PERFORMANCE IMPROVEMENT OF A PHOTOVOLTAIC GENERATOR POWERED DC MOTOR-PUMP SYSTEM BASED ON ARTIFICIAL NEURAL NETWORKS

Abstract

This paper presents the optimization of a photovoltaic (PV) waterpumping system using maximum power point tracking technique(MPPT). The optimization is suspended to reference optimal power. Thisoptimization technique is developed to assure the optimum choppingratio of buck-boost converter. The presented MPPT technique is used inphotovoltaic water pumping system in order to optimize its efficiency. Anadaptive controller with emphasis on Nonlinear Autoregressive MovingAverage (NARMA) based on artificial neural networks approach isapplied in order to optimize the duty ratio for PV maximum power at anyirradiation level. In this application, an indirect data-based technique istaken, where a model of the plant is identified on the basis of inputoutputdata and then used in the model-based design of a neural networkcontroller. The proposed controller has the advantages of robustness,fast response and good performance. The PV generator DC motor pump system with the proposed controller has been tested through a step change in irradiation level. Simulation results show that accurate MPPTtracking 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 simulationstudies. Obtained results demonstrate the effectiveness and superiority ofthe proposed approach.