{"title":"基于模糊gsa的多目标VAr规划","authors":"Sina Ebrahimi Farsangi, E. Rashedi, M. Farsangi","doi":"10.1109/CSIEC.2017.7940180","DOIUrl":null,"url":null,"abstract":"In this study, fuzzy logic technique and Gravitational Search Algorithm (GSA) are applied to place Static VAr Compensator (SVC) to improve voltage stability through a multi-objective placement problem. The VAr planning is formulated to maximize indexes of fuzzy performance including: deviation of bus voltage, loss of system, and the cost of installation. The results obtained are compared with fuzzy Real Genetic Algorithm (RGA). The results obtained show that the GSA has better convergence rate comparing to fuzzy RGA in finding the best solution.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Multi-objective VAr planning using fuzzy-GSA\",\"authors\":\"Sina Ebrahimi Farsangi, E. Rashedi, M. Farsangi\",\"doi\":\"10.1109/CSIEC.2017.7940180\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, fuzzy logic technique and Gravitational Search Algorithm (GSA) are applied to place Static VAr Compensator (SVC) to improve voltage stability through a multi-objective placement problem. The VAr planning is formulated to maximize indexes of fuzzy performance including: deviation of bus voltage, loss of system, and the cost of installation. The results obtained are compared with fuzzy Real Genetic Algorithm (RGA). The results obtained show that the GSA has better convergence rate comparing to fuzzy RGA in finding the best solution.\",\"PeriodicalId\":166046,\"journal\":{\"name\":\"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSIEC.2017.7940180\",\"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 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSIEC.2017.7940180","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this study, fuzzy logic technique and Gravitational Search Algorithm (GSA) are applied to place Static VAr Compensator (SVC) to improve voltage stability through a multi-objective placement problem. The VAr planning is formulated to maximize indexes of fuzzy performance including: deviation of bus voltage, loss of system, and the cost of installation. The results obtained are compared with fuzzy Real Genetic Algorithm (RGA). The results obtained show that the GSA has better convergence rate comparing to fuzzy RGA in finding the best solution.
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