A Machine Learning Based Algorithm to Process Partial Shading Effects in PV Arrays

Abstract

Solar energy is a widely used type of renewable energy. Photovoltaic arrays are used to harvest solar energy. The major goal, in harvesting the maximum possible power, is to operate the system at its maximum power point (MPP). If the irradiation conditions are uniform, the P-V curve of the PV array has only one peak that is called its MPP. But when the irradiation conditions are non-uniform, the P-V curve has multiple peaks. Each peak represents an MPP for a specific irradiation condition. The highest of all the peaks is called Global Maximum Power Point (GMPP). Under uniform irradiation conditions, there is zero or no partial shading. But the changing irradiance causes a shading effect which is called Partial Shading. Many conventional and soft computing techniques have been in use to harvest solar energy. These techniques perform well under uniform and weak shading conditions but fail when shading conditions are strong. In this paper, a new method is proposed which uses Machine Learning based algorithm called Opposition-Based-Learning (OBL) to deal with partial shading conditions. Simulation studies on different cases of partial shading have proven this technique effective in attainingMPP.