Generative adversarial network-based scheme for diagnosing faults in cyber-physical power systems

Hossein Hassani, Roozbeh Razavi-Far, Mehrdad Saif, Vasile Palade

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
23 Downloads (Pure)

Abstract

This paper presents a novel diagnostic framework for distributed power systems that is based on using generative adversarial networks for generating artificial knockoffs in the power grid. The proposed framework makes use of the raw data measurements including voltage, frequency, and phase-angle that are collected from each bus in the cyber-physical power systems. The collected measurements are firstly fed into a feature selection module, where multiple state-of-the-art techniques have been used to extract the most informative features from the initial set of available features. The selected features are inputs to a knockoff generation module, where the generative adversarial networks are employed to generate the corresponding knockoffs of the selected features. The generated knockoffs are then fed into a classification module, in which two different classification models are used for the sake of fault diagnosis. Multiple experiments have been designed to investigate the effect of noise, fault resistance value, and sampling rate on the performance of the proposed framework. The effectiveness of the proposed framework is validated through a comprehensive study on the IEEE 118-bus system.

Original languageEnglish
Article number5173
JournalSensors
Volume21
Issue number15
DOIs
Publication statusPublished - 30 Jul 2021

Bibliographical note

Funding Information:
Acknowledgments: This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Cyber-physical power systems
  • Fault diagnosis
  • Feature selection
  • Generative adversarial networks

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Generative adversarial network-based scheme for diagnosing faults in cyber-physical power systems'. Together they form a unique fingerprint.

Cite this