{"title":"弱电网条件下优化自适应控制的电网交互光伏集成电动汽车充电系统","authors":"M. Aijaz, Ikhlaq Hussain, S. A. Lone","doi":"10.1109/GlobConHT56829.2023.10087504","DOIUrl":null,"url":null,"abstract":"This article presents a three phase system equipped with photovoltaic (PV) integration and (Electric Vehicle) EV functionality. The presented system possesses the capability of operating in utility connected mode as well as islanded mode. The DC link voltage ($V_{DC}$) is regulated by a transit search algorithm (TSA) PI controller to limit the dynamic and static error. The voltage regulation capability is tested across various simulation studies such as PV power fluctuations and characteristics of a weak grid such as load perturbation, load faults and grid voltage disturbance. Comparison is made between slime mould algorithm (SMA), genetic algorithm (GA) and TSA and it depicts improved performance in dynamic as well as static response. Dynamic error on an average of GA optimised system is 2.42% while SMA optimised system is 3.2% and TSA optimised has 2.6%. Static error of GA tuned system is 0.25%, 0.05% of SMA optimised system while only 0.025% of TSA. Other simulation studies prove the robustness of the system to dynamic power system conditions.","PeriodicalId":355921,"journal":{"name":"2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Grid Interactive PV Integrated EV Charging System with Optimised Adaptive Control Under Weak Grid Conditions\",\"authors\":\"M. Aijaz, Ikhlaq Hussain, S. A. Lone\",\"doi\":\"10.1109/GlobConHT56829.2023.10087504\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article presents a three phase system equipped with photovoltaic (PV) integration and (Electric Vehicle) EV functionality. The presented system possesses the capability of operating in utility connected mode as well as islanded mode. The DC link voltage ($V_{DC}$) is regulated by a transit search algorithm (TSA) PI controller to limit the dynamic and static error. The voltage regulation capability is tested across various simulation studies such as PV power fluctuations and characteristics of a weak grid such as load perturbation, load faults and grid voltage disturbance. Comparison is made between slime mould algorithm (SMA), genetic algorithm (GA) and TSA and it depicts improved performance in dynamic as well as static response. Dynamic error on an average of GA optimised system is 2.42% while SMA optimised system is 3.2% and TSA optimised has 2.6%. Static error of GA tuned system is 0.25%, 0.05% of SMA optimised system while only 0.025% of TSA. Other simulation studies prove the robustness of the system to dynamic power system conditions.\",\"PeriodicalId\":355921,\"journal\":{\"name\":\"2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT)\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GlobConHT56829.2023.10087504\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GlobConHT56829.2023.10087504","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Grid Interactive PV Integrated EV Charging System with Optimised Adaptive Control Under Weak Grid Conditions
This article presents a three phase system equipped with photovoltaic (PV) integration and (Electric Vehicle) EV functionality. The presented system possesses the capability of operating in utility connected mode as well as islanded mode. The DC link voltage ($V_{DC}$) is regulated by a transit search algorithm (TSA) PI controller to limit the dynamic and static error. The voltage regulation capability is tested across various simulation studies such as PV power fluctuations and characteristics of a weak grid such as load perturbation, load faults and grid voltage disturbance. Comparison is made between slime mould algorithm (SMA), genetic algorithm (GA) and TSA and it depicts improved performance in dynamic as well as static response. Dynamic error on an average of GA optimised system is 2.42% while SMA optimised system is 3.2% and TSA optimised has 2.6%. Static error of GA tuned system is 0.25%, 0.05% of SMA optimised system while only 0.025% of TSA. Other simulation studies prove the robustness of the system to dynamic power system conditions.