Optimization of Energy Efficiency in Photovoltaic Water Pumping Systems Using Neural Networks

Abstract

ABSTRACT In light of current global issues, including rising energy prices, environmental concerns, and the requirement for resilient water systems, photovoltaic (PV) water pumping systems have emerged as a promising solution, particularly for off‐grid communities. The goal of this work is to use an optimal control strategy to achieve independence and efficiency. The chosen PV water pumping system is based on an induction motor equipped with direct torque command. To extract the maximum energy from the PV panel, a maximum power point tracking (MPPT) based on a neural network algorithm is proposed. The results obtained will be compared with those from the same system using conventional Incremental Conductance (IC) and Perturbation and Observation (P&O) methods. On the basis of the results obtained, we can state that the neural MPPT technique is the most effective, achieving an efficiency of 98% and providing the fastest tracking, superior power stability, and minimal harmonics. With a total harmonic distortion of 3.41% and a larger water volume, it is the most suitable option for a PV water pumping system. The MPPT–IC technique is a promising option, offering an efficiency of 93% with fast responsiveness, albeit resulting in larger power fluctuations. However, the MPPT–P&O strategy is the least effective due to its slow response and high harmonic distortion rate of 10.96%, with an efficiency of 87%. These factors make it the least suitable for optimal performance.