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

Area of Science:

• Renewable Energy Systems
• Control Engineering
• Power Electronics

Background:

• Photovoltaic Water Pumping Systems (PVWPS) are crucial for irrigation and water supply.
• Optimizing controllers for Maximum Power Point Tracking (MPPT) and Direct Torque Control (DTC) is key to PVWPS efficiency.
• Existing methods may have limitations in dynamic response and power quality.

Purpose of the Study:

• To enhance PVWPS performance by optimizing MPPT and DTC controllers using Particle Swarm Optimization (PSO).
• To compare the effectiveness of PSO-based control with Artificial Neural Network (ANN) based control.
• To validate the proposed control strategy through simulation and real-time testing.

Main Methods:

• Implementing a PSO-based MPPT controller to maximize photovoltaic array output.
• Utilizing a PSO-optimized Proportional-Integral (PI) controller within a Direct Torque Control (DTC) framework for induction motor efficiency.
• Evaluating system performance in MATLAB/Simulink and validating with real-time simulations on a dSPACE DS1104 platform.

Main Results:

• PSO-based MPPT and DTC demonstrated an 83.33% reduction in power oscillations.
• Significant reductions in flux (66.67%) and torque ripples (60%) were achieved.
• A 50% improvement in response time and increased water flow were observed compared to ANN-based methods.

Conclusions:

• The proposed PSO-based control strategy offers superior performance, robustness, and reliability for PVWPS.
• PSO optimization provides substantial improvements in power quality and dynamic response.
• Real-time validation confirms the effectiveness of the PSO approach for PVWPS applications.