Development of a modified stochastic subspace identification method for rapid structural assessment of in-service utility-scale wind turbine towers

The strong drive to harness wind energy has recently led to rapid growth of wind farm construction. Wind turbine towers with increased sizes and flexibility experience large vibrations. Structural health monitoring of wind turbines is proposed in the wind energy industry to ensure their proper performance and save maintenance costs. This study proposes a system identification method for vibration-based structural assessment of wind turbine towers. This method developed based on the stochastic subspace identification method can identify modal parameters of structures in operating conditions with harmonic components in excitations. It benefits wind turbine tower structural health assessment because classical operational modal analysis methods can fail as periodic rotation excitation from a turbine introduces harmonic disturbance to tower structure response data. The effectiveness, accuracy and robustness of the proposed method were numerically investigated and verified through a lumped-mass system model. The method was then applied to an in-service utility-scale wind turbine tower. The field testing campaign and modal parameter identification as well as structural assessment results were presented. Copyright (c) 2017 John Wiley & Sons, Ltd.