An Adaptive Control Algorithm for Maximum Power Point Tracking for Photovoltaic Energy Conversion Systems - A Comparative Study

Abstract

This paper presents intelligent methods for the purpose of Maximum Power Point Tracking (MPPT). One is based on a Fuzzy Logic Controller (FLC) and the second is based on an Artificial Neural Network (ANN); they are applied to a converter circuit. The fuzziness determines the size of the perturbed voltage when there are rapid changes of the solar irradiation. A control scheme is presented which allows better control of the converter current reference using voltage and current from the PV system as inputs to the MPPT perturb and observes method. A new approach is also proposed to carry out the model of the photovoltaic array and to predict the maximum power point with an artificial neural network. This approach does not require the detailed knowledge of the physical parameters of the solar cell material. The neural model is trained by using a random set of data collected from the real photovoltaic array. In this paper, the fuzzy controller and the neural controller are used to enhance the classical perturb and observe method. Experimental tests have been carried out to demonstrate that the intelligent techniques are fast, stable and more productive. They are efficient in converting and transferring the power from the PV to the load.