considered is the multi-objective and multi-factor optimization problem, and the time required to solve it increases exponentially with the increase of the dimension of this task. Under these circumstances, it is advisable to use evolutionary and swarm optimization methods. In this paper, two swarm intelligence algorithms have been applied: Artificial Bees Colony optimization and Particle Swarm optimization. Particular attention has been devoted to applying the algorithms in practice, in this case, to solving the problem of the optimal placement of reactive power sources. It has been shown that using a specially developed interface between an optimization algorithm and a model of the optimization problem allows researchers to quickly apply stochastic optimization algorithms. In addition, the influence of the heuristic algorithms parameters of Particle Swarm and Bee Swarm on their effectiveness has been explored. It has been shown that the reactive compensation can reduce the active power losses in the power supply system up to 20%, and the payback period of the installation of the necessary equipment is 2 to 4 years.","PeriodicalId":201464,"journal":{"name":"2017 18th International Scientific Conference on Electric Power Engineering (EPE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Optimal placement of reactive power sources in power supply systems, using particle swarm optimization and artificial Bees colony optimization\",\"authors\":\"S. Kokin, V. Manusov, P. Matrenin\",\"doi\":\"10.1109/EPE.2017.7967231\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The reduction of active power losses in transmission lines of modern power supply systems by solving the problem of reactive power compensation is an important task. The efficiency of the compensation depends on the choice of powers and placement of compensation units in nodes of a network. The task considered is the multi-objective and multi-factor optimization problem, and the time required to solve it increases exponentially with the increase of the dimension of this task. Under these circumstances, it is advisable to use evolutionary and swarm optimization methods. In this paper, two swarm intelligence algorithms have been applied: Artificial Bees Colony optimization and Particle Swarm optimization. Particular attention has been devoted to applying the algorithms in practice, in this case, to solving the problem of the optimal placement of reactive power sources. It has been shown that using a specially developed interface between an optimization algorithm and a model of the optimization problem allows researchers to quickly apply stochastic optimization algorithms. In addition, the influence of the heuristic algorithms parameters of Particle Swarm and Bee Swarm on their effectiveness has been explored. It has been shown that the reactive compensation can reduce the active power losses in the power supply system up to 20%, and the payback period of the installation of the necessary equipment is 2 to 4 years.\",\"PeriodicalId\":201464,\"journal\":{\"name\":\"2017 18th International Scientific Conference on Electric Power Engineering (EPE)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 18th International Scientific Conference on Electric Power Engineering (EPE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EPE.2017.7967231\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 18th International Scientific Conference on Electric Power Engineering (EPE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EPE.2017.7967231","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal placement of reactive power sources in power supply systems, using particle swarm optimization and artificial Bees colony optimization
The reduction of active power losses in transmission lines of modern power supply systems by solving the problem of reactive power compensation is an important task. The efficiency of the compensation depends on the choice of powers and placement of compensation units in nodes of a network. The task considered is the multi-objective and multi-factor optimization problem, and the time required to solve it increases exponentially with the increase of the dimension of this task. Under these circumstances, it is advisable to use evolutionary and swarm optimization methods. In this paper, two swarm intelligence algorithms have been applied: Artificial Bees Colony optimization and Particle Swarm optimization. Particular attention has been devoted to applying the algorithms in practice, in this case, to solving the problem of the optimal placement of reactive power sources. It has been shown that using a specially developed interface between an optimization algorithm and a model of the optimization problem allows researchers to quickly apply stochastic optimization algorithms. In addition, the influence of the heuristic algorithms parameters of Particle Swarm and Bee Swarm on their effectiveness has been explored. It has been shown that the reactive compensation can reduce the active power losses in the power supply system up to 20%, and the payback period of the installation of the necessary equipment is 2 to 4 years.