@article{Vasarevicius_Martavicius_Pikutis_2012, title={Application of Artificial Neural Networks for Maximum Power Point Tracking of Photovoltaic Panels}, volume={18}, url={https://eejournal.ktu.lt/index.php/elt/article/view/3065}, DOI={10.5755/j01.eee.18.10.3065}, abstractNote={Maximum power point tracking technique for PV panels with support of online learning artificial neural network is offered. Mathematical model of the system is implemented in Matlab/Simulink environment. Maximum power point tracking is performed using <em>IncCond</em> algorithm and radial basis function artificial neural network. Several criteria for estimation of system performance were derived. It is shown that ANN can increase overall system efficiency by 10%.<p>DOI: <a href="http://dx.doi.org/10.5755/j01.eee.18.10.3065">http://dx.doi.org/10.5755/j01.eee.18.10.3065</a></p>}, number={10}, journal={Elektronika ir Elektrotechnika}, author={Vasarevicius, D. and Martavicius, R. and Pikutis, M.}, year={2012}, month={Dec.}, pages={65-68} }