Detection of Synchrophasor False Data Injection Attack using Feature Interactive Network

Qiu, Wei; Tang, Qiu; Zhu, Kunzhi; Wang, Weikang; Liu, Yilu; Yao, Wenxuan

doi:10.1109/tsg.2020.3014311

Title: Detection of Synchrophasor False Data Injection Attack using Feature Interactive Network

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Abstract

The synchrophasor data recorded by Phasor Measurement Units (PMUs) plays an increasingly critical role in the regulation and situational awareness of power systems. However, the widely installed PMUs are vulnerable to multiple malicious attacks from cyber hackers during data transmission and storage. To address this problem, a Modified Ensemble Empirical Mode Decomposition (MEEMD) is proposed first to extract the intrinsic mode functions of each Synchrophasor Data Attacks (SDA). The frequency-based adaptive screening criterion embedded in MEEMD is used to eliminate the false intrinsic mode functions. Next, a Multivariate Convolutional Neural Network (MCNN) is proposed to identify multiple SDA by utilizing the extracted intrinsic mode functions and original SDA as input vectors. A fusion block as the main structure of MCNN is also leveraged to increase the diversity of features and compress the model parameters. Integrating MEEMD and MCNN, a framework with automatic feature extraction and multi-source information fusion capability, referred to as Feature Interactive Network (FIN), is proposed to detect multiple SDA. Based on the proposed FIN framework, six types of SDA are explored for the first time using actual synchrophasor data in FNET/Grideye that was collected from different locations in the U.S. Eastern Interconnection. Finally, a large quantity ofmore »« less

Authors:

Qiu, Wei ^[1]; Tang, Qiu ^[2]; Zhu, Kunzhi ^[2]; Wang, Weikang ^[3]; Liu, Yilu ^[4]; Yao, Wenxuan ^[5]

Hunan Univ., Changsha (China); Univ. of Tennessee, Knoxville, TN (United States)
Hunan Univ., Changsha (China)
Univ. of Tennessee, Knoxville, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Publication Date:: Wed Aug 05 00:00:00 EDT 2020

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1665970

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Smart Grid

Additional Journal Information:: Journal Volume: TBD; Journal Issue: TBD; Journal ID: ISSN 1949-3053

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 24 POWER TRANSMISSION AND DISTRIBUTION; Feature extraction; Phasor measurement units; Convolution; Convolutional neural networks; Support vector machines; Anomaly detection; Power systems

Citation Formats


                    Qiu, Wei, Tang, Qiu, Zhu, Kunzhi, Wang, Weikang, Liu, Yilu, and Yao, Wenxuan. Detection of Synchrophasor False Data Injection Attack using Feature Interactive Network.  United States: N. p., 2020. 
Web.  doi:10.1109/tsg.2020.3014311.

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                    Qiu, Wei, Tang, Qiu, Zhu, Kunzhi, Wang, Weikang, Liu, Yilu, & Yao, Wenxuan. Detection of Synchrophasor False Data Injection Attack using Feature Interactive Network.  United States.  https://doi.org/10.1109/tsg.2020.3014311

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                    Qiu, Wei, Tang, Qiu, Zhu, Kunzhi, Wang, Weikang, Liu, Yilu, and Yao, Wenxuan. Wed .  
"Detection of Synchrophasor False Data Injection Attack using Feature Interactive Network".  United States.  https://doi.org/10.1109/tsg.2020.3014311.  https://www.osti.gov/servlets/purl/1665970.

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@article{osti_1665970,

  title        = {Detection of Synchrophasor False Data Injection Attack using Feature Interactive Network},

  author       = {Qiu, Wei and Tang, Qiu and Zhu, Kunzhi and Wang, Weikang and Liu, Yilu and Yao, Wenxuan},

  abstractNote = {The synchrophasor data recorded by Phasor Measurement Units (PMUs) plays an increasingly critical role in the regulation and situational awareness of power systems. However, the widely installed PMUs are vulnerable to multiple malicious attacks from cyber hackers during data transmission and storage. To address this problem, a Modified Ensemble Empirical Mode Decomposition (MEEMD) is proposed first to extract the intrinsic mode functions of each Synchrophasor Data Attacks (SDA). The frequency-based adaptive screening criterion embedded in MEEMD is used to eliminate the false intrinsic mode functions. Next, a Multivariate Convolutional Neural Network (MCNN) is proposed to identify multiple SDA by utilizing the extracted intrinsic mode functions and original SDA as input vectors. A fusion block as the main structure of MCNN is also leveraged to increase the diversity of features and compress the model parameters. Integrating MEEMD and MCNN, a framework with automatic feature extraction and multi-source information fusion capability, referred to as Feature Interactive Network (FIN), is proposed to detect multiple SDA. Based on the proposed FIN framework, six types of SDA are explored for the first time using actual synchrophasor data in FNET/Grideye that was collected from different locations in the U.S. Eastern Interconnection. Finally, a large quantity of experiments with different attack strengths are used to evaluate the adaptability and classification performance of the proposed FIN.},

  doi          = {10.1109/tsg.2020.3014311},

  journal      = {IEEE Transactions on Smart Grid},

  number       = TBD,

  volume       = TBD,

  place        = {United States},

  year         = {Wed Aug 05 00:00:00 EDT 2020},

  month        = {Wed Aug 05 00:00:00 EDT 2020}

}

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