Abstract
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.