{"title":"智能电网相量测量单元的优化配置","authors":"S. Vijayalakshmi, D. Kavitha","doi":"10.1109/NPEC.2018.8476724","DOIUrl":null,"url":null,"abstract":"One of the most important requirements for smart grid execution is quick, accurate and synchronized measurements, these can be done by using phasor measurement unit (PMU). To achieve full observability, PMUs should be placed optimally because the cost of PMU is very high. While placing PMU, importance may be given to weak buses, generator buses etc. Genetic Algorithm (GA) is used to solve optimal placement of PMU (OPP) with fully observability condition for base case and contingency case. SBX cross over and polynomial mutation is used in this algorithm in order to get best solution. IEEE 14 bus system, IEEE 30 bus system and IEEE 57 bus system have been taken to test this method. The results are shows the ability of GA to find the best solution for reducing the total number of PMUs with complete observability. To prove the ruggedness of this approach, the results are differentiated with other techniques.","PeriodicalId":170822,"journal":{"name":"2018 National Power Engineering Conference (NPEC)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Optimal Placement of Phasor Measurement Units for Smart Grid Applications\",\"authors\":\"S. Vijayalakshmi, D. Kavitha\",\"doi\":\"10.1109/NPEC.2018.8476724\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the most important requirements for smart grid execution is quick, accurate and synchronized measurements, these can be done by using phasor measurement unit (PMU). To achieve full observability, PMUs should be placed optimally because the cost of PMU is very high. While placing PMU, importance may be given to weak buses, generator buses etc. Genetic Algorithm (GA) is used to solve optimal placement of PMU (OPP) with fully observability condition for base case and contingency case. SBX cross over and polynomial mutation is used in this algorithm in order to get best solution. IEEE 14 bus system, IEEE 30 bus system and IEEE 57 bus system have been taken to test this method. The results are shows the ability of GA to find the best solution for reducing the total number of PMUs with complete observability. To prove the ruggedness of this approach, the results are differentiated with other techniques.\",\"PeriodicalId\":170822,\"journal\":{\"name\":\"2018 National Power Engineering Conference (NPEC)\",\"volume\":\"124 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 National Power Engineering Conference (NPEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NPEC.2018.8476724\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 National Power Engineering Conference (NPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NPEC.2018.8476724","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal Placement of Phasor Measurement Units for Smart Grid Applications
One of the most important requirements for smart grid execution is quick, accurate and synchronized measurements, these can be done by using phasor measurement unit (PMU). To achieve full observability, PMUs should be placed optimally because the cost of PMU is very high. While placing PMU, importance may be given to weak buses, generator buses etc. Genetic Algorithm (GA) is used to solve optimal placement of PMU (OPP) with fully observability condition for base case and contingency case. SBX cross over and polynomial mutation is used in this algorithm in order to get best solution. IEEE 14 bus system, IEEE 30 bus system and IEEE 57 bus system have been taken to test this method. The results are shows the ability of GA to find the best solution for reducing the total number of PMUs with complete observability. To prove the ruggedness of this approach, the results are differentiated with other techniques.