Performance Analysis of a Direct Torque Controlled PV Water Pumping System Using Grey Wolf Optimizer and Incremental Conductance Maximum Power Point Tracking under Variable Irradiance

Oussama Idbouhouch; Mourad Zegrari; Nabila Rabbah

doi:10.48084/etasr.16460

Authors

Oussama Idbouhouch Laboratory of Digital Engineering for Leading Technologies and Automation (DELTA), National Higher School of Arts and Crafts (ENSAM), Hassan II University, Casablanca, Morocco | Green Energy Park Research Platform (GEP), Research Institute for Solar Energy and New Energies (IRESEN), Mohammed VI Polytechnic University (UM6P), Benguerir, Morocco
Mourad Zegrari Laboratory of Digital Engineering for Leading Technologies and Automation (DELTA), National Higher School of Arts and Crafts (ENSAM), Hassan II University, Casablanca, Morocco
Nabila Rabbah Laboratory of Cyber Physical Systems (LCCPS), National Higher School of Arts and Crafts (ENSAM), Hassan II University, Casablanca, Morocco

Volume: 16 | Issue: 2 | Pages: 33596-33610 | April 2026 | https://doi.org/10.48084/etasr.16460

Received: 23 November 2025 | Revised: 15 December 2025, 26 December 2025, 31 December 2025, and 8 January 2026 | Accepted: 9 January 2026 | Online: 23 February 2026

Corresponding author: Oussama Idbouhouch

Abstract

This paper presents a performance analysis of a Photovoltaic (PV) water pumping system driven by an Induction Motor (IM) and controlled through the Direct Torque Control (DTC) strategy. The DTC approach was implemented to directly regulate the electromagnetic torque and stator flux, thereby ensuring fast dynamic response and high-efficiency operation. To optimize the energy conversion process under variable irradiance, two Maximum Power Point Tracking (MPPT) techniques were integrated into the DC–DC boost converter: the Incremental Conductance (INC) and the Grey Wolf Optimizer (GWO). The main objective of this study is to assess and compare the dynamic and steady-state performance of the DTC–INC and DTC–GWO configurations under both uniform and partially shaded conditions. The simulation results, performed using MATLAB/Simulink, show that while both strategies achieve stable operation under normal irradiance, the GWO-based MPPT exhibits superior adaptability under partial shading by accurately locating the Global Maximum Power Point. This results in an approximate 32% increase in hydraulic power relative to the INC approach, thereby improving torque stability, accelerating convergence, and increasing hydraulic efficiency. This study contributes to the advancement of intelligent PV-based pumping systems by integrating heuristic optimization and high-performance motor control, offering a promising solution for sustainable water supply in remote and semi-arid regions.

Keywords:

photovoltaic water pumping, direct torque control, grey wolf optimizer, incremental conductance, MPPT, partial shading, induction motor

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