Constructing Condition Monitoring Model of Wind Turbine Blades

Jong-Yih Kuo, Shang-Yi You, Hui-Chi Lin, Chao-Yang Hsu and Baiying Lei
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Jong-Yih Kuo: Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106344, Taiwan
Shang-Yi You: Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106344, Taiwan
Hui-Chi Lin: Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106344, Taiwan
Chao-Yang Hsu: Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106344, Taiwan
Baiying Lei: Health Science Center, School of Biomedical Engineering, Shenzhen University, Shenzhen 518037, China

Mathematics, 2022, vol. 10, issue 6, 1-13

Abstract: Wind power has become an indispensable part of renewable energy development in various countries. Due to the high cost and complex structure of wind turbines, it is important to design a method that can quickly and effectively determine the structural health of the generator set. This research proposes a method that could determine structural damage or weaknesses in the blades at an early stage via a model to monitor the sound of the wind turbine blades, so as to reduce the quantity of labor required and frequency of regular maintenance, and to repair the damage rapidly in the future. This study used the operating sounds of normal and abnormal blades as a dataset. The model used discrete wavelet transform (DWT) to decompose the sound into different frequency components, performed feature extraction in a statistical measure, and combined with outlier exposure technique to train a deep neural network model that could capture abnormal values deviating from the normal samples. In addition, this paper observed that the performance of the monitoring model on the MIMII dataset was also better than the anomaly detection models proposed by other papers.

Keywords: anomaly detection; machine learning; wavelet transform (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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