Enhanced model based algorithm to reinforce PV system with dynamic MPPT capability

Abstract

Photovoltaic (PV) power has emerged as the most attractive resource in form of a clean and green energy. However, one major challenge associated with PV interfacing is its intermittent output characteristic which varies dramatically with the operating conditions. Thus designing an effective maximum power point tracking (MPPT) algorithm is a key aspect for optimizing PV system performance. Numerous MPPT algorithms have been proposed earlier having their own specific advantages. However, these are found to have two major limitations which have to be essentially addressed. Firstly, they become ineffective in the dynamic conditions where there is rapid change in environmental parameters like insolation and temperature. Secondly, they fail to discriminate between global and local peaks under partial shading conditions. Therefore, to achieve a reliable and efficient system operation, this paper presents an enhanced model-based (MB) algorithm that overcomes both these deficiencies. Based on new governing equations and precised estimation technique, it predetermines the MPP analytically. First simulated results are obtained where it is tested for dynamic variations of all the three parameters. Then the experimental validation is carried out on a 2 KW installed panel where real time data is recorded through CR1000 data logger and environmental parameters are sensed with elements like pyranometer and humidity sensor. A large number of experimental results are obtained for tracked MPP in the dynamic conditions, which are then summarized in tabular forms. These are finally plotted and compared with simulated results to illustrate the effectiveness of proposed MB algorithm.