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Accurate fault diagnosis of high-voltage circuit breakers is crucial for the safety of power grids. Mechanism action time, though can reflect the state of high-voltage circuit breakers, is usually difficult to obtain, as multiple sensors are required for acquisition. To solve this problem, a novel method that can extract mechanism action time from vibration signals was proposed in this paper. The method involved enhancement of vibration signals using short-time zero-crossing rate and determination of mechanism action time by the double threshold method. Then, time parameters were utilized to calculate singular spectrum entropy as the input vector for a classifier. Finally, a hybrid classifier that involved support vector data description and extreme learning machine was developed to identify faults. The hybrid classifier can not only classify known mechanical states but also detect unknown mechanical faults of high-voltage circuit breakers. The effectiveness of the proposed method was verified using a 35-kV high-voltage circuit breaker.

Fault Diagnosis of High-Voltage Circuit Breakers Using Mechanism Action Time and Hybrid Classifier