Abstract
The identification of operational modal parameters of a wind turbine blade is fundamental for online damage detection. In this paper, we use binocular photogrammetry technology instead of traditional contact sensors to measure the vibration of blade and apply the advanced stochastic system identification technique to identify the blade modal frequencies automatically when only output data are available. Image feature extraction and target point tracking (PT) are carried out to acquire the displacement of labeled targets on the wind turbine blade. The vibration responses of the target points are obtained. The data-driven stochastic subspace identification (SSI-Data) method based on the Kalman filter prediction sequence is explored to extract modal parameters from vibration response under unknown excitation. Hankel matrixes are reconstructed with different dimensions, so different modal parameters are produced. Similarity of these modal parameters is compared and used to cluster modes into groups. Under appropriate tolerance thresholds, spurious modes can be eliminated. Experiment results show that good effects and stable accuracy can also be achieved with the presented photogrammetry vibration measurement and automatic modal identification algorithm.
Highlights
Modal analysis of a wind turbine blade is a key step for analysis and testing of the dynamic performance of blade structure and is fundamental for blade design and manufacturing [1]
In order to verify the validity and reliability of our method, the vibration measurement experiment of a 3 kW wind turbine blade model was carried out, and the modal parameters were identified based on the measured vibration response data
A vibration detection method based on photogrammetry and modal parameters identification with improved subspace identification (SSI)-Data is presented in this paper. e
Summary
Modal analysis of a wind turbine blade is a key step for analysis and testing of the dynamic performance of blade structure and is fundamental for blade design and manufacturing [1]. Operational modal analysis means to analyze the modal parameters only based on structural dynamic response, without knowing excitation signals. The stochastic subspace identification (SSI) is a classical time-domain method to identify modal parameters for a linear system directly from structural vibration response data [10]. Xu et al [15] used a digital camera to measure the displacement vibration of an antenna and applied the eigensystem realization algorithm to identify the modal parameters, which were susceptible to background noises. Wang et al [16] realized geometric modeling of a light fan blade and some leaves, and modal analysis with PolyMax method provided by commercial software was carried out. Photogrammetry is used to measure marked points vibration, and modal analysis is carried out with data-driven stochastic subspace identification (SSI-Data). Considering the impact of noise on vibration data, spurious modes are discriminated through reconstructing Hankel matrix multiple times and fuzzy clustering method. e modal similarity is calculated and differentiated by a new index based on tolerance and modal confidence. e modal parameters are clustered and automatically identified with statistical analysis of results from dynamic vibration sequences
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