High-Voltage Circuit Breaker Fault Diagnosis Using a Hybrid Feature Transformation Approach Based on Random Forest and Stacked Autoencoder

In recent years, machine learning techniques have been applied to test the fault type in high-voltage circuit breakers (HVCBs). Most related research involves in improving the classification method for higher precision. Nevertheless, as an important part of the diagnosis, the feature information des...

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Bibliographic Details
Published in:	IEEE transactions on industrial electronics (1982) Vol. 66; no. 12; pp. 9777 - 9788
Main Authors:	Ma, Suliang, Chen, Mingxuan, Wu, Jianwen, Wang, Yuhao, Jia, Bowen, Jiang, Yuan
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.12.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Binary codes Circuit breakers Circuit faults Fault diagnosis Feature extraction feature transformation High voltages high-voltage circuit breaker (HVCB) Machine learning Mapping Neural networks Optimization Radio frequency random forest (RF) stacked autoencoder (SAE) Support vector machines Transformations Vibrations Wavelet analysis wavelet packet decomposition Wavelet transforms
ISSN:	0278-0046, 1557-9948
Online Access:	Get full text
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