Mitiche Imene, McGrail Tony, Boreham Philip, Nesbitt Alan, Morison Gordon
Department of Computing, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK.
Doble Engineering, Bere Regis BH20 7LA, UK.
Sensors (Basel). 2021 Nov 8;21(21):7426. doi: 10.3390/s21217426.
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.
高压（HV）电力变压器中套管的可靠性和健康状况在供电行业至关重要，因为任何意外故障都可能导致停电，从而造成重大经济损失。挑战在于确定绝缘劣化使套管面临不可接受的故障风险的点。通过监测相关测量值，我们可以追踪发生的任何变化，并可能表明设备状况异常。在这项工作中，我们提出了一种基于机器学习的方法，用于从三个套管抽头实时检测电流幅值和相位角中的异常。所提出的方法快速、自监督且灵活。它由一个长短期记忆自动编码器（LSTMAE）网络组成，该网络学习套管的正常电流和相位测量值，并根据平均绝对误差（MAE）指标评估检测这些测量值发生变化的任何点。使用从已识别出异常事件的高压变压器套管测量的实际数据成功评估了该方法。
