A Cyber-Physical Anomaly Detection for Wide-Area Protection Using Machine Learning

Singh, Vivek Kumar; Govindarasu, Manimaran

doi:10.1109/tsg.2021.3066316

A Cyber-Physical Anomaly Detection for Wide-Area Protection Using Machine Learning

Journal Article · Wed Mar 17 04:00:00 EDT 2021 · IEEE Transactions on Smart Grid

DOI:https://doi.org/10.1109/tsg.2021.3066316· OSTI ID:1985651

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Idaho National Laboratory (INL), Idaho Falls, ID (United States); Iowa State University
Iowa State University, Ames, IA (United States)

Wide-area protection scheme (WAPS) provides system-wide protection by detecting and mitigating small and large-scale disturbances that are difficult to resolve using local protection schemes. As this protection scheme is evolving from a substation-based distributed remedial action scheme (DRAS) to the control center-based centralized RAS (CRAS), it presents severe challenges to their cybersecurity because of its heavy reliance on an insecure grid communication, and its compromise would lead to system failure. This article presents an architecture and methodology for developing a cyber-physical anomaly detection system (CPADS) that utilizes synchrophasor measurements and properties of network packets to detect data integrity and communication failure attacks on measurement and control signals in CRAS. The proposed machine leaning-based methodology applies a rules-based approach to select relevant input features, utilizes variational mode decomposition (VMD) and decision tree (DT) algorithms to develop multiple classification models, and performs final event identification using a rules-based decision logic. Here, we have evaluated the proposed methodology of CPADS using the IEEE 39 bus system for several performance measures (accuracy, recall, precision, and F-measure) in a cyber-physical testbed environment. Furthermore, our experimental results reveal that the proposed algorithm (VMD-DT) of CPADS outperforms the existing machine learning classifiers during noisy and noise-free measurements while incurring an acceptable processing overhead.

View Accepted Manuscript (DOE)

Research Organization:: Iowa State University, Ames, IA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: OE0000830

OSTI ID:: 1985651

Report Number(s):: DOE-ISU-0000830-7

Journal Information:: IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Journal Issue: 4 Vol. 12; ISSN 1949-3053

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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24 POWER TRANSMISSION AND DISTRIBUTION
Wide-area protection
cybersecurity
machine learning
synchrophasor
variational mode decomposition

A Cyber-Physical Anomaly Detection for Wide-Area Protection Using Machine Learning

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