Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications

This article contains a review of essential control techniques for maximum power point tracking (MPPT) to be applied in photovoltaic (PV) panel systems. These devices are distinguished by their capability to transform solar energy into electricity without emissions. Nevertheless, the efficiency can be enhanced provided that a suitable MPPT algorithm is well designed to obtain the maximum performance. From the analyzed MPPT algorithms, four different types were chosen for an experimental evaluation over a commercial PV system linked to a boost converter. As the reference that corresponds to the maximum power is depended on the irradiation and temperature, an artificial neural network (ANN) was used as a reference generator where a high accuracy was achieved based on real data. This was used as a tool for the implementation of sliding mode controller (SMC), fuzzy logic controller (FLC) and model predictive control (MPC). The outcomes allowed different conclusions where each controller has different advantages and disadvantages depending on the various factors related to hardware and software.

Automated summary

This article reviews essential control techniques for maximum power point tracking (MPPT) in photovoltaic panel systems and experimentally evaluates different types of MPPT algorithms using an artificial neural network as a reference generator. The implementation outcomes of sliding mode controller, fuzzy logic controller, and model predictive control are compared based on various factors related to hardware and software.