Improving photovoltaic water pumping system performance with PSO-based MPPT and PSO-based direct torque control using real-time simulation.

This work aims to enhance the performance of Photovoltaic Water Pumping Systems (PVWPS) by optimizing its two primary controllers. The first controller utilizes a Particle Swarm Optimization (PSO)-based Maximum Power Point Tracking (MPPT) technique to maximize the photovoltaic array's output under varying irradiance conditions. The second controller incorporates a PSO-optimized Proportional-Integral (PI) controller within a Direct Torque Control (DTC) method to improve the dynamic behavior of the induction motor (IM) and ensure the efficient functioning of the centrifugal pump. The performance of the PVWPS employing PSO for MPPT and DTC was evaluated in MATLAB Simulink and compared with a system using Artificial Neural Networks (ANN) for MPPT and DTC. The PSO-based approach demonstrated significant advantages, including an 83.33% reduction in power oscillations, a 66.67% and 60% reduction in flux and torque ripples, a 50% improvement in response time, and a rise in water flow. Real-time simulations of both the ANN-DTC and PSO-DTC configurations were carried out on the dSPACE DS1104 platform to validate the performance of each configuration. The outcomes of these simulations closely matched those from MATLAB/Simulink, further confirming the proposed PSO-based control strategy's effectiveness, robustness, and reliability.