基于高频电压信号的覆盖导线故障检测：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
摘要：
局部放电（PD）的检测和表征是架空线路绝缘诊断的关键。随着包含数千个自然获得的高频电压信号的大型数据集的发布，以前所未有的规模对故障相关PD模式进行数据驱动分析变得可行。PD模式和背景噪声干扰的高度多样性促使我们设计一种基于聚类技术的创新脉冲形状表征方法，该方法可以动态地识别一组代表性的PD相关脉冲。利用这些脉冲作为参考模式，我们构建了有见地的功能，并开发了一种新的机器学习模型，具有卓越的早期覆盖导体故障的检测性能。所提出的模型在Kaggle竞赛中优于获胜模型，并提供了最先进的解决方案来检测现场的实时干扰。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
