1st International Conference on Industrial Networks and Intelligent Systems

Research Article

Artificial Neural Network-based Maximum Power Point Tracker for the Photovoltaic Application

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  • @INPROCEEDINGS{10.4108/icst.iniscom.2015.258313,
        author={Oleksandr Veligorskyi and Roustiam Chakirov and Yuriy Vagapov},
        title={Artificial Neural Network-based Maximum Power Point Tracker for the Photovoltaic Application},
        proceedings={1st International Conference on Industrial Networks and Intelligent Systems},
        publisher={ICST},
        proceedings_a={INISCOM},
        year={2015},
        month={4},
        keywords={photovoltaic system artificial neural network maximum power point tracker efficiency partial-shaded photovoltaic},
        doi={10.4108/icst.iniscom.2015.258313}
    }
    
  • Oleksandr Veligorskyi
    Roustiam Chakirov
    Yuriy Vagapov
    Year: 2015
    Artificial Neural Network-based Maximum Power Point Tracker for the Photovoltaic Application
    INISCOM
    ICST
    DOI: 10.4108/icst.iniscom.2015.258313
Oleksandr Veligorskyi1, Roustiam Chakirov2, Yuriy Vagapov3,*
  • 1: Chernihiv National University of Technology
  • 2: Bonn-Rhein-Sieg University of Applied Science
  • 3: Glyndwr University
*Contact email: y.vagapov@glyndwr.ac.uk

Abstract

This paper proposes a new artificial neural network-based maximum power point tracker for photovoltaic application. This tracker significantly improves efficiency of the photovoltaic system with series-connection of photovoltaic modules in non-uniform irradiance on photovoltaic array surfaces. The artificial neural network uses irradiance and temperature sensors to generate the maximum power point reference voltage and employ a classical perturb and observe searching algorithm. The structure of the artificial neural network was obtained by numerical modelling using Matlab/Simulink. The artificial neural network was trained using Bayesian regularisation back-propagation algorithms and demonstrated a good prediction of the maximum power point. Relative number of Vmpp prediction errors in range of ±0.2V is 0.05% based on validation data.