An Enhanced 0.8$\text{V}_\text{OC}$-Model-Based Global Maximum Power Point Tracking Method for Photovoltaic Systems

Abstract

Under partial shading conditions (PSC), the power-voltage ( P–V ) characteristic curve of a photovoltaic string exhibits multiple peaks, posing a big challenge to the problem of global maximum power point tracking (GMPPT). The traditional 0.8 $\text{V}_\text{OC}$ -model-based GMPPT method locates the global maximum power point (GMPP) locus by comparing the power at each local power peak. However, a considerable amount of time is required for iteratively scanning each 0.8 $\text{V}_\text{OC}$ vicinity. To address this problem, an improved 0.8 $\text{V}_\text{OC}$ -model-based GMPPT method is proposed in this article. A shading vector is introduced to characterize the PSC. The proposed GMPPT method estimates the 0.8 $\text{V}_\text{OC}$ region with the GMPP directly from the measured shading vector by the k -nearest neighbors approach and saves the time consumed in the comparison process used in the conventional method. Simulation and experimental results demonstrate that the proposed method is capable of tracking the GMPP efficiently and accurately under various shading patterns.