Oscillation-Characteristic-Based Method for Identifying Control Parameters of DFIG-Based Wind Turbines

WANG Ganyao, LÜ Jing, DAI Jinshui, SU Tianyu, WANG Xiao

Electric Power Construction ›› 2026, Vol. 47 ›› Issue (9) : 143-154.

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PDF(3148 KB)
Electric Power Construction ›› 2026, Vol. 47 ›› Issue (9) : 143-154. DOI: 10.12204/j.issn.1000-7229.2026.09.011
Renewable Energy and Energy Storage

Oscillation-Characteristic-Based Method for Identifying Control Parameters of DFIG-Based Wind Turbines

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Abstract

[Objective] In practical wind farms, the key control parameters of aged or imported wind turbines are often inaccessible, which hampers accurate system modeling. To address this issue, this paper proposes a data-driven identification method based on oscillation characteristics to determine these critical parameters of doubly-fed induction generator (DFIG)-based wind turbines. [Methods] Using only limited field oscillation data, the method first identifies the crucial control parameters via phase-margin sensitivity analysis, and then constructs a neural network model that takes active power output and oscillation features (frequency and damping ratio) as inputs and outputs the key control parameters, without requiring any adjustment of the control system or injection of external excitation. [Results] Simulation and field-measurement cases demonstrate that under noise conditions with a signal-to-noise ratio (SNR) of no less than 30 dB, the identification errors for all key control parameters remain below 10%, and the deviation in reproducing the oscillation characteristics for the actual case is less than 1%. [Conclusions] By exploiting the intrinsic correlation between oscillation data and control parameters, the proposed method circumvents the risks and practical constraints associated with external excitation injection, offering a novel approach for secure identification of key control parameters in “grey-box” wind turbines.

Key words

doubly-fed induction generator (DFIG)-based wind turbine / sub‑synchronous oscillation / control parameter identification / Hilbert‑Huang transform / neural networks

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WANG Ganyao , LÜ Jing , DAI Jinshui , et al . Oscillation-Characteristic-Based Method for Identifying Control Parameters of DFIG-Based Wind Turbines[J]. Electric Power Construction. 2026, 47(9): 143-154 https://doi.org/10.12204/j.issn.1000-7229.2026.09.011

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Yu Jing, Lin Hongfei, Wang Xiao, et al. Mechanism analysis of near fundamental-frequency positive/negative-sequence oscillations in MMC-HVDC connected direct-drive wind farm[J]. Electric Power Construction, 2024, 45(2): 10-25.

Positive- and negative-sequence oscillation phenomena close to the fundamental frequency (40-60 Hz) occur in a practical modular multilevel converter-based high-voltage DC (MMC-HVDC) transmission system for new energy integration, which has led to a decrease in the power output of new energy sources. The mechanism of near-fundamental-frequency oscillations is more complex and has more influencing factors than oscillations in other frequency bands. This study focuses on the near-fundamental-frequency oscillation stability of an MMC-HVDC-connected direct-drive wind farm. Refined impedance models of the direct-drive wind turbine and sending-end MMC are established by considering positive and negative sequence controls. Based on the established impedance models, the mechanisms of the near-fundamental-frequency positive- and negative-sequence oscillations between the direct-drive wind farm and the sending-end MMC are revealed. In addition, the parameter phase-margin sensitivity is defined, and the key influencing factors of the near-fundamental-frequency oscillation stability of the interconnected system were analyzed quantitatively. Finally, an electromagnetic transient simulation model of the MMC-HVDC-connected direct-drive wind farm is developed. The near-fundamental-frequency positive- and negative-sequence oscillation phenomena in the actual project are reproduced, and the correctness of the near-fundamental-frequency oscillation mechanism analysis is validated.

Footnotes

作者贡献声明(Authors' Contributions): 王干尧负责整体方案实施、建模与仿真、数据分析、论文撰写等;吕敬负责制定研究方案,提出论文框架,论文修订,实测数据收集等;戴金水参与仿真分析及论文审阅与修订;苏田宇参与问题提出、研究方案制定,提供实测数据;王潇参与问题提出及研究方案制定。所有作者均阅读并同意了论文终稿内容。

利益冲突声明(Conflict of Interests): 所有作者声明不存在利益冲突。

Funding

National Natural Science Foundation of China(52277195)
North China Electric Power Research Institute Co., Ltd.(KJZ2024105)
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