Abstract:
As part of the sustainable development plan, there is a trend of shifting to a new energy
paradigm, in which carbon-free technologies are being extensively used for renewable energy
generation, transmission, and consumption. The driving force behind this energy paradigm are
enabled by advancement in power switching devices and digital signal processing units that
constitute power converters. However, to ensure energy efficiency and reliability from these
power converters there is a need to maximum power point tracking (MPPT) in a PV system.
To this end, conventional control such as incremental conductance, perturb and observe,
constant voltage, load switching schemes find wide applications in distributed generation,
microgrids, and power quality compensation. While much has been done to improve their
performance, there remains a lot more to do to improve the performance of conventional
control techniques for real-time application. In particular, poor performance and/or
requirement for complete knowledge of model parameters as well as disturbances are the main
drawbacks of conventional control methods. In this work, an alternative Perturb and observe
(PO), Fuzzy Logic Control (FLC) scheme in power converters is designed and simulated in
SIMULINK/MATLAB. Furthermore, particle swarm optimization (PSO) algorithm is used
for fine tuning the FLC inputs and output scaling gains. The total PV panel capacity is 32 kW
to supply desired motor rating of 22 kW to supply water demand of 478.4 m3
/day at the head
of 98 m in the flow rate of 49.2 m3
/h. Based on the simulation result for a hypothetical
photovoltaic water pumping application demonstrate the efficiency has been 84.99%, 95.65%,
and 96.5% for perturb and observe, Fuzzy Logic, and PSO-based Fuzzy controller respectively.
So that the results confirm that the proposed PSO-based fuzzy controller methods have the
potential to significantly increase the total efficiency of the PV water pumping system. It is
recommended to apply hybrid PSO-based Genetic algorithm to improve the speed of
convergence and the ability to find the global optimum in the future research investigation in
this area
