Toward Statistical Real-Time Power Fault Detection

Mantautas Rimkus, Piotr Kokoszka, Kumaraguru Prabakar, Haonan Wang

Research output: Contribution to journalArticlepeer-review

Abstract

We propose statistical fault detection methodology based on high-frequency data streams that are becoming available in modern power grids. Our approach can be treated as an online (sequential) change point monitoring methodology. However, due to the mostly unexplored and very nonstandard structure of high-frequency power grid streaming data, substantial new statistical development is required to make this methodology practically applicable. The paper includes development of scalar detectors based on multichannel data streams, determination of data-driven alarm thresholds and investigation of the performance and robustness of the new tools. Due to a reasonably large database of faults, we can calculate frequencies of false and correct fault signals, and recommend implementations that optimize these empirical success rates.

Original languageAmerican English
Pages (from-to)196-217
Number of pages22
JournalCommunications in Statistics Case Studies Data Analysis and Applications
Volume9
Issue number2
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 Taylor & Francis Group, LLC.

NREL Publication Number

  • NREL/JA-5D00-85788

Keywords

  • High-frequency streaming data
  • power grid faults
  • sequential change point detection

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