Data-driven anomaly detection in high-voltage transformer bushings with LSTM auto-encoder

Imene Mitiche*, Tony McGrail, Philip Boreham, Alan Nesbitt, Gordon Morison

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The reliability and health of bushings in high-voltage (HV) power transformers is essential in the power supply industry, as any unexpected failure can cause power outage leading to heavy financial losses. The challenge is to identify the point at which insulation deterioration puts the bushing at an unacceptable risk of failure. By monitoring relevant measurements we can trace any change that occurs and may indicate an anomaly in the equipment’s condition. In this work we propose a machine-learning-based method for real-time anomaly detection in current magnitude and phase angle from three bushing taps. The proposed method is fast, self-supervised and flexible. It consists of a Long Short-Term Memory Auto-Encoder (LSTMAE) network which learns the normal current and phase measurements of the bushing and detects any point when these measurements change based on the Mean Absolute Error (MAE) metric evaluation. This approach was successfully evaluated using real-world data measured from HV transformer bushings where anomalous events have been identified.
Original languageEnglish
JournalSensors
Volume21
Issue number21
DOIs
Publication statusPublished - 8 Nov 2021

Keywords

  • transformer bushings; insulation failure; anomaly detection; LSTM; auto-encoder

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