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Nowadays, aerial covered conductors (CC) are increasingly used in many placesof the world due to their higher operational reliability, reduced constructionspace and effective protection for wildlife animals. In spite of theseadvantages, a major challenge of using CC is that the ordinary upstreamprotection devices are not able to detect the phase-to-ground faults and thefrequent tree/tree branch hitting conductor events on such conductors. This isbecause these events only lead to partial discharge (PD) activities rather thanovercurrent signatures typically seen on bare conductors. To solve thisproblem, in recent years, ENET Center in Czech Republic (ENET) devised a simplemeter to measure the voltage signal of the electric stray field along CC,aiming to detect the above hazardous PD activities. In 2018, ENET shared alarge amount of waveform data recorded by their meter on Kaggle, the world'slargest data science collaboration platform, encouraging worldwide experts todevelop an effective pattern recognition method for the acquired signals. Inresponse, we developed a unique method based on time-series decomposition andLong Short-Term Memory Network (LSTM) in addition to unique feature engineeringprocess to recognize PD activities on CC. The proposed method is tested on theENET public dataset and compared to various traditional classification methods.It demonstrated superior performance and great practicality.

Partial Discharge Detection on Aerial Covered Conductors Using Time-Series Decomposition and Long Short-term Memory Network