Don Gamage, Xibeng Zhang, A. Ukil, Chathura Wanigasekara, A. Swain
{"title":"直流微电网的ANFIS控制器设计","authors":"Don Gamage, Xibeng Zhang, A. Ukil, Chathura Wanigasekara, A. Swain","doi":"10.1109/ICEPE50861.2021.9404439","DOIUrl":null,"url":null,"abstract":"An adaptive neuro-fuzzy inference system (ANFIS) controller is developed and presented in this study to control hybrid energy storage system (HESS) which combines the battery and super-capacitor (SC). The battery compensates the energy requirement for a longer duration while the SC limits the stress on battery caused by the power fluctuations during transient period which alternately gives longer life span for the battery while regulate the DC link voltage constant. The proposed ANFIS controller is being compared for performance with various other controllers including the reinforcement controller based on Q-learning proportional and integral (PI) controller, fuzzy controller and conventional PI controller. Further, the state of charge (SOC) of the battery and SC are monitored in order to decide the required optimal amount of power or energy for the HESS in deficit/excess modes. The results of the simulation, in different loading conditions, indicate that the ANFIS's controller performance for the DC microgrid is superior compared to others.","PeriodicalId":250203,"journal":{"name":"2020 3rd International Conference on Energy, Power and Environment: Towards Clean Energy Technologies","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Design of ANFIS Controller for a DC Microgrid\",\"authors\":\"Don Gamage, Xibeng Zhang, A. Ukil, Chathura Wanigasekara, A. Swain\",\"doi\":\"10.1109/ICEPE50861.2021.9404439\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An adaptive neuro-fuzzy inference system (ANFIS) controller is developed and presented in this study to control hybrid energy storage system (HESS) which combines the battery and super-capacitor (SC). The battery compensates the energy requirement for a longer duration while the SC limits the stress on battery caused by the power fluctuations during transient period which alternately gives longer life span for the battery while regulate the DC link voltage constant. The proposed ANFIS controller is being compared for performance with various other controllers including the reinforcement controller based on Q-learning proportional and integral (PI) controller, fuzzy controller and conventional PI controller. Further, the state of charge (SOC) of the battery and SC are monitored in order to decide the required optimal amount of power or energy for the HESS in deficit/excess modes. The results of the simulation, in different loading conditions, indicate that the ANFIS's controller performance for the DC microgrid is superior compared to others.\",\"PeriodicalId\":250203,\"journal\":{\"name\":\"2020 3rd International Conference on Energy, Power and Environment: Towards Clean Energy Technologies\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-03-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 3rd International Conference on Energy, Power and Environment: Towards Clean Energy Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEPE50861.2021.9404439\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 3rd International Conference on Energy, Power and Environment: Towards Clean Energy Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEPE50861.2021.9404439","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An adaptive neuro-fuzzy inference system (ANFIS) controller is developed and presented in this study to control hybrid energy storage system (HESS) which combines the battery and super-capacitor (SC). The battery compensates the energy requirement for a longer duration while the SC limits the stress on battery caused by the power fluctuations during transient period which alternately gives longer life span for the battery while regulate the DC link voltage constant. The proposed ANFIS controller is being compared for performance with various other controllers including the reinforcement controller based on Q-learning proportional and integral (PI) controller, fuzzy controller and conventional PI controller. Further, the state of charge (SOC) of the battery and SC are monitored in order to decide the required optimal amount of power or energy for the HESS in deficit/excess modes. The results of the simulation, in different loading conditions, indicate that the ANFIS's controller performance for the DC microgrid is superior compared to others.