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

TL;DR

This paper presents a Kalman filter-based subspace identification method to accurately estimate damping in operational offshore wind turbines, effectively mitigating harmonic excitation effects from rotor rotation.

Contribution

It introduces a novel harmonic mitigation algorithm using Kalman filtering combined with subspace identification for offshore wind turbine dynamics analysis.

Findings

Successfully identified the first three tower bending modes.

Improved identification accuracy by concatenating multiple datasets.

Validated results against established harmonic mitigation algorithms.

Abstract

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.