一种新颖高效的全局最大功率跟踪方法用于在重复性光照条件下的复杂部分遮阴光伏系统

Abstract Partial shading conditions (PSC) significantly hinder the conversion efficiency of photovoltaic (PV) generation systems (PVGS), posing challenges for global maximum power point (GMPP) tracking (GMPPT). This paper proposes a novel and efficient two-stage GMPPT method to address the challenges, introducing multiple techniques to improve tracking performance under complicated and repeatable irradiance environments. In the first stage, a Lambert W-function (LWF)-based modeling and estimation mechanism are developed to identify the candidate shaded region (SR) containing the GMPP and its corresponding voltage operating point (VOP) using fewer samplings. In the second phase, the variable step size incremental conductance (VSSINC) method, starting from the VOP found in the first stage, takes over subsequent tracking to refine convergence on the GMPP. A PV system formed by a 5-series 1-parallel (5S1P) module string is utilized as a study case. The devised method demonstrates significant advancements compared to the four presented benchmark methods. Simulations across 2002 shading patterns (SP) achieve maximum improvement rates of 98.4 % in average tracking power error (ATPE), 8.1 % in total tracking success rate (TTSR), and 66.9 % in average tracking time (ATT). Experimental results under three random SPs show improvements of 93.1 % in average tracking power loss (ATPL), 5.31 % in average tracking accuracy (ATA), and 86.6 % in ATT, all of which outperform the counterparts. These results highlight the significant improvement in TTSR, robustness to changes in SP, and efficiency derived from the proposed accurate LWF-based system modeling and estimation for the critical VOPs. This study also provides a new solution for maximizing the power extraction from PVGS and paving the way for the broader application and advancement in solar technology.