TY - JFULL AU - A. Chaouachi and R. M. Kamel and K. Nagasaka PY - 2010/6/ TI - MPPT Operation for PV Grid-connected System using RBFNN and Fuzzy Classification T2 - International Journal of Electrical and Computer Engineering SP - 771 EP - 780 VL - 4 SN - 1307-6892 UR - https://publications.waset.org/pdf/1357 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 41, 2010 N2 - 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 Radial Basis Function Neural Networks (RBFNN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated RBFNN 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 non-linear 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. ER -