{"title":"Optimal Location and Sizing of Wind-Turbine Generation using Grey Wolf Optimizer","authors":"Z. M. Yasin, N. A. Salim, H. Mohamad","doi":"10.1109/ICPEA53519.2022.9744673","DOIUrl":null,"url":null,"abstract":"Integration of Wind-Turbine Generation (WTG) in distribution network has proven to bring various benefits such as carbon emission reduction, power quality improvement, power loss reduction etc. However, the inappropriate planning of WTG will result in greater system losses, incurred higher installation costs and worsen the performance. Therefore, in this paper, Grey Wolf Optimizer (GWO) is proposed to solve the optimal location and sizing of WTG problems. GWO is an optimization technique developed by Seyedali Mirjalili based on the searching and hunting behavior of grey wolf. To analyze the effectiveness of the aforementioned technique, GWO is applied in IEEE-69 bus radial distribution test system for various objective functions such as total cost minimization, voltage profile improvement and power loss minimization. Evolutionary Programming (EP) is used to compare the simulated results. From the results obtained, it shows that GWO provide better solutions for all three objective functions with faster computation time.","PeriodicalId":371063,"journal":{"name":"2022 IEEE International Conference in Power Engineering Application (ICPEA)","volume":"339 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference in Power Engineering Application (ICPEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPEA53519.2022.9744673","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
Integration of Wind-Turbine Generation (WTG) in distribution network has proven to bring various benefits such as carbon emission reduction, power quality improvement, power loss reduction etc. However, the inappropriate planning of WTG will result in greater system losses, incurred higher installation costs and worsen the performance. Therefore, in this paper, Grey Wolf Optimizer (GWO) is proposed to solve the optimal location and sizing of WTG problems. GWO is an optimization technique developed by Seyedali Mirjalili based on the searching and hunting behavior of grey wolf. To analyze the effectiveness of the aforementioned technique, GWO is applied in IEEE-69 bus radial distribution test system for various objective functions such as total cost minimization, voltage profile improvement and power loss minimization. Evolutionary Programming (EP) is used to compare the simulated results. From the results obtained, it shows that GWO provide better solutions for all three objective functions with faster computation time.