A. Chaouachi and R. M. Kamel and K. Nagasaka
MPPT Operation for PV Gridconnected System using RBFNN and Fuzzy Classification
772 - 780
2010
4
5
International Journal of Electrical and Computer Engineering
https://publications.waset.org/pdf/1357
https://publications.waset.org/vol/41
World Academy of Science, Engineering and Technology
This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a gridconnected 20 kW Photovoltaic (PV) system using neurofuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neurofuzzy network is composed of a fuzzy rulebased 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 networkbased approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neurofuzzy network is a neural network based multimodel 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.
Open Science Index 41, 2010