Mohamed A. Khafagy, S. Abdellatif, R. Swief, H. Ghali
{"title":"Optimizing The Performance Of Photovoltaic Systems Under Non-uniform Irradiance Using ANN-MPPT Algorithm","authors":"Mohamed A. Khafagy, S. Abdellatif, R. Swief, H. Ghali","doi":"10.1109/CPERE56564.2023.10119640","DOIUrl":null,"url":null,"abstract":"Photovoltaic (PV) systems are usually designed based on the standard operating condition in a specific location. However, harsh environmental conditions impact the PV array during regular operation. One of these harsh conditions can be the non-uniform irradiance spatially or temporally. Herein, the maximum power point tracking (MPPT) systems are essential in maintaining the operation under the maximum possible conditions. Artificial neural network (ANN) techniques have attracted researchers’ attention as an up-to-date artificial alternative in MPPT algorithms. We demonstrated two PV systems, on-grid and off-grid, with a power capacity of 5 kW. The proposed ANN model showed enhanced performance concerning conventional ones with a minimum response time of 0.0075 se","PeriodicalId":169048,"journal":{"name":"2023 IEEE Conference on Power Electronics and Renewable Energy (CPERE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Conference on Power Electronics and Renewable Energy (CPERE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CPERE56564.2023.10119640","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
Photovoltaic (PV) systems are usually designed based on the standard operating condition in a specific location. However, harsh environmental conditions impact the PV array during regular operation. One of these harsh conditions can be the non-uniform irradiance spatially or temporally. Herein, the maximum power point tracking (MPPT) systems are essential in maintaining the operation under the maximum possible conditions. Artificial neural network (ANN) techniques have attracted researchers’ attention as an up-to-date artificial alternative in MPPT algorithms. We demonstrated two PV systems, on-grid and off-grid, with a power capacity of 5 kW. The proposed ANN model showed enhanced performance concerning conventional ones with a minimum response time of 0.0075 se
光伏(PV)系统通常是根据特定位置的标准运行条件设计的。然而,恶劣的环境条件会影响光伏阵列的正常运行。这些恶劣条件之一可能是空间或时间上的不均匀辐照度。在此,最大功率点跟踪(MPPT)系统对于维持最大可能条件下的运行至关重要。人工神经网络(ANN)技术作为MPPT算法中最新的人工替代技术引起了研究人员的关注。示范并网和离网两套光伏发电系统,装机容量为5千瓦。与传统的人工神经网络模型相比,该模型的性能得到了显著提高,最小响应时间为0.0075 se