Abstract
Global maximum power point tracking (GMPPT) in case of rapidly variable irregular shading conditions is a complex task, which is exacerbated in case of inexpensive solutions where sensors for acquiring environmental conditions are excluded, that is there is not any information about irradiance and temperature distribution along the photovoltaic system. In such a context, the paper deals with the feasibility study and implementation of a novel easy and cost-effective hybrid two-stages GMPPT algorithm. The first stage synergically combines two different methods to predict the optimal operating condition: an artificial neural network based algorithm and a segmentation-based approach. A traditional hill-climbing method is used in the second stage to finely track the maximum power point. The theoretical aspects of the proposed solution are firstly described, then a wide simulative and experimental tests campaign have been executed to validate the effectiveness of the GMPPT method, confirming that a very simple artificial neural network structure combined with a standard segmentation based method leads to excellent performance for applications where the environmental conditions vary very frequently.
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