DOI:10.35833/MPCE.2019.000119 |
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A Two-stage Kalman Filter for Cyber-attack Detection in Automatic Generation Control System |
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Page view: 201
Net amount: 640 |
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Author:
Ayyarao S. L. V. Tummala1,Ravi Kiran Inapakurthi2
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Author Affiliation:
1.GMR Institute of Technology, Rajam, Srikakulam, Andhra Pradesh, India;2. Raghu Engineering College, Dakamarri, Visakhapatnam, Andhra Pradesh, India
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Abstract: |
Communication plays a vital role in incorporating smartness into the interconnected power system. However, historical records prove that the data transfer has always been vulnerable to cyber-attacks. Unless these cyber-attacks are identified and cordoned off, they may lead to black-out and result in national security issues. This paper proposes an optimal two-stage Kalman filter (OTS-KF) for simultaneous state and cyber-attack estimation in automatic generation control (AGC) system. Biases/cyber-attacks are modeled as unknown inputs in the AGC dynamics. Five types of cyber-attacks, i.e., false data injection (FDI), data replay attack, denial of service (DoS), scaling, and ramp attacks, are injected into the measurements and estimated using OTS-KF. As the load variations of each area are seldom available, OTS-KF is reformulated to estimate the states and outliers along with the load variations of the system. The proposed technique is validated on the benchmark two-area, three-area, and five-area power system models. The simulation results under various test conditions demonstrate the efficacy of the proposed filter. |
Keywords: |
Cyber-security ; automatic generation control (AGC) ; load frequency control ; false data injection ; cyber-attack detection |
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Received:October 25, 2019
Online Time:2022/01/28 |
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