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Digital twin-driven online intelligent assessment of wind turbine drivetrain
  • +2
  • Yadong Zhou,
  • Zhou Jianxing,
  • Quanwei Cui,
  • Jianmin Wen,
  • Xiang Fei
Yadong Zhou
Xinjiang University
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Zhou Jianxing
Xinjiang University

Corresponding Author:[email protected]

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Quanwei Cui
Xinjiang University
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Jianmin Wen
Xinjiang University
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Xiang Fei
Xinjiang University
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Condition monitoring and evaluation of wind turbine drivetrain hold great importance. However, the implementation of real-time monitoring often faces challenges in efficiency and accuracy, as the drivetrain typically operates under harsh conditions. In order to resolve this, this paper proposes a vibration-based damage monitoring digital twin (VBDM-DT) that enables the online intelligent evaluation of wind turbine drivetrain. The VBDM-DT integrates a random wind load model, a high-fidelity dynamics model, and a fatigue damage model. The random wind load model takes the wind speed from the supervisory control and data acquisition (SCADA) as input to estimate the input torque of the drivetrain in real time. Simultaneously, VBDM-DT uses the vibration signals from the condition monitoring system (CMS) to intelligently calibrate the dynamics model, allowing it to be continuously adjusted and optimized in response to actual vibrations. And the fatigue damage model takes the real-time dynamic load estimated by the high-fidelity dynamics model as input to realize real-time fatigue damage monitoring of key components of the drivetrain. The VBDM-DT model is applied to a 2 MW wind turbine drivetrain to verify the effectiveness of the proposed method. In addition, a visualization platform is developed to vividly and intuitively display the real-time operating information, dynamic loads, and damage levels of the key components of wind turbine.
25 Oct 2023Submitted to Wind Energy
27 Oct 2023Submission Checks Completed
27 Oct 2023Assigned to Editor
30 Oct 2023Review(s) Completed, Editorial Evaluation Pending
11 Nov 2023Reviewer(s) Assigned
01 Mar 20241st Revision Received
06 Mar 2024Review(s) Completed, Editorial Evaluation Pending
30 Mar 2024Editorial Decision: Revise Minor
07 Apr 2024Editorial Decision: Accept