{"title":"Parameter optimization of power system stabilizer via Salp Swarm algorithm","authors":"Serdar Ekinci, B. Hekimoğlu","doi":"10.1109/ICEEE2.2018.8391318","DOIUrl":null,"url":null,"abstract":"A novel application of a very recent heuristic-based method, namely Salp Swarm Algorithm (SSA) is presented here for tuning of power system stabilizer (PSS) in a multi- machine power system. The tuning problem of PSS parameters is expressed as an optimization problem and the SSA method is utilized for searching the optimal parameters. The efficacy of the SSA-based PSS design was successfully tested on a well- known 3-machine, 9-bus power system. The results are comparatively evaluated with the other results obtained by the Tabu Search (TS) and the Biogeography-Based Optimization (BBO) methods. From the eigenvalue analysis and nonlinear simulation results it is confirmed that for damping oscillations, the performance of the proposed SSA approach in this study is better than that obtained by other intelligent techniques (TS and BBO).","PeriodicalId":6482,"journal":{"name":"2018 5th International Conference on Electrical and Electronic Engineering (ICEEE)","volume":"1 1","pages":"143-147"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"65","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 5th International Conference on Electrical and Electronic Engineering (ICEEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEE2.2018.8391318","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 65
Abstract
A novel application of a very recent heuristic-based method, namely Salp Swarm Algorithm (SSA) is presented here for tuning of power system stabilizer (PSS) in a multi- machine power system. The tuning problem of PSS parameters is expressed as an optimization problem and the SSA method is utilized for searching the optimal parameters. The efficacy of the SSA-based PSS design was successfully tested on a well- known 3-machine, 9-bus power system. The results are comparatively evaluated with the other results obtained by the Tabu Search (TS) and the Biogeography-Based Optimization (BBO) methods. From the eigenvalue analysis and nonlinear simulation results it is confirmed that for damping oscillations, the performance of the proposed SSA approach in this study is better than that obtained by other intelligent techniques (TS and BBO).
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