Damping Identification of an Operational Offshore Wind Turbine using Enhanced Kalman filter-based Subspace Identification

Operational Modal Analysis (OMA) provides essential insights into the structural dynamics of an Offshore Wind Turbine (OWT). In these dynamics, damping is considered an especially important parameter as it governs the magnitude of the response at the natural frequencies. Violation of the stationary white noise excitation requirement of classical OMA algorithms has troubled the identification of operational OWTs due to harmonic excitation caused by rotor rotation. Recently, a novel algorithm was presented that mitigates harmonics by estimating a harmonic subsignal using a Kalman filter and orthogonally removing this signal from the response signal, after which the Stochastic Subspace Identification algorithm is used to identify the system. Although promising results are achieved using this novel algorithm, several shortcomings are still present like the numerical instability of the conventional Kalman filter and the inability to use large or multiple datasets. This paper addresses these shortcomings and applies an enhanced version to a multi-megawatt operational OWT using an economical sensor setup with two accelerometer levels. The algorithm yielded excellent results for the first three tower bending modes with low variance. A comparison of these results against the established time-domain harmonics-mitigating algorithm, Modified LSCE, and the frequency-domain PolyMAX algorithm demonstrated strong agreement in results.