An Intelligent Automatic Adaptive Maximum Power Point Tracker for Photovoltaic Module Arrays

Abstract

In this study, a maximum power point tracker was developed for photovoltaic module arrays by using a teacher-learning-based optimization (TLBO) algorithm to control the photovoltaic system. When a photovoltaic module array is shaded, a conventional maximum power point tracker may obtain the local maximum power point rather than the global maximum power point. The tracker developed in this study was aimed at solving this problem. To prove the viability of the proposed method, a SANYO HIP 2717 photovoltaic module with diverse connection patterns and shading ratios was used. Thus, single-peak, double-peak, triple-peak, and multi-peak power–voltage characteristic curves of the photovoltaic module array were obtained. A simulation of maximum power point tracking (MPPT) was then performed with MATLAB software. With regard to practical testing, a boost converter was used as the hardware structure of the maximum power point tracker and a TMS320F2808 digital signal processor was selected to execute the rules for MPPT. The results of the practical tests verified that the proposed improved TLBO algorithm had a superior accuracy to existing TLBO algorithms. In addition, the proposed improved TLBO algorithm can shorten the tracking time to 1/2 or 1/4, so it can improve the efficiency of power generation by two to three percentage.