Kevin Steven Morgado Gómez, Néstor Germán Bolívar Pulgarín
{"title":"A comparative analysis of bio- inspired optimization algorithms for Optimal Reactive Power Dispatch","authors":"Kevin Steven Morgado Gómez, Néstor Germán Bolívar Pulgarín","doi":"10.1109/ice3is54102.2021.9649712","DOIUrl":null,"url":null,"abstract":"In this paper, it is developed an analysis of the usage of meta-heuristic models inspired by nature to obtain the Optimal Reactive Power Dispatch of a 30 bus system. Firstly, the obj ective function is presented, considering its power flow parameters. Afterward, the main algorithms were introduced the Grey Wolf Optimization (GWO), its improved version (1-GWO), and the Whale Optimization Algorithm (WOA). Then, they were implemented on the IEEE 30 bus system to calculate the obj ective function with 13 decision variables and 7 restrictions. Finally, the main results were presented, where it is highlighted an improvement in the Objective Function of 0.45%, in comparison with the traditional Optimal Power Flow implemented in Matpower.","PeriodicalId":134945,"journal":{"name":"2021 1st International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 1st International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ice3is54102.2021.9649712","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
In this paper, it is developed an analysis of the usage of meta-heuristic models inspired by nature to obtain the Optimal Reactive Power Dispatch of a 30 bus system. Firstly, the obj ective function is presented, considering its power flow parameters. Afterward, the main algorithms were introduced the Grey Wolf Optimization (GWO), its improved version (1-GWO), and the Whale Optimization Algorithm (WOA). Then, they were implemented on the IEEE 30 bus system to calculate the obj ective function with 13 decision variables and 7 restrictions. Finally, the main results were presented, where it is highlighted an improvement in the Objective Function of 0.45%, in comparison with the traditional Optimal Power Flow implemented in Matpower.