{"title":"Metaheuristic-Based Control for Three-Phase Grid-Connected Solar Photovoltaic Systems","authors":"Afef Badis, Mohamed Habib Boujmil, M. Mansouri","doi":"10.4018/ijeoe.310003","DOIUrl":null,"url":null,"abstract":"In this paper, a novel cascade control technique is proposed in order to identify the parameters of cascade controllers in a grid-connected photovoltaic (PV) system. Here, tuning of the inner and outer loop controllers is done simultaneously by means of an optimized genetic algorithm-based fractional order PID (GA-FOPID) control. Simulations are conducted using Matlab/Simulink software under different operating conditions, namely under fast-changing weather conditions, sudden parametric variations, and voltage dip, for the purpose of verifying the effectiveness of the proposed control strategy. By comparing the results with recently published optimization techniques such as particle swarm optimization (PSO) and ant colony optimization (ACO), the superiority and effectiveness of the proposed GA-FOPID control have been proven.","PeriodicalId":43245,"journal":{"name":"International Journal of Energy Optimization and Engineering","volume":" ","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Energy Optimization and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijeoe.310003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, a novel cascade control technique is proposed in order to identify the parameters of cascade controllers in a grid-connected photovoltaic (PV) system. Here, tuning of the inner and outer loop controllers is done simultaneously by means of an optimized genetic algorithm-based fractional order PID (GA-FOPID) control. Simulations are conducted using Matlab/Simulink software under different operating conditions, namely under fast-changing weather conditions, sudden parametric variations, and voltage dip, for the purpose of verifying the effectiveness of the proposed control strategy. By comparing the results with recently published optimization techniques such as particle swarm optimization (PSO) and ant colony optimization (ACO), the superiority and effectiveness of the proposed GA-FOPID control have been proven.