Data-Driven Adaptive Neural Network Additional Damping Controller for SSCI Suppression of DFIG-Based Wind Farms

He, Yalan; Zhang, Xiaomei; Jiang, Jinrui; Cao, Zhe; Li, Huiyong; Ma, Meiling; Yuan, Jinhao

doi:10.3390/en19112616

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Open AccessArticle

Data-Driven Adaptive Neural Network Additional Damping Controller for SSCI Suppression of DFIG-Based Wind Farms

by

Yalan He

¹,

Xiaomei Zhang

¹,

Jinrui Jiang

¹,

Zhe Cao

¹,

Huiyong Li

¹,

Meiling Ma

^2,*

and

Jinhao Yuan

²

¹

State Grid Gansu Electric Power Company Economic and Technological Research Institute, Lanzhou 730000, China

²

School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

^*

Author to whom correspondence should be addressed.

Energies 2026, 19(11), 2616; https://doi.org/10.3390/en19112616

Submission received: 28 April 2026 / Revised: 22 May 2026 / Accepted: 25 May 2026 / Published: 28 May 2026

(This article belongs to the Special Issue Safe and Stable Operation of Power-Electronics-Dominated Power Systems)

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Abstract

In this article, a data-driven adaptive neural network additional damping controller (DDANN-ADC) is proposed to regulate rotor-side converters of a DFIG-based power system to suppress sub-synchronous control interaction (SSCI). Firstly, a back propagation (BP) intermediate variable observer is designed to construct a dynamic model of DFIG-based wind farms based on real-time input–output measurement data. Subsequently, a modified cost function is developed for a BP online controller to generate a target control law, thereby contributing additional damping to the DFIG-based power system. The proposed DDANN-ADC can effectively utilize limited data generated during the control process to achieve online system identification and precise control of the system. Then, the stability of DFIG-based power system under the proposed DDANN-ADC is demonstrated with the Lyapunov function. Finally, simulation results reveal that the proposed DDANN-ADC methodology outperforms the traditional method with better adaptability and robustness under different operational conditions.

Keywords: DFIG-based wind farms; data-driven; adaptive neural network; sub-synchronous control interaction; additional damping controller

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MDPI and ACS Style

He, Y.; Zhang, X.; Jiang, J.; Cao, Z.; Li, H.; Ma, M.; Yuan, J. Data-Driven Adaptive Neural Network Additional Damping Controller for SSCI Suppression of DFIG-Based Wind Farms. Energies 2026, 19, 2616. https://doi.org/10.3390/en19112616

AMA Style

He Y, Zhang X, Jiang J, Cao Z, Li H, Ma M, Yuan J. Data-Driven Adaptive Neural Network Additional Damping Controller for SSCI Suppression of DFIG-Based Wind Farms. Energies. 2026; 19(11):2616. https://doi.org/10.3390/en19112616

Chicago/Turabian Style

He, Yalan, Xiaomei Zhang, Jinrui Jiang, Zhe Cao, Huiyong Li, Meiling Ma, and Jinhao Yuan. 2026. "Data-Driven Adaptive Neural Network Additional Damping Controller for SSCI Suppression of DFIG-Based Wind Farms" Energies 19, no. 11: 2616. https://doi.org/10.3390/en19112616

APA Style

He, Y., Zhang, X., Jiang, J., Cao, Z., Li, H., Ma, M., & Yuan, J. (2026). Data-Driven Adaptive Neural Network Additional Damping Controller for SSCI Suppression of DFIG-Based Wind Farms. Energies, 19(11), 2616. https://doi.org/10.3390/en19112616

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Data-Driven Adaptive Neural Network Additional Damping Controller for SSCI Suppression of DFIG-Based Wind Farms

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