Research Article
Artificial Neural Network-based Maximum Power Point Tracker for the Photovoltaic Application
@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
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.