Improving Diagnostic Performance of High-Voltage Circuit Breakers on Imbalanced Data Using an Oversampling Method

In recent years, applications of artificial intelligence (AI) techniques in fault diagnosis of high-voltage circuit breakers (HVCBs) have gained wide attention. In real applications, how-ever, HVCBs work in the normal state most of the time. Therefore, the problem of imbalanced monitoring data is pr...

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Bibliographic Details
Published inIEEE transactions on power delivery Vol. 37; no. 4; pp. 2704 - 2716
Main Authors Chen, Lei, Wan, Shuting, Dou, Longjiang
Format Journal Article
LanguageEnglish
Published New York IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptive sampling
Artificial intelligence
Circuit breakers
Circuit faults
Fault diagnosis
High voltages
imbalanced data
Monitoring
Noise measurement
Oversampling
Training
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Summary:In recent years, applications of artificial intelligence (AI) techniques in fault diagnosis of high-voltage circuit breakers (HVCBs) have gained wide attention. In real applications, how-ever, HVCBs work in the normal state most of the time. Therefore, the problem of imbalanced monitoring data is prevalent, which threatens the generalization capability of AI-based diagnosis methods, resulting in poor fault diagnostic performance. To address this problem, an oversampling method called Density-weighted Minority Oversampling (DWMO) was proposed to balance monitoring data in this study. Experimental results on HVCB monitoring data with various imbalance ratios show that DWMO can improve the diagnostic performance of AI techniques and outperformed other commonly used oversampling methods including Synthetic Minority Oversampling Technique (SMOTE), Borderline-SMOTE, Adaptive Synthetic Sampling Approach, and Majority Weighted Minority Oversampling Technique.
ISSN:0885-8977
1937-4208
DOI:10.1109/TPWRD.2021.3114547