Acoustical damage detection of wind turbine blade using the improved incremental support vector data description. (August 2020)

• Record Type: Journal Article
• Authors: Chen, Bin; Yu, Songhao; Yu, Yang; Zhou, Yilin
• Is Part Of: Renewable energy. Volume 156(2020)
• Page Start: 548
• Page End: 557
• Publication Date: 2020-08
• Subjects: Wind turbine blade -- Damage detection -- Acoustic signal -- Support vector data description -- Incremental learning; WTB Wind turbine blade -- SVDD Support vector data description -- AE Acoustic emission -- FBG Fiber brag grating -- UAV Unmanned aerial vehicle -- IBSVDD Incremental boundary support vector data description -- SNR Signal to noise ratio -- WPD Wavelet packet decomposition -- PCA Principal component analysis -- CCR Cumulative contribution rate -- OCC One class classification -- KKT Karush-Kuhn-Tucker -- ISVDD Incremental support vector data description -- NISVDD Non-support-vector-based incremental support vector data description; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2020.04.096
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: The blade is a crucial part of wind turbine for generating electricity and prone to damage due to harsh external environment. Accurate damage detection of wind turbine blade (WTB) is still a prominent challenge. This paper presents an acoustical detection method for damage identification of the WTB based on pattern recognition. In the proposed method, sound pulse extraction of the WTB is first investigated through physical method in combination with the filter and sliding window. Subsequently, the wavelet packet energy ratios of acoustic signal are introduced to characterize the discrepancy between intact and cracked sound pulses, and the support vector data description (SVDD) model is built for WTB damage detection. Besides, an improved incremental learning method is presented and employed to adaptively update the SVDD model, which aims at simplifying calculation procedure. Finally, the performance of proposed method is evaluated using experimental data collected from the WTBs with both intact and damaged conditions in commercial wind farms. It is demonstrated that proposed method has improvement in prediction accuracy compared to previous incremental SVDD models and performs the best on training time. Highlights: An acoustical damage detection method of the wind turbine blade is proposed. Sound pulse extraction method is given with physical and signal processing techniques. An incremental SVDD method is presented for damage detection of wind turbine blade. Experiments validate the effectiveness of proposed method in detecting the blade.