Multiscale Wavelet-Driven Graph Convolutional Network for Blade Icing Detection of Wind Turbines
Author:
Affiliation:
1. Department of Manufacturing and Civil Engineering, Norwegian University of Science and Technology (NTNU), Gjøvik, Norway
2. Department of Technology, Management and Economics, Technical University of Denmark, Kongens Lyngby, Denmark
Funder
Chinese Scholarship Council
NTNU
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/9946942/09913320.pdf?arnumber=9913320
Reference63 articles.
1. Wind turbine performance under icing conditions
2. A Review of the Effects of Ice Accretion on the Structural Behavior of Wind Turbines
3. WaveletAE: A wavelet-enhanced autoencoder for wind turbine blade icing detection;yuan;arXiv 1902 05625,2019
4. Intelligent wind turbine blade icing detection using supervisory control and data acquisition data and ensemble deep learning
5. Learning deep representation of imbalanced SCADA data for fault detection of wind turbines
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