Sandeep Sehgal, A. Swarnkar, N. Gupta, K. R. Niazi
{"title":"配电网在不同负荷方案下的减损重构","authors":"Sandeep Sehgal, A. Swarnkar, N. Gupta, K. R. Niazi","doi":"10.1109/SCEECS.2012.6184772","DOIUrl":null,"url":null,"abstract":"This paper presents a meta-heuristic based algorithm for determining the minimum loss reconfiguration for different load models in radial distribution system. Proposed method includes the genetic algorithm (GA) for optimizing the reconfiguration problem. After investigating the results obtained by the proposed method, it is clear that the type of load is a main deciding factor for reconfiguration problem and the methodology is capable to find out the optimal solution for any size of distribution systems and demands much less computational burden as compared to other existing algorithms. Simulation results are tested on widely used 33-node test distribution system and compared to the conventional constant load model.","PeriodicalId":372799,"journal":{"name":"2012 IEEE Students' Conference on Electrical, Electronics and Computer Science","volume":"111 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Reconfiguration of distribution network for loss reduction at different load schemes\",\"authors\":\"Sandeep Sehgal, A. Swarnkar, N. Gupta, K. R. Niazi\",\"doi\":\"10.1109/SCEECS.2012.6184772\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a meta-heuristic based algorithm for determining the minimum loss reconfiguration for different load models in radial distribution system. Proposed method includes the genetic algorithm (GA) for optimizing the reconfiguration problem. After investigating the results obtained by the proposed method, it is clear that the type of load is a main deciding factor for reconfiguration problem and the methodology is capable to find out the optimal solution for any size of distribution systems and demands much less computational burden as compared to other existing algorithms. Simulation results are tested on widely used 33-node test distribution system and compared to the conventional constant load model.\",\"PeriodicalId\":372799,\"journal\":{\"name\":\"2012 IEEE Students' Conference on Electrical, Electronics and Computer Science\",\"volume\":\"111 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE Students' Conference on Electrical, Electronics and Computer Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCEECS.2012.6184772\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Students' Conference on Electrical, Electronics and Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCEECS.2012.6184772","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Reconfiguration of distribution network for loss reduction at different load schemes
This paper presents a meta-heuristic based algorithm for determining the minimum loss reconfiguration for different load models in radial distribution system. Proposed method includes the genetic algorithm (GA) for optimizing the reconfiguration problem. After investigating the results obtained by the proposed method, it is clear that the type of load is a main deciding factor for reconfiguration problem and the methodology is capable to find out the optimal solution for any size of distribution systems and demands much less computational burden as compared to other existing algorithms. Simulation results are tested on widely used 33-node test distribution system and compared to the conventional constant load model.