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

Dai, Kaoshan; Wang, Ying; Huang, Yichao; Zhu, Weidong; Xu, Yongfeng

doi:10.1002/we.2117

Cited by 30 publications

(14 citation statements)

References 35 publications

(38 reference statements)

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“…Vibration monitoring of WTs in combination with numerical simulations, optical measurement techniques, or microwave sensors have been investigated by several other studies before. The dynamic properties of different WT types were analyzed regarding a structural health assessment.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous identification of wind turbine vibrations by using seismic data, elastic modeling and laser Doppler vibrometry

et al. 2020

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This work compares continuous seismic ground motion recordings over several months on top of the foundation and in the near field of a wind turbine (WT) at Pfinztal, Germany, with numerical tower vibration simulations and simultaneous optical measurements. We are able to distinguish between the excitation of eigenfrequencies of the tower‐nacelle system and the influence of the blade rotation on seismic data by analyzing different wind and turbine conditions. We can allocate most of the major spectral peaks to either different bending modes of the tower, flapwise, and edgewise bending modes of the blades or multiples of the blade‐passing frequency after comparing seismic recordings with tower simulation models. These simulations of dynamic properties of the tower are based on linear modal analysis performed with finite beam elements. To validate our interpretations of the comparison of seismic recordings and simulations, we use optical measurements of a laser Doppler vibrometer at the tower of the turbine at a height of about 20 m. The calculated power spectrum of the tower vibrations confirms our interpretation of the seismic peaks regarding the tower bending modes. This work gives a new understanding of the source mechanisms of WT‐induced ground motions and their influence on seismic data by using an interdisciplinary approach. Thus, our results may be used for structural health purposes as well as the development of structural damping methods, which can also reduce ground motion emissions from WTs. Furthermore, it demonstrates how numerical simulations of wind turbines can be validated by using seismic recordings and laser Doppler vibrometry.

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Section: Introductionmentioning

confidence: 99%

Simultaneous identification of wind turbine vibrations by using seismic data, elastic modeling and laser Doppler vibrometry

et al. 2020

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“…The SHM of wind turbine towers was proposed for system monitoring and was integrated into the Supervisory Control and Data Acquisition (SCADA) systems [17][18][19][20]. The SHM of HAWT towers concerns problems including resonance responses caused by operation frequency or blade passing frequency [21][22][23], foundation scouring [24], blade flange conditions [25], and structural performances under extreme events [26,27].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Study of a Rooftop Vertical Axis Wind Turbine Tower Based on an Automated Vibration Data Processing Algorithm

et al. 2018

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When constructed on tall building rooftops, the vertical axis wind turbine (VAWT) has the potential of power generation in highly urbanized areas. In this paper, the ambient dynamic responses of a rooftop VAWT were investigated. The dynamic analysis was based on ambient measurements of the structural vibration of the VAWT (including the supporting structure), which resides on the top of a 24-story building. To help process the ambient vibration data, an automated algorithm based on stochastic subspace identification (SSI) with a fast clustering procedure was developed. The algorithm was applied to the vibration data for mode identification, and the results indicate interesting modal responses that may be affected by the building vibration, which have significant implications for the condition monitoring strategy for the VAWT. The environmental effects on the ambient vibration data were also investigated. It was found that the blade rotation speed contributes the most to the vibration responses.

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“…Zierath et al used a classical modal analysis (CMA) and OMA to obtain the accurate modal parameters of a 2 MW wind turbine [10]. Dai et al proposed a modified stochastic subspace identification method to rapidly assess a wind turbine tower [11]. Recently, the automated OMA technique has also been applied to wind turbine systems to track their dynamic behavior under operational conditions.…”

Section: Introductionmentioning

confidence: 99%

Resonance Monitoring of a Horizontal Wind Turbine by Strain-Based Automated Operational Modal Analysis

Tang

Wang

et al. 2020

Energies

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A strain-based automated operational modal analysis algorithm is proposed to track the long-term dynamic behavior of a horizontal wind turbine under operational conditions. This algorithm is firstly validated by a scaled wind turbine model, and then it is applied to the dynamic strain responses recorded from a 5 MW wind turbine system. We observed variations in the fundamental frequency and 1f, 3f excitation frequencies due to the mass imbalance of the blades and aerodynamic excitation by the tower dam or tower wake. Inspection of the Campbell diagram revealed that the adverse resonance phenomenon and Sommerfeld effect causing excessive vibrations of the wind tower.

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

Cited by 30 publications

References 35 publications

Simultaneous identification of wind turbine vibrations by using seismic data, elastic modeling and laser Doppler vibrometry

Simultaneous identification of wind turbine vibrations by using seismic data, elastic modeling and laser Doppler vibrometry

Dynamic Study of a Rooftop Vertical Axis Wind Turbine Tower Based on an Automated Vibration Data Processing Algorithm

Resonance Monitoring of a Horizontal Wind Turbine by Strain-Based Automated Operational Modal Analysis

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