Acoustic emission (AE) technology can predict the occurrence of partial discharge (PD) faults, which is used to improve the safe operation level of gas-insulated switchgear (GIS) equipment. However, the strong noise interference from the production site is still the main factor affecting the identification accuracy. In this study, a simplified model is designed to approximate the accumulation of free metal particles on the surface of the GIS internal insulation structure, and white noise of various intensities is added to the collected PD-induced AE signals to simulate the background interference. The results prove that the proposed denoising method can achieve a better denoising effect in various signal-to-noise ratio (SNR) conditions. In particular, in the case of low SNR, the recognition accuracy of the accumulation degree of metal particles has been improved by more than 15%.

Gas-insulated switchgear (GIS) is widely used in high-voltage power transmission systems. There has also been increasing demand for the real-time and online detection of faults in GIS equipment. In this study, a new type of optical fiber acoustic emission (AE) sensor based on the photoelastic effect and the polarization modulation method is proposed and fabricated. Partial discharge (PD)-induced AE signals of different defects were collected by this sensor and used for back-propagation artificial neural network (BP-ANN) training and recognition after data preprocessing and feature extraction. The results of the research show that a BP-ANN with selfadaptation and self-learning combined with the proposed sensor has good performance in the recognition and prediction of PD faults in GIS equipment, and the average accuracy of the test set reached 93.7%. The detection technology for weak AE signals and the fault identification method reported in this study can provide a reference for online monitoring of GIS and other equipment, which will have appreciable economic value and social significance.

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