An accurate false data detection in smart grid based on residual recurrent neural network and adaptive threshold

Yufeng Wang, Wanjiao Shi, Qun Jin, Jianhua Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Smart grids are vulnerable to cyber-attacks, which can cause significant damage and huge economic losses. Generally, state estimation (SE) is used to observe the operation of the grid. State estimation of the grid is vulnerable to false data injection attack (FDIA), so diagnosing this type of malicious attack has a major impact on ensuring reliable operation of the power system. In this paper, we present an effective FDIA detection method based on residual recurrent neural network (R2N2) prediction model and adaptive judgment threshold. Specifically, considering the data contains both linear and nonlinear components, the R2N2 model divides the prediction process into two parts: The first part uses the linear model to fit the state data; the second part predicts the nonlinearity of the residuals of the linear prediction model. The adaptive judgment threshold is inferred through fitting the Weibull distribution with the sum of squared errors between the predicted values and observed values. The thorough simulation results demonstrate that our scheme performs better than other prediction based FDIA detection schemes.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Energy Internet, ICEI 2019
EditorsGuokai Wu, Jiye Wang, Qinliang Tan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages499-504
Number of pages6
ISBN (Electronic)9781728114934
DOIs
Publication statusPublished - 2019 May
Event3rd IEEE International Conference on Energy Internet, ICEI 2019 - Nanjing, China
Duration: 2019 May 272019 May 31

Publication series

NameProceedings - IEEE International Conference on Energy Internet, ICEI 2019

Conference

Conference3rd IEEE International Conference on Energy Internet, ICEI 2019
CountryChina
CityNanjing
Period19/5/2719/5/31

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Keywords

  • Adaptive detection threshold
  • False data injection attack
  • Residual recurrent neural network
  • State estimation

ASJC Scopus subject areas

  • Control and Optimization
  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

Cite this

Wang, Y., Shi, W., Jin, Q., & Ma, J. (2019). An accurate false data detection in smart grid based on residual recurrent neural network and adaptive threshold. In G. Wu, J. Wang, & Q. Tan (Eds.), Proceedings - IEEE International Conference on Energy Internet, ICEI 2019 (pp. 499-504). [8791377] (Proceedings - IEEE International Conference on Energy Internet, ICEI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEI.2019.00094