Cited by 180Power System Optimization and StabilitySmart Grid Security and ResiliencePower Systems Fault Detection

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Classification of Disturbances and Cyber-Attacks in Power Systems Using Heterogeneous Time-Synchronized Data

Pan, Shengyi, Morris, Thomas, Adhikari, Uttam·2015·IEEE Transactions on Industrial Informatics

DOI 10.1109/tii.2015.2420951

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Abstract

Visualization and situational awareness are of vital importance for power systems, as the earlier a power-system event such as a transmission line fault or cyber-attack is identified, the quicker operators can react to avoid unnecessary loss. Accurate time-synchronized data, such as system measurements and device status, provide benefits for system state monitoring. However, the time-domain analysis of such heterogeneous data to extract patterns is difficult due to the existence of transient phenomena in the analyzed measurement waveforms. This paper proposes a sequential pattern mining approach to accurately extract patterns of power-system disturbances and cyber-attacks from heterogeneous time-synchronized data, including synchrophasor measurements, relay logs, and network event monitor logs. The term common path is introduced. A common path is a sequence of critical system states in temporal order that represent individual types of disturbances and cyber-attacks. Common paths are unique signatures for each observed event type. They can be compared to observed system states for classification. In this paper, the process of automatically discovering common paths from labeled data logs is introduced. An included case study uses the common path-mining algorithm to learn common paths from a fusion of heterogeneous synchrophasor data and system logs for three types of disturbances (in terms of faults) and three types of cyber-attacks, which are similar to or mimic faults. The case study demonstrates the algorithm's effectiveness at identifying unique paths for each type of event and the accompanying classifier's ability to accurately discern each type of event.

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Authors: Pan, S., Morris, T., & Adhikari, U.
Affiliations: Mississippi State University (US)
ORCID: Pan, Shengyi
Morris, Thomas
Adhikari, Uttam
Venue: IEEE Transactions on Industrial Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2015-06
Volume: 11
Issue: 3
Pages: 650-662
ISSN: 1551-3203 (ISSN-L)
1941-0050
DOI: 10.1109/tii.2015.2420951
OpenAlex: W2020524857
Cited by: 180
References: 29
Citations by year: 2026: 19 · 2025: 16 · 2024: 17 · 2023: 30 · 2022: 17 · 2021: 16 · 2020: 17 · 2019: 19 · 2018: 7 · 2017: 16 · 2016: 4 · 2015: 2
FWCI: 4.66
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Industrial Informatics (publishedVersion)
Funders: Division of Undergraduate Education (1315726)
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
Physical Sciences › Engineering › Control and Systems Engineering › Power Systems Fault Detection
Keywords: cyberattack detection, sequential pattern mining, synchrophasor measurements, fault classification, situational awareness

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Classification of Disturbances and Cyber-Attacks in Power Systems Using Heterogeneous Time-Synchronized Data

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