{"title":"基于滞后电流控制逆变器的光伏系统神经网络MPPT控制方案","authors":"R. Dubey","doi":"10.1109/RAECS.2014.6799516","DOIUrl":null,"url":null,"abstract":"These days photovoltaic panel are the one of the main source of renewable power. This panel gives the dc power which can be directly used in dc power application. In our daily life we generally work with ac load. Hence an inverter is proposed in this paper which will provide a robust operation and very simple to implement. A hysteresis current controlled inverter is proposed with fixed band and the value of the load variation is determined with output current THD lower than 5%. Inverter is developed with three level techniques. Maximum power tracking of panel power is done by constructing artificial neural network. System performance is measured in terms of the efficiency of the MPPT controller and flexibility in the inverter operation for standalone load.","PeriodicalId":229600,"journal":{"name":"2014 Recent Advances in Engineering and Computational Sciences (RAECS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Neural network MPPT control scheme with hysteresis current controlled inverter for photovoltaic system\",\"authors\":\"R. Dubey\",\"doi\":\"10.1109/RAECS.2014.6799516\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"These days photovoltaic panel are the one of the main source of renewable power. This panel gives the dc power which can be directly used in dc power application. In our daily life we generally work with ac load. Hence an inverter is proposed in this paper which will provide a robust operation and very simple to implement. A hysteresis current controlled inverter is proposed with fixed band and the value of the load variation is determined with output current THD lower than 5%. Inverter is developed with three level techniques. Maximum power tracking of panel power is done by constructing artificial neural network. System performance is measured in terms of the efficiency of the MPPT controller and flexibility in the inverter operation for standalone load.\",\"PeriodicalId\":229600,\"journal\":{\"name\":\"2014 Recent Advances in Engineering and Computational Sciences (RAECS)\",\"volume\":\"67 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-03-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 Recent Advances in Engineering and Computational Sciences (RAECS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RAECS.2014.6799516\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Recent Advances in Engineering and Computational Sciences (RAECS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAECS.2014.6799516","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neural network MPPT control scheme with hysteresis current controlled inverter for photovoltaic system
These days photovoltaic panel are the one of the main source of renewable power. This panel gives the dc power which can be directly used in dc power application. In our daily life we generally work with ac load. Hence an inverter is proposed in this paper which will provide a robust operation and very simple to implement. A hysteresis current controlled inverter is proposed with fixed band and the value of the load variation is determined with output current THD lower than 5%. Inverter is developed with three level techniques. Maximum power tracking of panel power is done by constructing artificial neural network. System performance is measured in terms of the efficiency of the MPPT controller and flexibility in the inverter operation for standalone load.