论文标题
高阻抗故障检测的物理信息学习
Physics-Informed Learning for High Impedance Faults Detection
论文作者
论文摘要
分配网格中的高阻抗断层（HIF）可能会导致野火并威胁人类生命。变电站的常规保护继电器无法检测到超过10 \％的HIF，因为过度流动较低，而HIF的签名是局部的。分发系统中安装了更多$μ$ PMU，高分辨率$ $ $ $ PMU数据集提供了从多个点检测HIF的机会。尽管如此，应用$μ$ PMU数据集的主要障碍是缺乏标签。为了解决这个问题，我们构建了一个具有物理信息的卷积自动编码器（PICAE），以检测HIF，而无需标记HIF进行培训。 PICAE的重要性是一个物理正则化，它源自电压特性的椭圆形轨迹，即使在高度嘈杂的情况下，也可以将HIF与其他异常事件区分开。我们制定了一个全系统检测框架，该框架合并了多个节点的局部检测结果，以提高检测准确性和可靠性。提出的方法在通过PSCAD/EMTDC模拟的IEEE 34节点测试馈线中进行了验证。我们的Picae在各种情况下都优于现有作品，并且对不同的可观察性和噪音非常强大。
High impedance faults (HIFs) in distribution grids may cause wildfires and threaten human lives. Conventional protection relays at substations fail to detect more than 10\% HIFs since over-currents are low and the signatures of HIFs are local. With more $μ$PMU being installed in the distribution system, high-resolution $μ$PMU datasets provide the opportunity of detecting HIFs from multiple points. Still, the main obstacle in applying the $μ$PMU datasets is the lack of labels. To address this issue, we construct a physics-informed convolutional auto-encoder (PICAE) to detect HIFs without labeled HIFs for training. The significance of our PICAE is a physical regularization, derived from the elliptical trajectory of voltages-current characteristics, to distinguish HIFs from other abnormal events even in highly noisy situations. We formulate a system-wide detection framework that merges multiple nodes' local detection results to improve the detection accuracy and reliability. The proposed approaches are validated in the IEEE 34-node test feeder simulated through PSCAD/EMTDC. Our PICAE outperforms the existing works in various scenarios and is robust to different observability and noise.
