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Article

ANN-Based Direct Power Control for Improved Dynamic Performance of DFIG-Based Wind Turbine System: Experimental Validation

by
Hamid Chojaa
1,
Mishari Metab Almalki
2 and
Mahmoud A. Mossa
3,*
1
Industrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco
2
Department of Electrical Engineering, Faculty of Engineering, Al-Baha University, Alaqiq 65779-7738, Saudi Arabia
3
Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt
*
Author to whom correspondence should be addressed.
Machines 2025, 13(11), 1006; https://doi.org/10.3390/machines13111006
Submission received: 5 October 2025 / Revised: 27 October 2025 / Accepted: 28 October 2025 / Published: 1 November 2025
(This article belongs to the Special Issue Wound Field and Less Rare-Earth Electrical Machines in Renewables)
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Abstract

Direct power control (DPC) is a widely accepted control scheme utilized in renewable energy applications owing to its several advantages over other control mechanisms, including its simplicity, ease of implementation, and faster response. However, DPC suffers from inherent drawbacks and limitations that constrain its applicability. These restrictions include notable ripples in active power and torque, as well as poor power quality brought on by the usage of a hysteresis regulator for capacity management. To address these issues and overcome the limitations of DPC, this study proposes a novel approach that incorporates artificial neural networks (ANNs) into DPC. The proposed technique focuses on doubly fed induction generators (DFIGs) and is validated through experimental testing. ANNs are employed to recompense for the deficiencies of the hysteresis controller and switching table. The intelligent DPC technique is then compared to three other strategies: classic DPC, backstepping control, and integral sliding-mode control. Various tests are conducted to compare the ripple ratio, current quality, durability, response time, and reference tracking. The validity and robustness of the proposed intelligent DPC for DFIGs are verified through both simulation and experimental results obtained from the MATLAB/Simulink environment and the Real-Time Interface (RTI) of the dSPACE DS1104 controller card. The results confirm that the intelligent DPC outperforms conventional control strategies in terms of stator current harmonic distortion, dynamic response, power ripple minimization, reference tracking accuracy, robustness, and overshoot reduction. Overall, the intelligent DPC exhibits superior performance across all evaluated criteria compared to the alternative approaches.
Keywords: ANN; Backstepping; DPC; dSPACE DS1104; ISMC; WECS ANN; Backstepping; DPC; dSPACE DS1104; ISMC; WECS

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MDPI and ACS Style

Chojaa, H.; Almalki, M.M.; Mossa, M.A. ANN-Based Direct Power Control for Improved Dynamic Performance of DFIG-Based Wind Turbine System: Experimental Validation. Machines 2025, 13, 1006. https://doi.org/10.3390/machines13111006

AMA Style

Chojaa H, Almalki MM, Mossa MA. ANN-Based Direct Power Control for Improved Dynamic Performance of DFIG-Based Wind Turbine System: Experimental Validation. Machines. 2025; 13(11):1006. https://doi.org/10.3390/machines13111006

Chicago/Turabian Style

Chojaa, Hamid, Mishari Metab Almalki, and Mahmoud A. Mossa. 2025. "ANN-Based Direct Power Control for Improved Dynamic Performance of DFIG-Based Wind Turbine System: Experimental Validation" Machines 13, no. 11: 1006. https://doi.org/10.3390/machines13111006

APA Style

Chojaa, H., Almalki, M. M., & Mossa, M. A. (2025). ANN-Based Direct Power Control for Improved Dynamic Performance of DFIG-Based Wind Turbine System: Experimental Validation. Machines, 13(11), 1006. https://doi.org/10.3390/machines13111006

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