Fault Detection for Covered Conductors With High-Frequency Voltage Signals: From Local Patterns to Global Features
This provides a solution for insulation diagnosis in overhead power lines, addressing a domain-specific problem with incremental improvements in detection accuracy.
The paper tackled the problem of detecting early-stage faults in covered conductors by analyzing partial discharge patterns from high-frequency voltage signals, achieving state-of-the-art performance that outperformed a Kaggle competition winner.
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.