  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. In
this paper, the algorithm is tested on field data obtained from a
multi-megawatt operational OWT using an economical sensor setup with two
accelerometer levels. The first three tower bending modes could be
distinguished, and, through the LQ-decomposition used in the algorithm, the
identification results could be improved further by concatenating multiple
datasets. A comparison against established harmonics-mitigating algorithms,
Modified Least-squared Complex Exponential and PolyMAX, was done to validate
the results.
