{"title":"基于遗传算法的无功规划中多个tscs的最优位置和初始参数设置","authors":"N. Padhy, M. Abdel-Moamen, B. Praveen Kumar","doi":"10.1109/PES.2004.1373013","DOIUrl":null,"url":null,"abstract":"In this paper, a genetic algorithms based optimal reactive power planning model incorporating FACTS devices has been presented. Optimal placement of multiple FACTS devices will naturally control the overall reactive power requirements. But the mathematical complexity and hence the solution time increases for reactive power planning of large power networks with multiple FACTS devices. To obtain a feasible and suboptimal solution for reactive power planning, optimal location of FACTS devices and its parameters have been determined using simple genetic algorithms. Genetic algorithm, performed on two parameters: the optimal location of the devices and their control parameter and then the fitness function has been determined using quasi-Newton algorithm based optimal power flows for minimization of reactive power losses and generations. The performance of the proposed algorithm has been tested for IEEE-30 systems with multiple TCSC devices. It has also been observed that the proposed algorithm can be applied to larger systems and do not suffer with computational difficulties.","PeriodicalId":236779,"journal":{"name":"IEEE Power Engineering Society General Meeting, 2004.","volume":"18 8","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"31","resultStr":"{\"title\":\"Optimal location and initial parameter settings of multiple TCSCs for reactive power planning using genetic algorithms\",\"authors\":\"N. Padhy, M. Abdel-Moamen, B. Praveen Kumar\",\"doi\":\"10.1109/PES.2004.1373013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a genetic algorithms based optimal reactive power planning model incorporating FACTS devices has been presented. Optimal placement of multiple FACTS devices will naturally control the overall reactive power requirements. But the mathematical complexity and hence the solution time increases for reactive power planning of large power networks with multiple FACTS devices. To obtain a feasible and suboptimal solution for reactive power planning, optimal location of FACTS devices and its parameters have been determined using simple genetic algorithms. Genetic algorithm, performed on two parameters: the optimal location of the devices and their control parameter and then the fitness function has been determined using quasi-Newton algorithm based optimal power flows for minimization of reactive power losses and generations. The performance of the proposed algorithm has been tested for IEEE-30 systems with multiple TCSC devices. It has also been observed that the proposed algorithm can be applied to larger systems and do not suffer with computational difficulties.\",\"PeriodicalId\":236779,\"journal\":{\"name\":\"IEEE Power Engineering Society General Meeting, 2004.\",\"volume\":\"18 8\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"31\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Power Engineering Society General Meeting, 2004.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PES.2004.1373013\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Power Engineering Society General Meeting, 2004.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PES.2004.1373013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal location and initial parameter settings of multiple TCSCs for reactive power planning using genetic algorithms
In this paper, a genetic algorithms based optimal reactive power planning model incorporating FACTS devices has been presented. Optimal placement of multiple FACTS devices will naturally control the overall reactive power requirements. But the mathematical complexity and hence the solution time increases for reactive power planning of large power networks with multiple FACTS devices. To obtain a feasible and suboptimal solution for reactive power planning, optimal location of FACTS devices and its parameters have been determined using simple genetic algorithms. Genetic algorithm, performed on two parameters: the optimal location of the devices and their control parameter and then the fitness function has been determined using quasi-Newton algorithm based optimal power flows for minimization of reactive power losses and generations. The performance of the proposed algorithm has been tested for IEEE-30 systems with multiple TCSC devices. It has also been observed that the proposed algorithm can be applied to larger systems and do not suffer with computational difficulties.