Maximum Power Point Tracking (MPPT) for a PV System Based on Artificial Neural Network ANN and Comparison with P&O Algorithm

Abstract

For the optimal operation of photovoltaic system, The MPPT (Maximum Power Point Tracking) control unit is an essential part for the photovoltaic system. In addition to the protection function, this command ensures the continuation of the maximum power point (MPPT) and allows the PV generator to deliver its maximum power regardless of the variation in climatic conditions (sunshine and) temperature). This work intends to provide an artificial neural network (ANN) maximum power point tracking (MPPT) method which is fast and precise in finding and tracking the maximum power point (MPP) in photovoltaic (PV) applications, under rapidly changing of solar irradiation, and the P&O algorithm. ANN and P&O MPPT algorithms, and other components of the MPPT control system which are PV module and DC-DC boost converter, are simulated in MATLAB/ Simulink, we used in The proposed ANN two inputs which are irradiation and ambient temperature, and one output is the optimum voltage of the PV system. The proposed ANN was analyzed under different irradiation conditions. The response of the proposed ANN for MPPT controllers found to be lesser oscillation at MPP and faster tracking response compared with the P&O algorithm. Comparisons of MPPT with P&O algorithm and without MPPT tracker are also shown in this paper. It is demonstrated that the neural network based MPPT tracking require less time and provide more accurate results than the P&O algorithm based MPPT.