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In this article, we propose a new anomaly detection method to detect the partial discharge in a gas-insulated switchgear. An autoencoder was used for anomaly detection and was modeled on the one-class classification problem. Based on the one-class classification scenario, in which the training data exploited the noise data only, the proposed autoencoder learned the low-dimensional latent information from the high-dimensional space of the input signal. Then, the reconstruction error was used as a fault indicator, and the threshold was determined using the partial discharge data. The performance of the proposed AE was verified by on-site noise and PRPD experiments, using an online UHF PD monitoring system in the real-world environment. The results showed that the proposed autoencoder not only achieved 86.75% detection performance for the on-site noise and partial discharge data in gas-insulated switchgears but also allowed better detection performance than the one-class support vector machine learning procedure by 40.5%.

Anomaly Detection for Partial Discharge in Gas-Insulated Switchgears Using Autoencoder