Real-Time Automatic Anomaly Detection Approach Designed for Electrified Railway Power System

Huiqiao Ren, Fulin Zhou, Katsuki Fujisawa

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

An automatic and intelligent abnormal electrical process detection scheme is crucial for protecting the stability and power quality of an electrical power system and further, the operation of the future grid. This paper introduces the automatic monitoring system for electrified railway power system and designs a framework based on the convolution neural network for abnormal electrical process detection, integrating the data processing, feature extraction, and classification into one model. Then inception blocks are introduced as a kernel-wise approach to boost the performance. The data from the railway electrification system is applied to this scheme and receives a high performance of 97% abnormal electrical process recognition rate.

本文言語英語
ホスト出版物のタイトル2021 7th International Conference on Mechatronics and Robotics Engineering, ICMRE 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ116-120
ページ数5
ISBN(電子版)9780738132051
DOI
出版ステータス出版済み - 2 3 2021
イベント7th International Conference on Mechatronics and Robotics Engineering, ICMRE 2021 - Virtual, Budapest, ハンガリー
継続期間: 2 3 20212 5 2021

出版物シリーズ

名前2021 7th International Conference on Mechatronics and Robotics Engineering, ICMRE 2021

会議

会議7th International Conference on Mechatronics and Robotics Engineering, ICMRE 2021
国/地域ハンガリー
CityVirtual, Budapest
Period2/3/212/5/21

All Science Journal Classification (ASJC) codes

  • 人工知能
  • 制御およびシステム工学
  • 電子工学および電気工学
  • 機械工学
  • 制御と最適化

フィンガープリント

「Real-Time Automatic Anomaly Detection Approach Designed for Electrified Railway Power System」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル