A novel detection algorithm to identify false data injection attacks on power system state estimation

Mehdi Ganjkhani, Seyedeh Narjes Fallah, Sobhan Badakhshan, Shahaboddin Shamshirband, Kwok wing Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)

Abstract

This paper provides a novel bad data detection processor to identify false data injection attacks (FDIAs) on the power system state estimation. The attackers are able to alter the result of the state estimation virtually intending to change the result of the state estimation without being detected by the bad data processors. However, using a specific configuration of an artificial neural network (ANN), named nonlinear autoregressive exogenous (NARX), can help to identify the injected bad data in state estimation. Considering the high correlation between power system measurements as well as state variables, the proposed neural network-based approach is feasible to detect any potential FDIAs. Two different strategies of FDIAs have been simulated in power system state estimation using IEEE standard 14-bus test system for evaluating the performance of the proposed method. The results indicate that the proposed bad data detection processor is able to detect the false injected data launched into the system accurately.

Original languageEnglish
Article number2209
JournalEnergies
Volume12
Issue number11
DOIs
Publication statusPublished - 11 Jun 2019

Keywords

  • Artificial neural network (ANN)
  • False data injection attack (FDIA)
  • Nonlinear autoregressive exogenous (NARX) bad data detection
  • State estimation

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

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