Global Maximum Power Point Tracking of Photovoltaic Module Arrays Based on Improved Cuckoo Search Algorithm

Abstract

In this paper, an improved cuckoo search-learning-based optimization algorithm (CSLBOA) for the maximum power point tracking (MPPT) of a photovoltaic module array is presented. For any shading discovered on a photovoltaic module array, there will be more than one maximum power point (MPP) observed in the power–voltage (P–V) characteristic curve of the photovoltaic module array. However, only the local maximum power point (LMPP) can be tracked by the traditional maximum power point tracker, but not the global maximum power point (GMPP). Therefore, in this paper, an intelligent maximum power point tracker based on an improved cuckoo search algorithm is presented to address the abovementioned issue. First, Matlab software is used to simulate the P–V characteristic curves of a photovoltaic module array with single-peak, double-peak, triple-peak, and quadruple-peak values while the photovoltaic module arrays are under different shading conditions. Second, the improved cuckoo search algorithm proposed is applied to track the global maximum power point precisely and efficiently. According to the simulation results, it shows that the improved cuckoo search algorithm has a better tracking speed response and steady-state performance than those of traditional ones.