An Artificial Neural Network based Maximum Power Point Tracking method for photovoltaic system

Solar Energy is one of the very promising renewable energy resources which have grown rapidly in the past few years and presently it is gaining popularity and momentum at an ever increasing rate. But there are also some limitations for trapping Solar Energy. A major disadvantage of trapping Solar Energy in photovoltaic cells is that it's operating point changes with changing insolation and temperature which in turn reduces its overall efficiency. Therefore, it becomes imperative that maximum power should be derived from solar PV panels via Maximum Power Point Tracking (MPPT) which in turn increases its efficiency. In this paper, a PV model has been used to simulate actual PV arrays behavior, and then a Maximum Power Point tracking method using Artificial Neural Network (ANN) is proposed in order to control the on goings of the DC-DC Buck Boost Converter. Simulation results show that Artificial Neural Network is near accurate in predicting the Maximum Power Point.