A PSOI based MPPT technique for PV system under dynamically changing PSC

Abstract

As conventional techniques fail to track global MPP under partial shaded condition; maximum power point tracking algorithms based on optimization algorithms are an attractive alternative to track the global maximum power point under partial shaded condition. Due to several advantages particle swarm optimization algorithm is most implemented and most suitable for MPP tracking under PSC. Though in most of the cases PSO guarantees global MPP under PSC, it suffers from certain disadvantages like local maxima trapping due to random initialization of population, increased tracking time, larger exploration of search space, output power oscillations and larger settling time. To overcome the limitations of PSO, a novel improved PSO algorithm is proposed, which includes opposition based learning and worst population elimination methods. The performance of the proposed algorithm is examined on 8S and 4S2P PV configurations subjected to dynamically changing partial shaded condition irradiation patterns and results are presented. The results are compared with the conventional PSO algorithm under similar conditions. From results it is noticed that proposed algorithm has very less tracking time, less exploration of search space, do not suffer from local maxima trapping and reduces the output power oscillations. The proposed algorithm shows superior performance compared to conventional PSO algorithm.