This paper presents the optimization of a photovoltaic (PV) water
pumping system using maximum power point tracking technique
(MPPT). The optimization is suspended to reference optimal power. This
optimization technique is developed to assure the optimum chopping
ratio of buck-boost converter. The presented MPPT technique is used in
photovoltaic water pumping system in order to optimize its efficiency. An
adaptive controller with emphasis on Nonlinear Autoregressive Moving
Average (NARMA) based on artificial neural networks approach is
applied in order to optimize the duty ratio for PV maximum power at any
irradiation level. In this application, an indirect data-based technique is
taken, where a model of the plant is identified on the basis of inputoutput
data and then used in the model-based design of a neural network
controller. The proposed controller has the advantages of robustness,
fast response and good performance. The PV generator DC motor pump < br />system with the proposed controller has been tested through a step < br />change in irradiation level. Simulation results show that accurate MPPT
tracking performance of the proposed system has been achieved.
Further, the performance of the proposed artificial neural network
(ANN) controller is compared with a PID controller through simulation
studies. Obtained results demonstrate the effectiveness and superiority of
the proposed approach.