  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.

Signal Processing;Machine Learning