Detection of Synchrophasor False Data Injection Attack using Feature Interactive Network

Journal Article · Wed Aug 05 00:00:00 EDT 2020 · IEEE Transactions on Smart Grid

DOI:https://doi.org/10.1109/tsg.2020.3014311· OSTI ID:1665970

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

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

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1665970
• Journal Information: IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Journal Issue: TBD Vol. TBD; ISSN 1949-3053
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

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