High-voltage circuit breaker fault voiceprint recognition based on prototype similar domain adaptive spectral morphological neural network
1
State Grid Shanghai Ultra High Voltage Company, Shanghai 200122, China
Submission date: 2025-02-08
Final revision date: 2025-05-29
Acceptance date: 2025-06-09
Online publication date: 2025-07-02
Publication date: 2025-07-02
Corresponding author
KEYWORDS
Deep transfer learningHigh-voltage circuit breakerVoiceprint recognitionPrototype similarity domainSpectral morphology variational mode decomposition
TOPICS
ABSTRACT
High-voltage circuit breakers will emit continuous vibration signals during operation. The signals contain a large number of pulses and fluctuations caused by faults. They are the main data source for evaluating the functioning condition for high-tension breaker switches. To aim for examine its vocal features in vibration acoustic signals of high-tension breaker switches in different operating states, a prototype similarity domain adaptive spectral morphological neural network (PSDA-SMNN) was proposed. Through internal and external loop training, the difference in feature distribution between domains is reduced, and unlabeled faults of high-tension breaker switches in varying operational states are recognized. Test outcomes indicate which the suggested framework is able to precisely identify the malfunction operational condition for high-tension breaker switches and detect common issues for high-tension breaker switches under various interference settings, having a classification precision of approximately 95%.
FUNDING
This study was funded by the Science and Technology Project of State Grid Shanghai Municipal Electric Power Company (520950240009).
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| eISSN: | 2449-5220 |
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