{"title":"Load Frequency Control of Two Area and Multi Source Power System Using Grey Wolf Optimization Algorithm","authors":"A. Doğan","doi":"10.23919/ELECO47770.2019.8990643","DOIUrl":null,"url":null,"abstract":"In this study, load frequency of two area interconnected power systems are controlled based on Proportional Integral Derivative (PID) controller structures and gain parameters of controllers are decided using Grey Wolf Optimization (GWO) algorithm. Dynamic response of the proposed structure is investigated considering integral of time multiplied absolute error (ITEA) as cost function in a two area and multi source power system. Capability and efficiency of GWO algorithm is illustrated in comparison to Particle Swarm Optimization (PSO) and Artificial Bee colony (ABC). It is observed that GWO provides minimum value of cost function and better dynamic response among the considered algorithms.","PeriodicalId":6611,"journal":{"name":"2019 11th International Conference on Electrical and Electronics Engineering (ELECO)","volume":"35 9","pages":"81-84"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 11th International Conference on Electrical and Electronics Engineering (ELECO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/ELECO47770.2019.8990643","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this study, load frequency of two area interconnected power systems are controlled based on Proportional Integral Derivative (PID) controller structures and gain parameters of controllers are decided using Grey Wolf Optimization (GWO) algorithm. Dynamic response of the proposed structure is investigated considering integral of time multiplied absolute error (ITEA) as cost function in a two area and multi source power system. Capability and efficiency of GWO algorithm is illustrated in comparison to Particle Swarm Optimization (PSO) and Artificial Bee colony (ABC). It is observed that GWO provides minimum value of cost function and better dynamic response among the considered algorithms.
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