Control of PV Based Water Pumping System Optimized Using Neural Network

The objective of this study is to enhance the efficiency of a solar-powered water pumping system through the integration of advanced control methodologies. The study focuses on harnessing the potential of photovoltaic (PV) panels by employing Maximum Power Point Tracking (MPPT) with incremental conductance method to maximize power extraction under varying irradiance conditions. The irradiance values obtained from the solar panels are utilized as inputs to a neural network (NN) predictive model, which accurately forecasts power output. The predicted power values are then transformed into reference speeds and employed within a field-oriented control (FOC) framework to regulate the operation of the induction motor (IM). This integrated approach aims to enhance the efficiency and reliability of the system by dynamically adjusting motor speed based on real-time irradiance fluctuations and mitigating the impact of changing environmental conditions. The findings contribute to the advancement of control strategies for renewable energy systems, particularly in remote agricultural applications where reliable water supply is crucial for sustainable development.