Robust Control for DFIG-Based WECS with ANN-Based MPPT

Achraf El Ouali; Yassine Lakhal; Mohamed Benchagra; Hamid Hamid; Mohamed   Vall Ould Mohamed

doi:10.51646/jsesd.v14iSTR2E.894

Authors

Achraf El Ouali Team of engineering and applied physics Higher school of Technology of Beni Mellal, Morocco. https://orcid.org/0009-0004-4639-8736
Yassine Lakhal Team of engineering and applied physics Higher school of Technology of Beni Mellal, Morocco. https://orcid.org/0000-0003-3278-237X
Mohamed Benchagra Team of engineering and applied physics Higher school of Technology of Beni Mellal, Morocco. https://orcid.org/0000-0001-5763-634X
Hamid Chojaa Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco.
Mohamed Vall Ould Mohamed Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 75471, Saudi Arabia.

DOI:

https://doi.org/10.51646/jsesd.v14iSTR2E.894

Keywords:

Artificial Neural Network, Maximum Power Point Tracking, Integral Sliding Mode Control.

Abstract

Mitigating nonlinearities and parameter fluctuations in high-rated wind energy systems is crucial for efficient energy conversion and grid integration. This paper presents a robust Integral Sliding Mode Control (ISMC) strategy for monitoring active and reactive power in a DFIG-based wind turbine. An artificial neural network based MPPT algorithm enhances speed control and addresses power fluctuations. The proposed ISMC ensures an optimal dynamic response to wind variations. Its performance is compared using a PI controller in Field-Oriented Control (FOC_PI) in MATLAB/Simulink on a wind system of 1.5 MW and tested under real-wind conditions. Simulation results confirm that ISMC outperforms FOC_PI in reference tracking, accuracy, dynamic behavior, and current distortion reduction.

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