TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Veligorskyi, Oleksandr
A1  - Chakirov, Roustiam
A1  - Vagapov, Yuriy
T1  - Artificial Neural Network-based Maximum Power Point Tracker for the Photovoltaic Application
T2  - Proceedings of the 2015 1st International Conference on Industrial Netvvorks and Intelligent Systems (INISCOM 2015), Tokyo, Japan, March 2-4, 2015
N2  - 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.
KW  - artificial neural network
KW  - efficiency
KW  - partial-shaded photovoltaic
KW  - maximum power point tracker
KW  - photovoltaic system
SN  - 978-1-63190-022-8
SB  - 978-1-63190-022-8
U6  - https://doi.org/10.4108/icst.iniscom.2015.258313
DO  - https://doi.org/10.4108/icst.iniscom.2015.258313
SP  - 6
S1  - 6
ER  -