Predicting the Global Maximum Power Point Locus using Shading Information

The power-voltage characteristic curves of a series-connected photovoltaic (PV) system exhibit multiple peaks in partial shading scenario (PSS). However, there lacks a feasible model that obtains the capability of predicting the global maximum power point (GMPP) locus. This paper analyzes the GMPP characterization and demonstrates that the voltage at the GMPP is located over a large range in various PSS. With the aim of improving the prediction performance, multiple Gaussian process regression (MGPR) models are proposed to predict the GMPP locus by using shading information, such as the shading rate and shading strength. The performance of the method is evaluated using a dataset obtained in a variety of environmental conditions. The results show that it outperforms the existing prediction models in terms of accuracy. With its high prediction capability, the proposed method can be used to increase the maximum power point tracking performance in PV systems.

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