F. Dkhichi, B. Oukarfi, Y. El kouari, D. Ouoba, A. Fakkar, Z. Sabiri
{"title":"Performances of Artificial Neural Network combined with Perturb & Observe technique in maximizing the photovoltaic system power","authors":"F. Dkhichi, B. Oukarfi, Y. El kouari, D. Ouoba, A. Fakkar, Z. Sabiri","doi":"10.1109/IRSEC.2016.7984023","DOIUrl":null,"url":null,"abstract":"The optimization process of the production of photovoltaic power is crucial in order to ensure an optimal functioning of an output load. To do this, the use of a “maximum power point tracker” technique that allows maximization of the generated power by a photovoltaic generator is important. This maximization approach can be ensured by the use of a DC-DC converter “Boost”. In this paper we shed light on the combination of two techniques of the power maximization, completely different. The first one is classic “Perturb & Observe” while the second one is based on artificial intelligence “Artificial Neural Network”. The results obtained by the proposed method are compared with those obtained separately by each of the two methods, subject of the combination. This comparative study is aimed to show the different performances of the proposed method.","PeriodicalId":180557,"journal":{"name":"2016 International Renewable and Sustainable Energy Conference (IRSEC)","volume":"14 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Renewable and Sustainable Energy Conference (IRSEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRSEC.2016.7984023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The optimization process of the production of photovoltaic power is crucial in order to ensure an optimal functioning of an output load. To do this, the use of a “maximum power point tracker” technique that allows maximization of the generated power by a photovoltaic generator is important. This maximization approach can be ensured by the use of a DC-DC converter “Boost”. In this paper we shed light on the combination of two techniques of the power maximization, completely different. The first one is classic “Perturb & Observe” while the second one is based on artificial intelligence “Artificial Neural Network”. The results obtained by the proposed method are compared with those obtained separately by each of the two methods, subject of the combination. This comparative study is aimed to show the different performances of the proposed method.