TITLE:
Maximum Power Point Tracking Control Using Neural Networks for Stand-Alone Photovoltaic Systems
AUTHORS:
Rihab Mahjoub Essefi, Mansour Souissi, Hsan Hadj Abdallah
KEYWORDS:
Maximum Power Point Tracking (MPPT), Photovoltaic (PV) System, Neural Network, Buck Converter
JOURNAL NAME:
International Journal of Modern Nonlinear Theory and Application,
Vol.3 No.3,
July
14,
2014
ABSTRACT:
The employment of maximum power point tracking techniques in the
photovoltaic power systems is well known and even of immense importance. There
are various techniques to track the maximum power point reported in several
literatures. In such context, there is an increasing interest in developing a
more appropriate and effective maximum power point tracking control methodology
to ensure that the photovoltaic arrays guarantee as much of their available
output power as possible to the load for any temperature and solar radiation
levels. In this paper, theoretical details of the work, carried out to develop
and implement a maximum power point tracking controller using neural networks
for a stand-alone photovoltaic system, are presented. Attention has been also
paid to the command of the power converter to achieve maximum power point
tracking. Simulations results, using Matlab/Simulink software, presented for
this approach under rapid variation of insolation and temperature conditions,
confirm the effectiveness of the proposed method both in terms of efficiency
and fast response time. Negligible oscillations around the maximum power point
and easy implementation are the main advantages of the proposed maximum power
point tracking (MPPT) control method.