In the following study we make a simulation of an independent
photovoltaic system connected to an (ohm
- unit of electrical resistance) load which consists of the following
parts:
(Photovoltaic Module - Converter dc- dc - Control system to
track
ing the maximum power point via MATLAB & Simulink
program)
Taking advantage of equations of Photovoltaic Module we chart
the graph and simulate curves of the Module.
We also simulate the converter –type Cuk- which gives higher or
lower voltage than input voltage but with reversed polarity.
We also make a comparison between the two systems tracking:
the first tracker is a traditional one and the second one is a system
in which it uses a fuzzy logic tracker.
The results of the comparison shows different capacities taking into
consideration the varieties of weather conditions of regular solar
radiation as well as the partial shadow.
Such results showed that fuzzy logic has got more capability to
harmonize with all conditions especially in cases of low solar
radiation and partial shadow.
This research deals with the modeling of a Multi-Layers Feed Forward Artificial Neural
Networks (MLFFNN), trained using Gradient Descent algorithm with Momentum factor &
adaptive learning rate, to estimate the output of the neural network correspon
ding to the
optimal Duty Cycle of DC-DC Boost Converter to track the Maximum Power Point of
Photovoltaic Energy Systems. Thus, the DMPPT-ANN “Developed MPPT-ANN”
controller proposed in this research, independent in his work on the use of electrical
measurements output of PV system to determine the duty cycle, and without the need to
use a Proportional-Integrative Controller to control the cycle of the work of the of DC-DC
Boost Converter, and this improves the dynamic performance of the proposed controller to
determine the optimal Duty Cycle accurately and quickly. In this context, this research
discusses the optimal selection of the proposed MLFFNN structure in the research in terms
of determining the optimum number of hidden layers and the optimal number of neurons in
them, evaluating the values of the Mean square error and the resulting Correlation
Coefficient after each training of the neural network. The final network model with the
optimal structure is then adopted to form the DMPPT-ANN Controller to track the MPP
point of the PV system. The simulation results performed in the Matlab / Simulink
environment demonstrated the best performance of the proposed DMPPT-ANN controller
based on the MLFFNN neural network model, by accurately estimating the Duty Cycle and
improving the response speed of the PV system output to MPP access, , as well as finally
eliminating the resulting oscillations in the steady state of the Power response curve of PV
system compared with the use of a number of reference controls: an advanced tracking
controller MPPT-ANN-PI based on ANN network to estimate MPP point voltage with
conventional PI controller, a MPPT-FLC and a conventional MPPT-INC uses the
Incremental Conductance technique INC
