Soft computing approaches for photovoltaic water pumping systems: A review

Abstract

Water pumping systems are crucial for extracting water from deep wells. However, electricity shortages and high fuel prices significantly impact the efficiency and reliability of these systems. Therefore, renewable energy sources have gained more attention as alternatives to fossil fuels. Photovoltaic (PV) energy-based pumping systems, in particular, are becoming popular, especially in rural areas where grid connections are often unavailable. Several factors influence the performance of photovoltaic water pumping systems (PVWPS), including solar irradiance, temperature, system design, maintenance, and pumping load. To ensure optimal performance under these varying conditions, two controllers are crucial. The first controller is the Maximum Power Point Tracking (MPPT) controller, designed to maximize power extraction from the PV panels under varying environmental conditions (in particular, solar radiation and temperature). The second controller regulates the speed and torque of the induction motor (IM) which drives the pump responsible for water extraction. Therefore, to improve the performance of these controllers under different conditions. This review paper first examines widely used soft computing methods, providing a detailed description of each. These methods are then applied to both the MPPT and the IM controllers, offering valuable insights for researchers looking to develop advanced PVWPS control configurations for future applications.

Keywords

• Artificial intelligence
• Direct torque control
• Induction motor
• Maximum power point tracking
• Photovoltaic water pumping system
• PV system