This article will present and discuss the approach and the first results of a long-term dynamic monitoring campaign on an offshore wind turbine in the Belgian North Sea. It focuses on the vibration levels and modal parameters of the fundamental modes of the support structure. These parameters are crucial to minimize the operation and maintenance costs and to extend the lifetime of offshore wind turbine structure and mechanical systems. In order to perform a proper continuous monitoring during operation, a fast and reliable solution, applicable on an industrial scale, has been developed. It will be shown that the use of appropriate vibration measurement equipment together with state-of-the art operational modal analysis techniques can provide accurate estimates of natural frequencies, damping ratios, and mode shapes of offshore wind turbines. The identification methods have been automated and their reliability has been improved, so that the system can track small changes in the dynamic behavior of offshore wind turbines. The advanced modal analysis tools used in this application include the poly-reference least squares complex frequency-domain estimator, commercially known as PolyMAX, and the covariance-driven stochastic subspace identification method. The implemented processing strategy will be demonstrated on data continuously collected during 2 weeks, while the wind turbine was idling or parked.

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In this contribution, first, the results in the development of a structural health monitoring approach for the foundations of an offshore wind turbine based on its resonance frequencies will be presented. Key problems are the operational and environmental variability of the resonance frequencies of the turbine that potentially conceal any structural change. This article uses a (non-)linear regression model to compensate for the environmental variations. An operational case-by-case monitoring strategy is suggested to cope with the dynamic variability between different operational cases of the turbine. Real-life data obtained from an offshore turbine on a monopile foundation are used to validate the presented strategy and to demonstrate the performance of the presented approach. First, the results indicate an overall stiffening of the investigated structure.

Offshore wind turbines (OWTs) are subjected to both quasi-static loads originating from variations in the thrust force and dynamic loads linked to turbulence, waves and turbine dynamics. Both types of loads contribute to fatigue life progression and thus define the turbine's age. As a structural health monitoring solution, one could thus directly measure the stress history at fatigue critical locations. However, for OWTs on monopile foundations some fatigue critical locations are located below the seabed. Installing strain sensors at these hotspots is therefore impossible for existing wind turbines. This measurement restriction is overcome by reconstructing the full-field response of the structure based on the limited number of accelerometers and strain sensors (installed at a few easily accessible locations) and a calibrated finite element model of the system. The system model uses a multi-band modal expansion approach constituted of the quasi-static and dynamic contributions. These contributions are superimposed to reconstruct the stress history at all degrees of freedom of the finite element model, and the subsequent assess fatigue life consumption at all fatigue hot spots of the OWT. In this paper, the proposed virtual sensing technique is validated by predicting the stresses in the transition piece with 12 days of consecutive measurements from an operational OWT. The data set contains both variations in environmental and operating conditions as well as extreme events. Finally, a full-field strain assessment in the tower and foundation system of the OWT is demonstrated.

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