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

Ikram Saady; Btissam Majout; Badre Bossoufi; Mohammed Karim; Ismail Elkafazi; Safae Merzouk; Mishari Metab Almalki; Thamer A H Alghamdi; Paweł Skruch; Anton Zhilenkov; Saleh Mobayen

doi:10.1038/s41598-025-00297-8

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

Sci Rep. 2025 May 8;15(1):16127. doi: 10.1038/s41598-025-00297-8.

Authors

Ikram Saady^{1

2}, Btissam Majout¹, Badre Bossoufi³, Mohammed Karim¹, Ismail Elkafazi⁴, Safae Merzouk², Mishari Metab Almalki⁵, Thamer A H Alghamdi^{6

7}, Paweł Skruch⁸, Anton Zhilenkov⁹, Saleh Mobayen^{10

11}

Affiliations

¹ Laboratory of Engineering Modelling and Systems Analysis, Sidi Mohamed Ben Abdellah University Fez, Fes, Morocco.
² Moroccan School of Engineering Sciences (EMSI), SMARTiLab, Rabat, Morocco.
³ Laboratory of Engineering Modelling and Systems Analysis, Sidi Mohamed Ben Abdellah University Fez, Fes, Morocco. badre.bossoufi@usmba.ac.ma.
⁴ Superior School of Technology in Khenifra, Sultan Moulay Slimane University, Beni Mellal, Morocco.
⁵ Department of Electrical Engineering, Faculty of Engineering, Al-Baha University, Alaqiq, 65779-7738, Saudi Arabia.
⁶ School of Engineering, Wolfson Centre for Magnetics, Cardiff University, Cardiff, CF24 3AA, UK.
⁷ Electrical Engineering Department, Faculty of Engineering, Al-Baha University, Al-Baha, 65779, Saudi Arabia.
⁸ Department of Automatic Control and Robotics, AGH University of Science and Technology, Kraków, 30-059, Poland.
⁹ Department of Cyber-Physical Systems, St. Petersburg State Marine Technical University, Saint-Petersburg, Russia, 190121.
¹⁰ Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 640301, Taiwan. mobayens@yuntech.edu.tw.
¹¹ Energy Systems Research Center, Khazar university, Mahasti str. 41, Baku AZ1096, Azerbaijan. mobayens@yuntech.edu.tw.

Abstract

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.

Keywords: PSO-DTC; PSO-based MPPT; PVWPS; dSPACE DS1104.