Improved Maximum Power Point Tracking for Solar PV Module using ANFIS

The output power delivered by solar photovoltaic (PV) module depends on weather conditions and to obtain maximum they are required to operate at maximum available power point for different weather conditions. Maximum power point tracking (MPPT) controllers are usually employed in PV power systems to extract maximum power. Since the solar PV module characteristics are highly nonlinear, conventional control techniques could be inefficient for an optimal use of these systems. This paper presents an improved methodology for maximum power point tracking of a solar PV module using adaptive neuro fuzzy inference system (ANFIS). Mathematical modeling of a solar PV module has been done in sequential steps using Matlab/Simulink software package and ANFIS based maximum power point tracking scheme is developed to control the extraction of maximum power from this solar PV module. The proposed ANFIS based reference model is trained to generate maximum power corresponding to varying solar irradiance level and operating temperature. Simulation results reveals that the response of proposed ANFIS based MPPT method is more accurate and fast as compared to the conventional techniques like perturb & observation (P&O). The main advantage of proposed MPPT scheme is fast response and high gain even at lower value of solar irradiance level without oscillations near the point of maximum power. The analytical and simulation results of this research are presented to validate the concept.