Photovoltaic Global Maximum Power Tracking Based on Improved Dragonfly Algorithm - Fei XUE; Xin MA; Bei TIAN; Hui WU (Zhongguo dianli)
A multi-peak phenomenon can be observed on the power-voltage (P-U) characteristic curve of a photovoltaic (PV) array under partially shaded conditions (PSCs). In this case, conventional maximum power point tracking (MPPT) algorithms tend to fall into local extremums, and swarm intelligence algorithms would spend much time in tracking. Thus, this paper proposes an improved MPPT algorithm based on the dragonfly algorithm (DA) and the perturbation and observation (P & O) algorithm. The convergence rate and global search ability of the algorithm are improved by optimizing particle roles and introducing the Lévy flight model. With the P & O algorithm, the concept of population density is put forward and an optimal local search strategy is formulated to modify the population search efficiency and precision. Finally, comparisons with the P & O algorithm, particle swarm optimization (PSO) algorithm, and the original DA through simulation verify the validity of the proposed algorithm.