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

Huiqiao Ren, Fulin Zhou, Katsuki Fujisawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2021 7th International Conference on Mechatronics and Robotics Engineering, ICMRE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages116-120
Number of pages5
ISBN (Electronic)9780738132051
DOIs
Publication statusPublished - Feb 3 2021
Event7th International Conference on Mechatronics and Robotics Engineering, ICMRE 2021 - Virtual, Budapest, Hungary
Duration: Feb 3 2021Feb 5 2021

Publication series

Name2021 7th International Conference on Mechatronics and Robotics Engineering, ICMRE 2021

Conference

Conference7th International Conference on Mechatronics and Robotics Engineering, ICMRE 2021
Country/TerritoryHungary
CityVirtual, Budapest
Period2/3/212/5/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Control and Optimization

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