Health diagnosis for wind turbine blades using wavelet transform
Date Issued
2016
Date
2016
Author(s)
Chin, Jeng-Yu
Abstract
The major object of this research is to analyze the noise feature of wind turbine while operating and to detect blade damage with noise feature. Many components of wind turbine will be worn after days of running. Different types of damage would have different sorts of signal feature, and the noise of worn turbine blade is extraordinary. Thus, the damage of turbine blades could be judged by hearing. Generally, it takes months for a concerned department to notice blade damage after sending employees to listen to the noise of turbine. If on land wind turbines health diagnosis is such a waste of time, the time consumption of off shore wind turbines would be unbelievable. Time-frequency analysis is a useful method of signal processing, it shows the cor-respondence between time and frequency and analyze what the signal stands for. This research applies time-frequency analysis to wind turbine health diagnosis with contin-uous complex Morlet wavelet analysis. The approximate process is to analyze sound wave of an undamaged wind turbine with wavelet transform, marginal frequency, decibel transformation, A-weighting and polynomial regression. By this process we can build a normal model to calculate the sum of squared errors between normal mod-el and damaged wind turbine blades noise, thus we can estimate damage severity of the blades. Most of other research of wind turbine health diagnosis need to stop and take apart wind turbines to detect damage. Our results shows we can detect damage while turbines are operating, which reduce time, labour and money consumption sig-nificantly. We record several sets of sound of wind turbines and represent the results of our detection, which will be proved by blade pictures.
Subjects
wavelet analysis
marginal spectrum
polynomial regression
wind turbine blades health diagnosis.
Type
thesis
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