Developing Intelligent MPPT for PV Systems Based on ANN and P&O Algorithms

The maximum power point tracking (MPPT) in photovoltaic (PV) systems varies depending on the fluctuation of the solar radiation and temperature; while the energy transfer from the PV to the load is controlled by specific algorithms. Conventional techniques for MPPT (Perturb and observe (P&O)) are easy to implement but they suffer from oscillations at MPP and speed is less due to fixed perturb step. To achieve better energy efficiency conversion in PV systems, it is required to develop maximum power point tracking (MPPT) control techniques. This paper presents an improved MPPT controller for PV systems using two techniques namely; Artificial Neural Network (ANN) and developed P&O techniques. The proposed ANN and the developed P&O algorithm are modeled using MATLAB/SIMULINK. The proposed ANN has two inputs which are solar radiation and ambient temperature. The optimum voltage of the PV system is the output of the proposed ANN. The proposed ANN was evaluated under different irradiation conditions and temperature. The response of the proposed ANN for MPPT controllers found to be lesser oscillation at MPP and faster tracking response compared with the developed P&O algorithm.