Multi-Agent Based Cyber Attack Detection and Mitigation for Distribution Automation System

As electric power became an essential part of daily life, resiliency and reliability of operation became important. A distribution management system (DMS) enables real-time monitoring and dynamic controls of the power distribution networks. As such, their controls should be designed to be resilient...

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Main Authors: In-Sun Choi, Junho Hong, Tae-Wan Kim
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9217462/
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author In-Sun Choi
Junho Hong
Tae-Wan Kim
author_facet In-Sun Choi
Junho Hong
Tae-Wan Kim
author_sort In-Sun Choi
collection DOAJ
description As electric power became an essential part of daily life, resiliency and reliability of operation became important. A distribution management system (DMS) enables real-time monitoring and dynamic controls of the power distribution networks. As such, their controls should be designed to be resilient against distribution grid disturbances and cyber events. Due to the high penetration of Information and Communication Technology (ICT) in the DMS, the reliability of power distribution grids is highly dependent on the cyber system. However, most power distribution applications, communication protocols, and devices are vulnerable to the cyber attacks since they are designed and implemented before cybersecurity became a critical issue. Recent reports and cyber attack incidents clearly indicate that cyber-attacks are increasingly likely on the power system infrastructures, e.g., control centers, nuclear power plants, and substations. These attacks may cause significant damages to the power grid. Cybersecurity research for the power distribution grid is a high priority subject in the emerging smart grid environment. This article proposes cybersecurity enhanced distribution automation system (DAS) with a multi-agent system (MAS). The proposed multi-agent based cyber attack detection and mitigation algorithms can identify the anomalies, abnormal activities, and unusual system operations of the DAS. The proposed algorithms have been applied to the existing communication protocols, protection schemes, and restoration applications. The result is validated with a testbed and proposes a new integrated tool for the detection and mitigation of cyber intrusions at a power distribution grid with multiple feeders.
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spelling doaj.art-2b9f96f3b82f48cfaf38224cc5b3608f2022-12-21T23:44:51ZengIEEEIEEE Access2169-35362020-01-01818349518350410.1109/ACCESS.2020.30297659217462Multi-Agent Based Cyber Attack Detection and Mitigation for Distribution Automation SystemIn-Sun Choi0Junho Hong1https://orcid.org/0000-0001-5035-8260Tae-Wan Kim2Department of Electrical Engineering, Myongji University, Yongin, South KoreaDepartment of Electrical and Computer Engineering, University of Michigan–Dearborn, Dearborn, MI, USADepartment of Electrical Engineering, Myongji University, Yongin, South KoreaAs electric power became an essential part of daily life, resiliency and reliability of operation became important. A distribution management system (DMS) enables real-time monitoring and dynamic controls of the power distribution networks. As such, their controls should be designed to be resilient against distribution grid disturbances and cyber events. Due to the high penetration of Information and Communication Technology (ICT) in the DMS, the reliability of power distribution grids is highly dependent on the cyber system. However, most power distribution applications, communication protocols, and devices are vulnerable to the cyber attacks since they are designed and implemented before cybersecurity became a critical issue. Recent reports and cyber attack incidents clearly indicate that cyber-attacks are increasingly likely on the power system infrastructures, e.g., control centers, nuclear power plants, and substations. These attacks may cause significant damages to the power grid. Cybersecurity research for the power distribution grid is a high priority subject in the emerging smart grid environment. This article proposes cybersecurity enhanced distribution automation system (DAS) with a multi-agent system (MAS). The proposed multi-agent based cyber attack detection and mitigation algorithms can identify the anomalies, abnormal activities, and unusual system operations of the DAS. The proposed algorithms have been applied to the existing communication protocols, protection schemes, and restoration applications. The result is validated with a testbed and proposes a new integrated tool for the detection and mitigation of cyber intrusions at a power distribution grid with multiple feeders.https://ieeexplore.ieee.org/document/9217462/Cybersecurity of distribution automation systemmulti-agent based systemfault detectionisolation and detection
spellingShingle In-Sun Choi
Junho Hong
Tae-Wan Kim
Multi-Agent Based Cyber Attack Detection and Mitigation for Distribution Automation System
IEEE Access
Cybersecurity of distribution automation system
multi-agent based system
fault detection
isolation and detection
title Multi-Agent Based Cyber Attack Detection and Mitigation for Distribution Automation System
title_full Multi-Agent Based Cyber Attack Detection and Mitigation for Distribution Automation System
title_fullStr Multi-Agent Based Cyber Attack Detection and Mitigation for Distribution Automation System
title_full_unstemmed Multi-Agent Based Cyber Attack Detection and Mitigation for Distribution Automation System
title_short Multi-Agent Based Cyber Attack Detection and Mitigation for Distribution Automation System
title_sort multi agent based cyber attack detection and mitigation for distribution automation system
topic Cybersecurity of distribution automation system
multi-agent based system
fault detection
isolation and detection
url https://ieeexplore.ieee.org/document/9217462/
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AT junhohong multiagentbasedcyberattackdetectionandmitigationfordistributionautomationsystem
AT taewankim multiagentbasedcyberattackdetectionandmitigationfordistributionautomationsystem