{"title":"Reactive Power Management by Optimal Positioning of FACTS Controllers using MFO Algorithm","authors":"Lalit Kumar, M. M. Kar, Sanjay Kumar","doi":"10.1109/ETI4.051663.2021.9619433","DOIUrl":null,"url":null,"abstract":"In this article, the moth flame optimization (MFO) algorithm is applied for reactive power planning (RPP) by placing FACTS controllers in optimal position. The main objective of the article is to minimize the real power loss considering different loading conditions. Furthermore, the operating cost of the transmission system and voltage profile is evaluated which plays a critical role in choosing the Controller based technique is compared with some other evolutionary techniques like Particle Swarm Optimization (PSO) and Biogeography-based optimization (BBO) which is applied on IEEE 30 bus system. The superiority of MFO is demonstrated in terms of real power losses, operating cost, and voltage profile of the system compared to PSO and BBO techniques and can be suggested for RPP.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETI4.051663.2021.9619433","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
In this article, the moth flame optimization (MFO) algorithm is applied for reactive power planning (RPP) by placing FACTS controllers in optimal position. The main objective of the article is to minimize the real power loss considering different loading conditions. Furthermore, the operating cost of the transmission system and voltage profile is evaluated which plays a critical role in choosing the Controller based technique is compared with some other evolutionary techniques like Particle Swarm Optimization (PSO) and Biogeography-based optimization (BBO) which is applied on IEEE 30 bus system. The superiority of MFO is demonstrated in terms of real power losses, operating cost, and voltage profile of the system compared to PSO and BBO techniques and can be suggested for RPP.