https://scholars.lib.ntu.edu.tw/handle/123456789/576822
標題: | Application of Diagnostic Technique for Noise Characteristic to Judging Wind Turbine Blade Abnormity in Actual Operation | 作者: | Wang C.-N Tang Y.-C. CHAO-NAN WANG |
關鍵字: | Electric utilities; Extraction; Feature extraction; Frequency estimation; Regression analysis; Turbomachine blades; Wind; Wind turbines; Diagnostic techniques; Marginal spectrum; Morlet Wavelet; Noise characteristic; Statistical regression analysis; Taiwan power companies; Time frequency analysis; Time varying analysis; Turbine components | 公開日期: | 2017 | 卷: | 38 | 期: | 2 | 起(迄)頁: | 145-153 | 來源出版物: | Journal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'eng Hsuebo Pao | 摘要: | This paper provides an estimation model for noise signal characteristic diagnosis based on the Time-Frequency analysis technique. The marginal spectrum and statistical regression analysis is used as an estimation method for feature extraction. In order to approach the actual conditions, with the assistance of Department of Renewable Energy, Taiwan Power Company, the noise signals of a blade-damaged wind turbine and normal wind turbine are measured. In the case of low wind speed and noise, the time-frequency spectra are compared, and the feature magnification indicator is analyzed. The results show that the blade crack is caused by high frequency noise, mainly above 4000Hz. The time-varying analysis of the indicator shows that the index value is apparently enlarged when the damaged blade rotation is measured by microphone, and the number of damaged blades can be obtained. ? 2017, Chinese Mechanical Engineering Society. All right reserved. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031916491&partnerID=40&md5=e82eb26ef10f10b427bf51012d420131 https://scholars.lib.ntu.edu.tw/handle/123456789/576822 |
ISSN: | 2579731 | SDG/關鍵字: | Electric utilities; Extraction; Feature extraction; Frequency estimation; Regression analysis; Turbomachine blades; Wind; Wind turbines; Diagnostic techniques; Marginal spectrum; Morlet Wavelet; Noise characteristic; Statistical regression analysis; Taiwan power companies; Time frequency analysis; Time varying analysis; Turbine components |
顯示於: | 工程科學及海洋工程學系 |
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