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Performance improvement in MPPT of SPV system using NN controller under fast changing environmental condition

Solar photovoltaic (SPV) energy have become an attractive renewable energy source because it is freely available, need less maintenance and pollution free. Since characteristics of SPV module is nonlinear in nature and varies with environmental condition, to extract maximum power from the PV module appropriate maximum power point tracking (MPPT) algorithm must be incorporated in the PV system to track the maximum power point (MPP). Although tracking performance of conventional MPPT method is high it suffers a lot in case of rapidly changing environmental condition. This paper proposes the improvement in tracking performance of a SPV module using neural network (NN) controller under fast changing environmental condition. The tracking performance of NN controller is compared with conventional perturb and observe (P&O) MPPT controller. The slow tracking of P&O and wrong tracking during changing weather condition is eliminated using NN controller. The large drooping characteristic of P&O under fall of irradiation is also eliminated. The result is verified using MATLAB-Simulink software package under fast changing irradiation and temperature.