Fast Two-Stage Global Maximum Power Point Tracking for Grid-Tied String PV Inverter Using Characteristics Mapping Principle

In principle, the location of global maximum power point (GMPP) of partially shaded photovoltaic (PV) array highly correlates with the shape of
$P - V$
curve, but such correlation is complex and hard to be specified using the traditional mathematic expressions. This article, therefore, first explores the nonlinear characteristics of
$P - V$
curve under partial shading conditions (PSCs) using an artificial neural network (ANN), which could help locate the GMPP after scanning several selected characteristic points. Then, the perturb and observe (P&O) algorithm will be further integrated to achieve fast GMPP tracking based on the obtained location of GMPP. Being different from the existing ANN-based GMPPT methods, meteorological information, such as irradiance and temperature, is not mandatory. In implementation, both PV array model construction and ANN training process are performed offline, which will not complicate the online GMPP tracking algorithm. Compared with several GMPPT algorithms in recent references, the proposed method achieves higher tracking speed and meanwhile guarantees a high tracking efficiency. The performance of the proposed method was verified in simulation and experiment, where it achieves an average tracking efficiency of 99.32% and an average tracking time of 0.8 s.