Extreme Learning Machine-Based State Reconstruction for Automatic Attack Filtering in Cyber Physical Power System

Ting Wu, Wenli Xue, Huaizhi Wang, C. Y. Chung, Guibin Wang, Jianchun Peng, Qiang Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

43 Citations (Scopus)


Successful detection of false data injection attacks (FDIAs) and removal of state bias due to FDIAs are essential for ensuring secure power grids operation and control. This article first extends the approximate dc model of FDIA to a more general ac model that can handle both traditional and synchronized measurements. To automatically filter out the established FDIAs, we propose a state reconstruction scheme consisting of a contaminated state separation method, an enhanced bad data identification approach and a state recovery algorithm. In this scheme, a classifier is developed by aggregating a series of extreme learning machines (ELMs) to detect anomaly states caused by FDIAs. Gaussian random distribution and Latin hypercube sampling are adopted to initialize the input weights of base ELMs, which can provide more diversities to enhance the ensemble performance. Then, to identify the exact locations of the compromised measurements, a state forecasting-based bad data identification approach is proposed by exploiting the consistency between the forecasted and the received measurements. Finally, an effective state recovery algorithm applies quasi-Newton method and Armijo line search to address the possible system unobservable problem due to the removal of attacked measurements. Numerical tests on serval IEEE standard test systems verify the efficiency of the proposed FDIA model and state reconstruction scheme.

Original languageEnglish
Article number9055170
Pages (from-to)1892-1904
Number of pages13
JournalIEEE Transactions on Industrial Informatics
Issue number3
Publication statusPublished - Mar 2021
Externally publishedYes


  • Cyber-physical power system
  • extreme learning machine
  • false data injection attack (FDIA)
  • quasi-Newton method
  • state estimation (SE)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


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