Improving the performance of photovoltaic module during partial shading using ANN

• Published in: International Journal of Power Electronics and Drive Systems Vol. 12; no. 4; p. 2435
• Main Authors: Hashim, Hadi Fakhir, Kareem, Marwah M., Al-Azzawi, Waleed Khalid, Ali, Adnan H.
• Format: Journal Article
• Language: English
• Published: Yogyakarta IAES Institute of Advanced Engineering and Science 01.12.2021
• Subjects: Artificial neural networks; Environmental effects; Hybrid systems; Modules; Neural networks; Panels; Photovoltaic cells; Power efficiency; Power management; Shading; Solar radiation
• ISSN: 2088-8694; 2722-256X; 2088-8694
• DOI: 10.11591/ijpeds.v12.i4.pp2435-2442

Photovoltaic (PV) panels have drawback of having their peak power reduced when clouds or shade are present. Furthermore, it is only available while the sun shine. Nearby structures, plants, bird droppings, and other obstacles shade operating photovoltaic (PV) devices, effectively reducing the incident solar radiation produced by the modules. When these PV panels are exposed to partial shading, their power efficiency is reduced. A neural network with a kind of artificial neural network is used in the suggested hybrid method (ANN). The key focus of this article is to use environmental effects dependent on partial shading to get the maximum performance from a solar system. The suggested hybrid solution is tested in the MATLAB/Simulink working platform using partial shading test cases, and the efficiency is compared to other approaches. Additionally, the best options for the suggested procedure, current, voltage, and power are examined.