Anomaly Detection of Gas-Insulated Switchgear (GIS) Equipment Based on Embedding Position Variational Auto-Encoder

Abstract

Abstract Gas-insulated switchgear (GIS) is one of the most important power devices in the power system. Once a fault occurs, it is difficult to determine and locate the fault due to its fully enclosed nature, which may lead to greater accidents and huge losses. Many GIS anomaly detection methods based on vibration and deep learning (DL) have been developed in recent years. However, these DL-based methods only utilize one single sampling point data on GIS, while not all faults are reflected at the points currently in use due to the signal attenuation. In this paper, an embedded position variational auto-encoder (EP-VAE) model for anomaly detection of GIS devices is presented, which can model multipoint vibration signals. We fuse the position information and the corresponding one dimension vibration signal to the two dimension grayscale images by Gram matrix. Then, the normal grayscale images are feed into the EP-VAE model, which is optimized by KL divergence and reconstruction error jointly. In the test phase, when one of these two losses is higher than the normal images, it is identified as an abnormal fault. We use the vibration data from the actual operation of GIS to carry out the anomaly detection experiments. The results show that our model EP-VAE has a detection accuracy of 93.12% for normal vibration grayscale images, and an abnormality detection accuracy of 87.94%.