{"title":"Cascaded Controller Tuned UPQC for PQ Enhancement in EV Connected Charging Station","authors":"Jatoth Rajender, Manisha Dubey, Yogendra Kumar","doi":"10.1002/rnc.70013","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Power grids are a byproduct of the development of conventional power systems. They comprise electronic power converters and a variety of renewable energy sources. Power quality (PQ) controllers for renewable sources have been modeled in order to handle the high energy demand. Although numerous solutions have been utilized thus far, the PQ issue demands specific consideration. Hence, the unified power quality conditioner (UPQC) is proposed to address the problem of PQ enhancement in electric vehicle (EV) connected charging stations (CS). For tuning UPQC, the tilt and proportional integral derivatives are cascaded (C-TI-PI) together. In addition, the golden search optimization algorithm (GSOA) is included to control the battery's charging behavior of the bidirectional buck boost converter. The implementation is carried out through MATLAB/Simulink, and the observed result shows the stability and efficacy of the proposed controller. The implementation results are analyzed through two cases to validate the robustness of a proposed C-TI-PI-based UPQC controller. Moreover, the total harmonic distortions (THD) are also determined for the proposed strategy and compared with the traditional methods. The overall characteristic response reveals the effectiveness of a proposed controller.</p>\n </div>","PeriodicalId":50291,"journal":{"name":"International Journal of Robust and Nonlinear Control","volume":"35 15","pages":"6589-6603"},"PeriodicalIF":3.2000,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Robust and Nonlinear Control","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/rnc.70013","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Power grids are a byproduct of the development of conventional power systems. They comprise electronic power converters and a variety of renewable energy sources. Power quality (PQ) controllers for renewable sources have been modeled in order to handle the high energy demand. Although numerous solutions have been utilized thus far, the PQ issue demands specific consideration. Hence, the unified power quality conditioner (UPQC) is proposed to address the problem of PQ enhancement in electric vehicle (EV) connected charging stations (CS). For tuning UPQC, the tilt and proportional integral derivatives are cascaded (C-TI-PI) together. In addition, the golden search optimization algorithm (GSOA) is included to control the battery's charging behavior of the bidirectional buck boost converter. The implementation is carried out through MATLAB/Simulink, and the observed result shows the stability and efficacy of the proposed controller. The implementation results are analyzed through two cases to validate the robustness of a proposed C-TI-PI-based UPQC controller. Moreover, the total harmonic distortions (THD) are also determined for the proposed strategy and compared with the traditional methods. The overall characteristic response reveals the effectiveness of a proposed controller.
电网是传统电力系统发展的副产品。它们包括电子电源转换器和各种可再生能源。为了处理高能源需求,对可再生能源的电能质量(PQ)控制器进行建模。尽管到目前为止已经使用了许多解决方案,但PQ问题需要具体考虑。为此,提出统一电能质量调节器(UPQC)来解决电动汽车联网充电站(CS)的PQ提升问题。为了调整UPQC,倾斜和比例积分导数级联在一起(C-TI-PI)。此外,采用黄金搜索优化算法(GSOA)对双向降压升压变换器的电池充电行为进行控制。通过MATLAB/Simulink进行了实现,实验结果表明了所设计控制器的稳定性和有效性。通过两个实例分析了实现结果,验证了所提出的基于c - ti - pi的UPQC控制器的鲁棒性。此外,还确定了该策略的总谐波失真(THD),并与传统方法进行了比较。总体特征响应表明所提控制器的有效性。
期刊介绍:
Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.