Fault Diagnosis of High-Voltage Circuit Breakers Using Hilbert-Huang Transform and Denoising-Stacked Autoencoder

As the main protection and control equipment of the power system, the high-voltage circuit breaker are required to be disconnected instantaneously within a few milliseconds. Once it fails, it will seriously threaten the safety of the power grid. In this paper, a new high-voltage circuit breaker fault diagnosis algorithm based on denoising-stacked autoencoder is proposed. Firstly, the acceleration sensor is used to collect the vibration signal of the high voltage circuit breaker. The high voltage circuit breaker fault signal data are collected during equipment failure in the laboratory simulation experiment and site field operation. This non-stationary random vibration signal is then denoised and processed using the Hilbert-Huang transform. Since the on-site vibration signal is derived from data from different voltage levels and equipment manufacturers, it is necessary to clean the data firstly. Finally, the denoising-stacked autoencoder is used to perform automatic feature extraction and pattern recognition classification on the preprocessed data. Automatic feature extraction reduces the dependence of traditional artificial feature engineering on expert knowledge as much as possible, and makes full use of fault features, thus improving the accuracy of diagnosis and the generalization ability of the model.