Abstract :
This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW photovoltaic (PV)
system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between
the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three multi-layered
feed forwarded Artificial Neural Networks (ANN). Inputs of the network (irradiance and temperature) are classified before they are fed
into the appropriated ANN for either training or estimation process while the output is the reference voltage. The main advantage of the
proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding
to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model
machine learning that defines a set of local models emulating the complex and nonlinear behavior of a PV generator under a wide range
of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the
highest efficiency comparing to a conventional single neural network and the Perturb and Observe (P&O) algorithm dispositive.
2010 Elsevier Ltd. All rights reserved.
