This research deals with improving the efficiency of solar photovoltaic (PV) power
systems using a Fuzzy Logic Controller (FLC) for Maximum Power Point Tracking
(MPPT), to control the duty cycle of DC-DC Voltage Converter, to achieve the
photovolt
aic system works at a Maximum Power Point under different atmospheric
changes of the solar insolation and ambient temperature. In this context, this research
presents a new model for FLC developed in Matlab/Simulink environment. The proposed
model for the controller is based on the conventional Perturb and Observe (P&O)
technique. Where, in similar to the conventional P&O technique, the changes in the Power
and tension of photovoltaic power system, are considered as the input variables of the
proposed controller, while the output variable is the change in the duty cycle. The main
advantage of the developed controller FLC, based on the considering the change in the
duty cycle has a Variable Step Size, and directly related to the changes in the power and
tension of the Photovoltaic system. Which make it possible to overcome the problem of
fixed Step Size in the change of the duty cycle in the conventional MPPT- P&O Controller
based on P&O technique. The MPPT- P&O Fuzzy, works by a variable step size achieve a
fast speed response and high efficiency for tracking the MPP point under sudden and
rapidly varying atmospheric conditions, compared with the conventional MPPT- P&O. The
simulation results completed in Matlab/Simulink environment, showed the best
performance of developed MPPT- P&O Fuzzy controller in tracking the MPP by achieving
a better dynamic performance and high accuracy, compared with the use of the
conventional MPPT- P&O under different atmospheric changes.
This research presents a new methodology for the development of a controller based
on Artificial Neural Networks and Direct control method in order to obtain the maximum
available energy from Solar Photovoltaic (PV) Energy systems under different a
tmospheric
changes of the solar insolation and ambient temperature. In this context, this research
presents a new model for MPPT-ANN in order to track the Maximum Power Point of PV
systems in Matlab/Simulink environment. The developed controller is based on Feed
Forward Neural Network FFNN trained by Back-propagation algorithm of error to
determine the optimal voltage operation of the system PV system at different atmospheric
changes. This research also suggests, control algorithm based on the direct control method
in order to determine the duty cycle, which used to control directly the operating of DCDC
Voltage Converter, depending on a comparison of the difference between the output
voltage of PV system and the optimal voltage output of the neural network. The developed
controller MPPT-ANN based on a network FFNN, Characterized by fast speed to track of
MPP point and achieve high efficiency for the PV system under the atmospheric changes.
The simulation results completed in Matlab/Simulink environment, showed the best
performance of developed controller MPPT-ANN by achieving a better dynamic
performance and high accuracy when tracking the MPP, compared with the use of the
another PI-ANN controller based on artificial neural network and the conventional
Proportional-Integral Controller, and compared with the use of the conventional MPPTP&
O based on Perturb and Observe (P&O) technique under different atmospheric changes.
