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  4. Vibration Analysis and Damage Detection of a Research-scale Wind Turbine Blade
 
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Vibration Analysis and Damage Detection of a Research-scale Wind Turbine Blade

Date Issued
2014
Date
2014
Author(s)
Hsiung, Wan-Ying
URI
http://ntur.lib.ntu.edu.tw//handle/246246/260867
Abstract
The objective of this study was to validate modal analysis, system identification and damage detection of small-scale rotating wind turbine blades in the laboratory and in the field. Here, wind turbine blades were instrumented with accelerometers and strain gages, and data acquisition was achieved using a prototype wireless sensing system. In the first portion of this study conducted in the laboratory, sensors were installed onto metallic structural elements that were fabricated to be representative of an actual wind blade. In order to control the excitation (rotation of the wind blade), a motor was used to spin the blades at controlled angular velocities. The wind turbine was installed on a shaking table for testing. Under the circumstance of the couple of rotating wind turbine blade and tower with base excitation to measure the signals of different rotation speed. On the other hand, the second part of this study which is field experiment utilized a small-scale horizontal axis wind turbine system mounted on the rooftop of a building at University of California, Davis. The model of this wind turbine is Bergey Windpower XL.1. Compared to the lab tests, the main difference was that the field tests relied on actual wind excitations (as opposed to a controlled motor). In laboratory test, the turbine blade divided into two cases, undamaged and damaged blades. Take two loose bolts connection in one of the turbine blade as damaged cases and use damage index to identify the damage occurrence in blade. In addition, the raw data from both laboratory tests and field tests were analyzed by signal processing and system identification techniques for deriving the model response of the blades. The Multivariate Singular Spectrum Analysis (MSSA), Covariance-driven Stochastic Subspace Identification method (SSI-COV) and Data-driven Subspace Identification method (SI-DATA) were used to identify the dynamic characteristics of the system. These above experiments confirmed that dynamic characterization of rotating wind turbines system was feasible, and the results will guide future wind turbine monitoring studies.
Subjects
協方差隨機子空間識別法
奇異譜分析法
風機系統
子空間識別法
結構健康監測
損壞偵測
Type
thesis
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ntu-103-R01521205-1.pdf

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