Soft Computing Modelling of a Directly Coupled PV Water Pumping System

Abstract

Photo-Voltaic Water Pumping System (PVWPS) is the most suitable system for irrigation in the remote areas. PVWPS may be characterized by its multi- variable-non- linear equations, However, efficient water demand management necessitates fast and accurate water flow rate estimation at actual operating states. This can hardly be achieved with conventional mathematical – based methodologies. In this paper two of the Soft Computing (SC) techniques are due selected; Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for PVWPS novel modeling and performance pre-evaluation .The (ANN) model and (ANFIS) models are trained off-line to identify the water flow rate based on air temperature, solar irradiation, and static head as input parameters. An iteration technique which is mathematical–based is also presented for comparison and evaluation purposes. The paper indicates accuracy, robustness and effectiveness of the proposed models in water flow rate control ,economic feasibility and fault detection.