Robust low complexity multisurface super twisting sliding mode control for DFIG systems

Wind energy has emerged as a key contributor to global decarbonization, with megawatt-class doubly fed induction generators (DFIGs) playing a central role in modern wind turbine systems owing to their high efficiency and controllability. However, conventional sliding mode control (SMC) strategies, including the super-twisting SMC (STSMC), often exhibit limited adaptability and high chattering when exposed to rapidly changing environmental and grid disturbances. In this study, we developed a novel multisurface super-twisting sliding mode control (MSSTSMC) approach that enhances system robustness without increasing structural complexity. By overlapping multiple sliding surfaces, the controller dynamically adjusts its response to disturbance intensity in real time. Comprehensive simulations were conducted for a 2-MW grid-connected DFIG system under step and turbulent wind profiles, severe parameter variations (up to 180%), and time-varying grid frequency fluctuations. The MSSTSMC outperformed the conventional SMC, STSMC, and disturbance-observer-based STSMC. Further, it achieved up to 37% lower active power tracking error and faster convergence time (< 0.75 s), while maintaining a low computational footprint and yielding under 0.1% current total harmonic distortion. These results demonstrate that MSSTSMC offers a practical, scalable, and computationally efficient solution for high-performance wind energy systems operating under dynamic conditions, thereby contributing to more stable and reliable renewable power generation.

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Publication Details

Journal
Scientific Reports
Published
2025-11-27
DOI
https://doi.org/10.1038/s41598-025-29985-1
Primary Topic
Wind Turbine Control Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Robust low complexity multisurface super twisting sliding mode control for DFIG systems

Duehee Lee, Youngsung Kwon, Gyeongjun Kim, Sejin Lee
Scientific Reports
Wind Turbine Control Systems
article

Robust low complexity multisurface super twisting sliding mode control for DFIG systems

Duehee Lee, Youngsung Kwon, Gyeongjun Kim, Sejin Lee
article en

Abstract

Wind energy has emerged as a key contributor to global decarbonization, with megawatt-class doubly fed induction generators (DFIGs) playing a central role in modern wind turbine systems owing to their high efficiency and controllability. However, conventional sliding mode control (SMC) strategies, including the super-twisting SMC (STSMC), often exhibit limited adaptability and high chattering when exposed to rapidly changing environmental and grid disturbances. In this study, we developed a novel multisurface super-twisting sliding mode control (MSSTSMC) approach that enhances system robustness without increasing structural complexity. By overlapping multiple sliding surfaces, the controller dynamically adjusts its response to disturbance intensity in real time. Comprehensive simulations were conducted for a 2-MW grid-connected DFIG system under step and turbulent wind profiles, severe parameter variations (up to 180%), and time-varying grid frequency fluctuations. The MSSTSMC outperformed the conventional SMC, STSMC, and disturbance-observer-based STSMC. Further, it achieved up to 37% lower active power tracking error and faster convergence time (< 0.75 s), while maintaining a low computational footprint and yielding under 0.1% current total harmonic distortion. These results demonstrate that MSSTSMC offers a practical, scalable, and computationally efficient solution for high-performance wind energy systems operating under dynamic conditions, thereby contributing to more stable and reliable renewable power generation.

Scientific ReportsVol. 15(1)
Handong Global University (KR), Konkuk University (KR)
Ministry of Trade, Industry and Energy, Korea Institute of Energy Technology Evaluation and Planning
Openalex Percentile: Top 63%
Wind Turbine Control Systems
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Robust low complexity multisurface super twisting sliding mode control for DFIG systems — Duehee Lee, Youngsung Kwon, et al. · Scientific Reports (2025) | TGRS Research Map | TGRS