Modeling and Control Maximum Power Point Tracking of an Autonomous Photovoltaic System Using Artificial Intelligence ()
ABSTRACT
Despite investigative efforts seen in the literature, the maximum power
point tracking remains again a crucial
problem in photovoltaic system (PV) connected to the power grid. In this
paper, a new maximum power point tracking technique which is our contribution to
the resolution of this problem is treated. We proposed a hybrid controller of
maximum power point tracking based on artificial neural networks. This hybrid
controller is composed of two neural networks. The first network has two inputs
and two outputs: the inputs are solar irradiation and ambient temperature and
the outputs are the reference output voltage and current corresponding at the maximum
power point. The second network has two inputs and one output: the inputs use
the outputs of the first network and the output will be the periodic cycle
which controls the DC/DC converter. The training step of neural networks
requires two modes: the offline mode and the online mode. The data necessary
for the training are collected from a very large number of real-time
measurements of the PV module. The performance of the proposed method is
analyzed under different operating conditions using the Matlab/Simulink
simulation tool. A comparative study between the proposed method and the
perturbation and observation approach was presented.
Share and Cite:
Fousseyni Toure, A. , Tchoffa, D. , El Mhamedi, A. , Diourte, B. and Lamolle, M. (2021) Modeling and Control Maximum Power Point Tracking of an Autonomous Photovoltaic System Using Artificial Intelligence.
Energy and Power Engineering,
13, 428-447. doi:
10.4236/epe.2021.1312030.