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.


    Access

    Download


    Export, share and cite



    Title :

    MPPT Operation for PV Grid-connected System using RBFNN and Fuzzy Classification


    Contributors:
    A. Chaouachi (author) / R. M. Kamel (author) / K. Nagasaka (author)

    Publication date :

    2010-05-25


    Remarks:

    oai:zenodo.org:1055046



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629 / 006




    Controlling Grid-Connected PV Array with P&O MPPT

    Kumari, Neha / Singh, Manjeet | IEEE | 2024


    Fuzzy-Wavelet RBFNN Model for Freeway Incident Detection

    Adeli, H. / Karim, A. | British Library Online Contents | 2000