Experimental Evaluation of Neural Network Based Real Time Maximum Power Tracking Controller for PV System

Abstract

This paper presents a neural network based real time maximum power tracking controller for interconnected photovoltaic(PV) systems to commercial power sources through inverters. The neural network is utilized to identify the optimal operating voltage of the PV array to achieve maximum output efficiency. The proposed controller generates the control signal in real time, and the control signal is fed back to the voltage control loop of the inverter to shift the terminal voltage of the PV array to the identified optimal one, which yields the maximum power generation. The controller is simply configured by using proportional and mtegral(PI) control loops. The proportional and the integral gains are tuned to their optimal values to achieve the quick response and also to prevent the overshoot and the undershoot in the transient state. The continuous measurement is required for the open circuit voltage on the monitoring cell, and also the terminal voltage of the PV array. The measurements are quite easy and straightforward. Because of the accurate identification of the optimal operating voltage through the neural network, more than 99% power is drawn from the actual maximum power.