{"title":"Implementation of Linear Quadratic Regulator in an Isolated Microgrid System","authors":"Prasun Sanki, M. Basu, P. Pal, D. Das","doi":"10.1109/VLSIDCS53788.2022.9811439","DOIUrl":null,"url":null,"abstract":"This paper elaborates a linear–quadratic–regulator (LQR) technique for an isolated microgrid in presence of electric vehicle (EV) and renewable power system (RPS) participation. Generally, in control theory the state feed-back control can place the poles in the desired locations in order to improve stability but many a time, it is observed that the steady state error is appropriately not achieved as well as the overall cost is compromised. In this connection, LQR control theory helps to obtain the feedback gain optimally using quadratic cost function. The weight adjustment matrices in LQR control theory requires to adjust in order to achieve desired system response. Hence, the weight matrices are adjusted to achieve optimal operating condition based on the proposed flow chart. Numerous, test cases are carried out considering different system configurations to validate performance and efficacy of the controller under MATLAB / Simulink environment.","PeriodicalId":307414,"journal":{"name":"2022 IEEE VLSI Device Circuit and System (VLSI DCS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE VLSI Device Circuit and System (VLSI DCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VLSIDCS53788.2022.9811439","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper elaborates a linear–quadratic–regulator (LQR) technique for an isolated microgrid in presence of electric vehicle (EV) and renewable power system (RPS) participation. Generally, in control theory the state feed-back control can place the poles in the desired locations in order to improve stability but many a time, it is observed that the steady state error is appropriately not achieved as well as the overall cost is compromised. In this connection, LQR control theory helps to obtain the feedback gain optimally using quadratic cost function. The weight adjustment matrices in LQR control theory requires to adjust in order to achieve desired system response. Hence, the weight matrices are adjusted to achieve optimal operating condition based on the proposed flow chart. Numerous, test cases are carried out considering different system configurations to validate performance and efficacy of the controller under MATLAB / Simulink environment.