Abstract
Partial shading is an unavoidable complication in the field of photovoltaic (PV) generation. Bypass diodes have become a standard feature of solar cell arrays to improve array performance under partial shading scenarios (PSS). However, the current–voltage and power–voltage characteristic data vary with different shading patterns. In this paper, a shading pattern detection algorithm is first proposed to estimate the number of shaded modules in a PV string. The result is then fed to a field-support vector regression (F-SVR) model, in which the measured features are transferred into a style-free high-dimension space prior to data training. The process of style-normalized transformation enables the features to be independent and identically distributed. Both simulations and experiments are conducted to evaluate the F-SVR model's ability to estimate the voltage at maximum power points. The results show that the proposed maximum power point estimation method can evidently reduce prediction errors.
Published Version
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