Vector-autoregressive based Vibration Monitoring of Wind Energy Turbines Using Laser Scanner Profile Measurements

Structural health monitoring of wind energy turbines (WET) is crucial due to the potential presence of an imbalanced rotor, leading to adverse centrifugal forces. In this research, non-contact vibration monitoring of a WET is conducted using a terrestrial laser scanner (TLS) of the type Zoller+Fröhlich IMAGER 5016 in a two-dimensional (2D) profile mode, and with a rotation speed of 55 revolutions per second. The TLS profile measurements cover the entire pillar up to a height of 100 meter. Therefore, the challenges imposed by conventional measurement systems, such as accelerometers or inductive displacement transducers, are tackled through the high spatial resolution of the TLS measurements and non-contact measurement methods. Additionally, both time and cost are reduced regarding the sensor installation. In general, the WET is measured in two different directions to account for significant movements, both in the direction of the wind and perpendicular to it. To initiate the analysis, time series are generated from the profile measurements for various positions covering the entire pillar. A robust time-domain modal parameter identification approach based on the Vector-autoregressive (VAR) process with multivariate t-distributed random deviations (VAR-RT-MPI) is proposed to estimate modal parameters, including eigenfrequencies, eigenforms, and modal damping. Besides, it allows estimating unknown auto- and cross-correlation coefficients of the VAR process, the cofactor matrix, and the degrees of freedom of the t-distribution. The VAR-RT-MPI algorithm enables to jointly estimate the eigenfrequencies and damping ratio coefficients considering multiple time series. Additionally, the spatio-temporal model of the pillar is characterised based on the estimates of the amplitudes (in a submillimetre range) and phase shifts at different positions. Vibration monitoring was conducted on a WET located in Hannover, Germany, and the results were compared with a well-known covariance driven stochastic subspace identification (SSI-COV) approach.