Real-Time Maximum Power Point Estimation of Photovoltaic Systems Using Shading Information

Under partial shading conditions, a series-connected photovoltaic (PV) string often shows multipeak power–voltage (
$P$
–
$V$
) characteristics, which indicate a multimodal distribution of maximum power points (MPPs). The phenomenon brings challenges to the MPP estimation since a single model usually cannot perform well in different modes. To address this problem, a real-time shading identification process is proposed to identify the shading information of the applied PV string. The measured data are divided into several fields according to the obtained number of peaks in the
$P$
–
$V$
curve. Multiple Gaussian process regression (M-GPR) models are then proposed to predict the MPP locus in a specific data field. Experiments are conducted to evaluate the accuracy of the proposed method. Simulation and experimental results show that the shading information enables the proposed M-GPR to obtain more accurate estimation performance compared to its single-model counterparts.