Wind turbine blade damping control method, system and device, storage medium and program product

By using a CNN-LSTM neural network-based wind turbine blade damping control method, the damping force of the electrovariable damper is dynamically adjusted, which solves the problem of limited vibration suppression effect caused by the fixed damping value of the traditional damper, and improves the safety and reliability of the wind turbine.

CN121273533APending Publication Date: 2026-01-06NANJING UNIV OF AERONAUTICS & ASTRONAUTICS +1
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Patent Information

Application Number
CN202511765086.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-27
Publication Date
2026-01-06

AI Technical Summary

Technical Problem

Traditional passive dampers have fixed damping values, which limits their effectiveness in suppressing wind turbine blade vibration, increases the risk of resonance, and affects the safety and reliability of wind turbines.

Method used

A CNN-LSTM neural network-based damping control method for wind turbine blades is adopted. By acquiring blade vibration monitoring data, the effective area of ​​the magnetic poles of the electrovariable damper is adjusted using a pre-trained damping control model, and the damping force is dynamically adjusted to adapt to the vibration requirements of the wind turbine.

Benefits of technology

It has achieved fully automatic control of wind turbine blade damping, which significantly improves the safety and reliability of wind turbines and reduces vibration risks.

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Abstract

The invention relates to a wind turbine blade damping control method, system and device, a storage medium and a program product. The method comprises the following steps: acquiring blade vibration monitoring data; according to the blade vibration monitoring data, using a pre-trained wind turbine blade damping control model to obtain operation prediction data and damping demand data; wherein the pre-trained wind turbine blade damping control model is obtained by training based on a CNN-LSTM neural network; and according to the operation prediction data and the damping demand data, the effective coincident area between two magnetic poles of an electrorheological damper of the wind turbine blade is adjusted, so that the damping of the electrorheological damper is adjusted by changing the magnetic field intensity of the electrorheological damper. The safety and reliability of operation of the wind turbine can be effectively improved.
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