Modeling, analysis and neural MPPT control design of a PV-generator powered dc motor-pump system

Abstract

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 input-output 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 system with the proposed controller has been tested through a step 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.