{"title":"提出了蚁群算法在电力系统PMU布局优化中的应用","authors":"Bo Wang, Dichen Liu, Li Xiong","doi":"10.1109/IPEMC.2009.5157814","DOIUrl":null,"url":null,"abstract":"GPS-based synchronous phasor measurement technology is a powerful tool for the security and reliable operation of the inter-connected electric power system. This paper presents an ACO-based approach to optimize the phasor measurement unit (PMU) placement problem. The pheromone trail persistence coefficient adaptive adjustment mechanism and stochastic perturbing progress are introduced into the Ant Colony System(ACS), in case the algorithm entering the stagnation behavior and getting stuck at local minima. The improved algorithm outperforms the ACS in obtaining global optimal solution and convergence speed, when applied to optimizing the PMU placement problem. A graph-theoretic procedure based on depth first search is adopted to analyze system observability. Simulation results in optimizing a provincial 46-bus system PMU placement problem show that the improved ACS algorithm is effective.","PeriodicalId":375971,"journal":{"name":"2009 IEEE 6th International Power Electronics and Motion Control Conference","volume":"250 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Advance ACO system in optimizing power system PMU placement problem\",\"authors\":\"Bo Wang, Dichen Liu, Li Xiong\",\"doi\":\"10.1109/IPEMC.2009.5157814\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"GPS-based synchronous phasor measurement technology is a powerful tool for the security and reliable operation of the inter-connected electric power system. This paper presents an ACO-based approach to optimize the phasor measurement unit (PMU) placement problem. The pheromone trail persistence coefficient adaptive adjustment mechanism and stochastic perturbing progress are introduced into the Ant Colony System(ACS), in case the algorithm entering the stagnation behavior and getting stuck at local minima. The improved algorithm outperforms the ACS in obtaining global optimal solution and convergence speed, when applied to optimizing the PMU placement problem. A graph-theoretic procedure based on depth first search is adopted to analyze system observability. Simulation results in optimizing a provincial 46-bus system PMU placement problem show that the improved ACS algorithm is effective.\",\"PeriodicalId\":375971,\"journal\":{\"name\":\"2009 IEEE 6th International Power Electronics and Motion Control Conference\",\"volume\":\"250 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE 6th International Power Electronics and Motion Control Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IPEMC.2009.5157814\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE 6th International Power Electronics and Motion Control Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPEMC.2009.5157814","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Advance ACO system in optimizing power system PMU placement problem
GPS-based synchronous phasor measurement technology is a powerful tool for the security and reliable operation of the inter-connected electric power system. This paper presents an ACO-based approach to optimize the phasor measurement unit (PMU) placement problem. The pheromone trail persistence coefficient adaptive adjustment mechanism and stochastic perturbing progress are introduced into the Ant Colony System(ACS), in case the algorithm entering the stagnation behavior and getting stuck at local minima. The improved algorithm outperforms the ACS in obtaining global optimal solution and convergence speed, when applied to optimizing the PMU placement problem. A graph-theoretic procedure based on depth first search is adopted to analyze system observability. Simulation results in optimizing a provincial 46-bus system PMU placement problem show that the improved ACS algorithm is effective.