Abstract
The expeditious broadening of Photovoltaic (PV) energy has attracted the private and government precinct world-wide due to the reduction of costs and being cleaner source of energy. However, most of the maximum power point tracking (MPPT) controller are inefficient under rapid change of environmental conditions. Under partial shading conditions (PSC) MPPT controllers fail to track global maximum power point (GMPP). Therefore, it is essential to propose MPPT controller that will be able to locate GMPP. In this study, the two powerful machine learning and deep learning MPPT algorithms are used to force the PV system to operate at higher efficiency under sudden change in solar irradiance and temperature. Support Vector Machine (SVM) and Recurrent Neural Network (RNN) performances are validated and proved using MATLAB SIMULINK simulation software.
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