@inproceedings{VeligorskyiChakirovVagapov2015, author = {Oleksandr Veligorskyi and Roustiam Chakirov and Yuriy Vagapov}, title = {Artificial Neural Network-based Maximum Power Point Tracker for the Photovoltaic Application}, series = {Proceedings of the 2015 1st International Conference on Industrial Netvvorks and Intelligent Systems (INISCOM 2015), Tokyo, Japan, March 2-4, 2015}, isbn = {978-1-63190-022-8}, doi = {10.4108/icst.iniscom.2015.258313}, year = {2015}, 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.}, language = {en} }