Power grid estimation using electric network frequency signals

Woorim Bang, Ji Won Yoon

Research output: Contribution to journalArticle

Abstract

The electric network frequency (ENF) has a statistical uniqueness according to time and location. The ENF signal is always slightly fluctuating for the load balance of the power grid around the fundamental frequency. The ENF signals can be obtained from the power line using a frequency disturbance recorder (FDR). The ENF signal can also be extracted from video files or audio files because the ENF signal is also saved due to the influence of the electromagnetic field when video files or audio files are recorded. In this paper, we propose a method to find power grid from ENF signals collected from various time and area. We analyzed ENF signals from the distribution level of the power system and online uploaded video files. Moreover, a hybrid feature extraction approach, which employs several features, is proposed to infer the location of the signal belongs regardless of the time that the signal was collected. Employing our suggested feature extraction methods, the signal which extracted from the power line can be classified 95.21% and 99.07% correctly when ENF signals have 480 and 1920 data points, respectively. In the case of ENF signals extracted from multimedia, the accuracy varies greatly according to the recorded environment such as network status and microphone quality. When constructing a feature vector from 120 data points of ENF signals, we could identify the power grid had an average of 94.17% accuracy from multimedia.

Original languageEnglish
Article number1982168
JournalSecurity and Communication Networks
Volume2019
DOIs
Publication statusPublished - 2019 Jan 1

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Circuit theory
Feature extraction
Microphones
Electromagnetic fields

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications

Cite this

Power grid estimation using electric network frequency signals. / Bang, Woorim; Yoon, Ji Won.

In: Security and Communication Networks, Vol. 2019, 1982168, 01.01.2019.

Research output: Contribution to journalArticle

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