Photovoltaic systems (PVs)offer an environmentally friendlysource of electricity;
however, up till now its price is still relatively high.Achieving the maximum power of
these systemsand maintaining it with lowest price in real applications is highl
y associated
with Maximum Power Point Tracking (MPPT) under different operation conditions.
This paper proposes the use of Genetic Algorithm (GA) for tracking maximum
power point depending on the solar cell model. GA gives, directly and precisely, the
optimal operating voltage (VOP) of the cell where the DC/DC converter will be adjusted
according to it based on the previous knowledge of the open circuit voltage (VOC) and
short circuit current (ISC) of the cell.
To validate the correctness and effectiveness of the proposed algorithm, MATLAB
R2010a programs for GA and PV system are written and incorporated together where the
series resistant of the cell is considered while the shunt resistant is neglected.
Simulation results of applying GA on different types of solar panels showedthe
possibilityof the accurateadjusting of the voltagetothe optimum valueand thusoperating the
systemat maximum power point.
The principal objective of this research is an adoption of the Genetic
Algorithm (GA) for studying it firstly, and to stop over the operations which
are introduced from the genetic algorithm.The candidate field for applying
the operations of the g
enetic algorithm is the sound data compression field.
This research uses the operations of the genetic algorithm for the
enhancement of the performance of one of the popular compression method.
Vector Quantization (VQ) method is selected in this work. After studying
this method, new proposed algorithm for mixing the (GA) with this method
was constructed and then the required programs for testing this algorithm
was written. A good enhancement was recorded for the performance of the
(VQ) method when mixed with the (GA). The proposed algorithm was
tested by applying it on some sound data files. Some fidelity measures are
calculated to evaluate the performance of the new proposed algorithm.
