Health assessment and fault diagnosis of substation equipment based on digital twin

Abstract

The equipment safety of substation is related to the normal operation of substation and even distribution network. In this paper, the condition monitoring and fault diagnosis of key equipment in substation are realized through the introduction of artificial intelligence algorithm and random matrix theory. A large number of PRPD spectrums are obtained by point tracing simulation of the measured PRPD spectra of switchgear. The characteristics of the spectra are designed and extracted to train the PRPD pattern recognition modelling and the accuracy of the test set of the model is 97%. Based on the random matrix theory, whether the oil chromatogram data generated by transformer operation is abnormal is monitored on-line. Through big data analysis, the equipment status is accurately evaluated and the type, position and severity of defects are accurately evaluated. The health assessment and fault diagnosis of substation equipment based on digital twin improves the intelligence and efficiency of substation equipment monitoring.