Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
Ofinno, Reston, VA 20190, USA.
Sensors (Basel). 2022 Jul 16;22(14):5320. doi: 10.3390/s22145320.
Smart electrical grids rely on data communication to support their operation and on sensing for diagnostics and maintenance. Usually, the roles of communication and sensing equipment are different, i.e., communication equipment does not participate in sensing tasks and vice versa. Power line communication (PLC) offers a cost-effective solution for communication and sensing for smart grids. This is because the high-frequency PLC signals used for data communication also reveal detailed information regarding the health of the power lines that they travel through. Traditional PLC-based power line or cable diagnostic solutions are dependent on prior knowledge of the cable type, network topology, and/or characteristics of the anomalies. In this paper, we develop a power line sensing technique that can detect various types of cable anomalies without any prior domain knowledge. To this end, we design a solution that first uses time-series forecasting to predict the PLC channel state information at any given point in time based on its historical data. Under the approximation that the prediction error follows a Gaussian distribution, we then perform chi-squared statistical test to build an anomaly detector which identifies the occurrence of a cable fault. We demonstrate the effectiveness and universality of our sensing solution via evaluations conducted using both synthetic and real-world data extracted from low- and medium-voltage distribution networks.
智能电网依赖于数据通信来支持其运行，并依赖于传感来进行诊断和维护。通常，通信和传感设备的作用是不同的，即通信设备不参与传感任务，反之亦然。电力线通信（PLC）为智能电网的通信和传感提供了一种具有成本效益的解决方案。这是因为用于数据通信的高频 PLC 信号还揭示了它们所经过的电力线健康状况的详细信息。基于传统 PLC 的电力线或电缆诊断解决方案依赖于对电缆类型、网络拓扑和/或异常特征的先验知识。在本文中，我们开发了一种无需任何先验领域知识即可检测各种类型电缆异常的电力线传感技术。为此，我们设计了一种解决方案，该方案首先使用时间序列预测根据其历史数据在任何给定时间点预测 PLC 信道状态信息。在预测误差遵循高斯分布的近似下，我们然后执行卡方统计检验来构建一个异常检测器，该检测器识别电缆故障的发生。我们通过使用从低电压和中压配电网中提取的合成和真实世界数据进行评估，证明了我们的传感解决方案的有效性和通用性。
