A Hybrid Method for False Data Injection Attack Detection in Smart Grid Based on Variational Mode Decomposition and OS-ELM

Chunxia Dou; Di Wu; Dong Yue; Bao Jin; Shiyun Xu

doi:10.17775/CSEEJPES.2019.00670

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A Hybrid Method for False Data Injection Attack Detection in Smart Grid Based on Variational Mode Decomposition and OS-ELM

Regular Papers | 更新时间：2026-10-15

- A Hybrid Method for False Data Injection Attack Detection in Smart Grid Based on Variational Mode Decomposition and OS-ELM
- A Hybrid Method for False Data Injection Attack Detection in Smart Grid Based on Variational Mode Decomposition and OS-ELM
- 中国电机工程学会电力与能源系统学报(英文) 2022年8卷第6期页码：1697-1707
- 作者机构：
  
  1. Institude of Engineering, Yanshan University,Qinhuangdao,China
  2. Institute of Advanced Technology, Nanjing University of Posts and Telecommunications,Nanjing,China
  3. Institute of Avanced Technology, Nanjing University of Posts and Telecommunications,Nanjing,China
  4. China Electrical Power Research Institute,Beijing,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2019.00670
  中图分类号：
- 纸质出版：2022-11-01
- 稿件说明：
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Chunxia Dou, Di Wu, Dong Yue, 等. A Hybrid Method for False Data Injection Attack Detection in Smart Grid Based on Variational Mode Decomposition and OS-ELM[J]. 中国电机工程学会电力与能源系统学报(英文), 2022,8(6):1697-1707.

Chunxia Dou, Di Wu, Dong Yue, et al. A Hybrid Method for False Data Injection Attack Detection in Smart Grid Based on Variational Mode Decomposition and OS-ELM[J]. CSEE Journal of Power and Energy Systems, 2022, 8(6): 1697-1707.
Chunxia Dou, Di Wu, Dong Yue, 等. A Hybrid Method for False Data Injection Attack Detection in Smart Grid Based on Variational Mode Decomposition and OS-ELM[J]. 中国电机工程学会电力与能源系统学报(英文), 2022,8(6):1697-1707. DOI： 10.17775/CSEEJPES.2019.00670.

Chunxia Dou, Di Wu, Dong Yue, et al. A Hybrid Method for False Data Injection Attack Detection in Smart Grid Based on Variational Mode Decomposition and OS-ELM[J]. CSEE Journal of Power and Energy Systems, 2022, 8(6): 1697-1707. DOI： 10.17775/CSEEJPES.2019.00670.

摘要

Abstract

Accurate state estimation is critical to wide-area situational awareness of smart grid. However

recent research found that power system state estimators are vulnerable to a new type of cyber-attack

called false data injection attack (FDIA). In order to ensure the security of power system operation and control

a hybrid FDIA detection mechanism utilizing temporal correlation is proposed. The proposed mechanism combines Variational Mode Decomposition (VMD) technology and machine learning. For the purpose of identifying the features of FDIA

VMD is used to decompose the system state time series into an ensemble of components with different frequencies. Furthermore

due to the lack of online model updating ability in a traditional extreme learning machine

an OS-extreme learning machine (OS-ELM) which has sequential learning ability is used as a detector for identifying FDIA. The proposed detection mechanism is evaluated on the IEEE-14 bus system using real load data from an independent system operator in New York. Apart from detection accuracy

the impact of attack intensity and environment noise on the performance of the proposed method are tested. The simulation results demonstrate the efficiency and robustness of our method.

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