Fault Detection for Covered Conductors With High-Frequency Voltage Signals: From Local Patterns to Global Features

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F20%3A10245525" target="_blank" >RIV/61989100:27730/20:10245525 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9233447" target="_blank" >https://ieeexplore.ieee.org/document/9233447</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSG.2020.3032527" target="_blank" >10.1109/TSG.2020.3032527</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fault Detection for Covered Conductors With High-Frequency Voltage Signals: From Local Patterns to Global Features

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Fault Detection for Covered Conductors With High-Frequency Voltage Signals: From Local Patterns to Global Features

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/ED2.1.00%2F19.0389" target="_blank" >ED2.1.00/19.0389: Rozvoj výzkumné infrastruktury Centra ENET</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Smart Grid

ISSN

1949-3053

e-ISSN

—

Svazek periodika

1

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

1

Kód UT WoS článku

999

EID výsledku v databázi Scopus

2-s2.0-85101956783