Neural Networks Technique for Maximum Power Point Tracking of Photovoltaic Array

In this paper, we present our work on maximum power point tracking (MPPT) using neural networks. The MATLAB Simulink simulation platform is used to establish a model of photovoltaic array. The Simulink model is tested with different temperature and irradiation parameters, and the resultant I-V and P-V characteristics proved the validation of the Simulink model of PV array. In this paper, the backpropagation training algorithm is used to train a multilayer neural network using on data points collected from Simulink model of PV array simulated under a range of irradiations and temperatures. The collected data from the system is then tested by the neural network. We show that the neural network can accurately predict the maximum power point of a photovoltaic array. Comparisons between MPPT using the perturb and observe (P & O) algorithm and without MPPT are also shown in this paper. It is demonstrated that the neural network-based MPPT require less time and provide more accurate results than the P&O algorithm-based MPPT.