Microgrid efficiency enhancement based on neuro-fuzzy MPPT control for Photovoltaic generator

In terms of optimal Microgrid (MG) control, the output power of a non-dispatchable Distributed Generation (DG) as a Photovoltaic (PV) system need to be controlled based on the optimal operating condition of its primary energy source by the mean of a Maximum Power Point Tracking (MPPT) to extract the potential maximum power which is nonlinearly depending on the weather conditions. In this work we presented a new methodology for this purpose using an approach based on a neuro-fuzzy generalized method to estimate the reference voltage (V*_pv) that guaranties an optimal power transfer between the DG and the microgrid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three Radial Basis Functions Neural Networks (RBFNN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated network for either training or estimation process. Simulation results under several rapid irradiance variations proved that the proposed MPPT control for the PV generator achieved high energy conversion efficiency comparing to a Normal Operating Power (NOP) (when the PV generator is directly coupled to the inverter, without MPPT control).