Experimental study on vibration diagnosis of insulation faults in gas-insulated metal-enclosed switchgear

As the core component of the power system, gas-insulated metal-enclosed switchgear (GIS) plays a crucial role in ensuring the safe and stable operation of the power system, so the fault diagnosis research of GIS has significant practical significance. At present, GIS fault diagnosis often adopts the insulation diagnosis method based on acousto-optic signals, but due to the complexity of the GIS operating environment, the ultra-high frequency signals in the environment will cause interference to the traditional partial discharge detection device. In order to solve the above problems, this paper builds a GIS fault diagnosis platform, effectively extracts the vibration signals under the fault condition through the vibration signal analysis method, and combines the short-time Fourier transform and convolutional neural network to diagnose the faults of the GIS by using the end-to-end fault pattern recognition model. The results show that the model has good accuracy and practicability for GIS fault diagnosis, and provides a new idea for the fault diagnosis of GIS equipment.