论文标题
带有高频电压信号的覆盖导体的故障检测：从局部模式到全局特征
Fault Detection for Covered Conductors With High-Frequency Voltage Signals: From Local Patterns to Global Features
论文作者
论文摘要
部分放电（PD）的检测和表征对于用覆盖导体的架空线诊断至关重要。随着一个大型数据集的释放，其中包含数千个自然获得的高频信号，在前所未有的量表上，数据驱动的与故障相关的PD模式的分析变得可行。 PD模式和背景噪声的高度多样性促使我们根据聚类技术设计创新的脉冲形状表征方法，该方法可以动态地识别一组代表性的PD相关脉冲。利用这些脉冲为参考模式，我们构建了有见地的特征，并开发了一种新型的机器学习模型，具有出色的检测性能，可用于早期覆盖的导体故障。提出的模型在Kaggle竞争中优于获胜模型，并提供了最先进的解决方案来检测该领域的实时干扰。
The detection and characterization of partial discharge (PD) are crucial for the insulation diagnosis of overhead lines with covered conductors. With the release of a large dataset containing thousands of naturally obtained high-frequency voltage signals, data-driven analysis of fault-related PD patterns on an unprecedented scale becomes viable. The high diversity of PD patterns and background noise interferences motivates us to design an innovative pulse shape characterization method based on clustering techniques, which can dynamically identify a set of representative PD-related pulses. Capitalizing on those pulses as referential patterns, we construct insightful features and develop a novel machine learning model with a superior detection performance for early-stage covered conductor faults. The presented model outperforms the winning model in a Kaggle competition and provides the state-of-the-art solution to detect real-time disturbances in the field.
