  Nowadays, aerial covered conductors (CC) are increasingly used in many places
of the world due to their higher operational reliability, reduced construction
space and effective protection for wildlife animals. In spite of these
advantages, a major challenge of using CC is that the ordinary upstream
protection devices are not able to detect the phase-to-ground faults and the
frequent tree/tree branch hitting conductor events on such conductors. This is
because these events only lead to partial discharge (PD) activities rather than
overcurrent signatures typically seen on bare conductors. To solve this
problem, in recent years, ENET Center in Czech Republic (ENET) devised a simple
meter to measure the voltage signal of the electric stray field along CC,
aiming to detect the above hazardous PD activities. In 2018, ENET shared a
large amount of waveform data recorded by their meter on Kaggle, the world's
largest data science collaboration platform, encouraging worldwide experts to
develop an effective pattern recognition method for the acquired signals. In
response, we developed a unique method based on time-series decomposition and
Long Short-Term Memory Network (LSTM) in addition to unique feature engineering
process to recognize PD activities on CC. The proposed method is tested on the
ENET public dataset and compared to various traditional classification methods.
It demonstrated superior performance and great practicality.
