A. Indira, Swarnasri Mandal, P. Acharjee, S. S. Thakur
{"title":"Load margin analysis for steady state voltage stability using PSO technique","authors":"A. Indira, Swarnasri Mandal, P. Acharjee, S. S. Thakur","doi":"10.1109/ICPWS.2009.5442658","DOIUrl":null,"url":null,"abstract":"For steady state voltage stability, load margin analysis is one of the efficient procedures. The mathematical modeling of load margin is formulated as an optimization problem considering in-equality constraints - voltage limits and reactive power generation limits of PV buses. Two particle swarm optimization (PSO) techniques namely adaptive PSO (APSO) and Hybrid PSO (HPSO) are developed. In APSO technique, new formulas are designed to get adaptive tuning parameters. In HPSO technique, breeding and subpopulation of genetic algorithm (GA) are incorporated in PSO to add diversity & to overcome local optima. The effectiveness and efficiency of the proposed techniques is established giving different test results of IEEE standard systems.","PeriodicalId":337759,"journal":{"name":"2009 International Conference on Power Systems","volume":"113 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Power Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPWS.2009.5442658","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
For steady state voltage stability, load margin analysis is one of the efficient procedures. The mathematical modeling of load margin is formulated as an optimization problem considering in-equality constraints - voltage limits and reactive power generation limits of PV buses. Two particle swarm optimization (PSO) techniques namely adaptive PSO (APSO) and Hybrid PSO (HPSO) are developed. In APSO technique, new formulas are designed to get adaptive tuning parameters. In HPSO technique, breeding and subpopulation of genetic algorithm (GA) are incorporated in PSO to add diversity & to overcome local optima. The effectiveness and efficiency of the proposed techniques is established giving different test results of IEEE standard systems.