Dynamic-State-Estimation-Based Cyber Attack Detection for Inverter-Based Resources

Avinash Kumar; Yuzhang Lin; Heqing Huang; Xiaonan Lu; Yue Zhao

doi:10.1109/PESGM52003.2023.10252357

Dynamic-State-Estimation-Based Cyber Attack Detection for Inverter-Based Resources

Avinash Kumar, Yuzhang Lin, Heqing Huang, Xiaonan Lu, Yue Zhao

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The cyber security of Inverter Based Resources (IBRs) has received increasing attention in recent years. In this paper, a cyber attack detection method is proposed based on dynamic state estimation for IBRs. The state-space models of the physical inverter system and the digital controller are separately derived and then coupled by the data flows in between, i.e., measurement signals and control signals. Based on Kalman Filtering (KF), two dual dynamic state estimators are developed for tracking the cyber and physical state variables, respectively. By checking the model-data consistency of both the physical and cyber layers via hypothesis testing, the proposed method features the capability of distinguishing between false data in measurement signals and in control signals. Simulation results in an IEEE 13 node test feeder demonstrate the effectiveness of the proposed method.

Original language	English (US)
Title of host publication	2023 IEEE Power and Energy Society General Meeting, PESGM 2023
Publisher	IEEE Computer Society
ISBN (Electronic)	9781665464413
DOIs	https://doi.org/10.1109/PESGM52003.2023.10252357
State	Published - 2023
Event	2023 IEEE Power and Energy Society General Meeting, PESGM 2023 - Orlando, United States Duration: Jul 16 2023 → Jul 20 2023

Publication series

Name	IEEE Power and Energy Society General Meeting
Volume	2023-July
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	2023 IEEE Power and Energy Society General Meeting, PESGM 2023
Country/Territory	United States
City	Orlando
Period	7/16/23 → 7/20/23

Keywords

Cyber security
Dynamic state estimation
Event detection
Inverter based resources
Kalman filter

ASJC Scopus subject areas

Energy Engineering and Power Technology
Nuclear Energy and Engineering
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering

Access to Document

10.1109/PESGM52003.2023.10252357

Cite this

Dynamic-State-Estimation-Based Cyber Attack Detection for Inverter-Based Resources. / Kumar, Avinash; Lin, Yuzhang; Huang, Heqing et al.
2023 IEEE Power and Energy Society General Meeting, PESGM 2023. IEEE Computer Society, 2023. (IEEE Power and Energy Society General Meeting; Vol. 2023-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kumar, A, Lin, Y, Huang, H, Lu, X & Zhao, Y 2023, Dynamic-State-Estimation-Based Cyber Attack Detection for Inverter-Based Resources. in 2023 IEEE Power and Energy Society General Meeting, PESGM 2023. IEEE Power and Energy Society General Meeting, vol. 2023-July, IEEE Computer Society, 2023 IEEE Power and Energy Society General Meeting, PESGM 2023, Orlando, United States, 7/16/23. https://doi.org/10.1109/PESGM52003.2023.10252357

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title = "Dynamic-State-Estimation-Based Cyber Attack Detection for Inverter-Based Resources",

abstract = "The cyber security of Inverter Based Resources (IBRs) has received increasing attention in recent years. In this paper, a cyber attack detection method is proposed based on dynamic state estimation for IBRs. The state-space models of the physical inverter system and the digital controller are separately derived and then coupled by the data flows in between, i.e., measurement signals and control signals. Based on Kalman Filtering (KF), two dual dynamic state estimators are developed for tracking the cyber and physical state variables, respectively. By checking the model-data consistency of both the physical and cyber layers via hypothesis testing, the proposed method features the capability of distinguishing between false data in measurement signals and in control signals. Simulation results in an IEEE 13 node test feeder demonstrate the effectiveness of the proposed method.",

keywords = "Cyber security, Dynamic state estimation, Event detection, Inverter based resources, Kalman filter",

author = "Avinash Kumar and Yuzhang Lin and Heqing Huang and Xiaonan Lu and Yue Zhao",

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doi = "10.1109/PESGM52003.2023.10252357",

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AU - Zhao, Yue

PY - 2023

Y1 - 2023

N2 - The cyber security of Inverter Based Resources (IBRs) has received increasing attention in recent years. In this paper, a cyber attack detection method is proposed based on dynamic state estimation for IBRs. The state-space models of the physical inverter system and the digital controller are separately derived and then coupled by the data flows in between, i.e., measurement signals and control signals. Based on Kalman Filtering (KF), two dual dynamic state estimators are developed for tracking the cyber and physical state variables, respectively. By checking the model-data consistency of both the physical and cyber layers via hypothesis testing, the proposed method features the capability of distinguishing between false data in measurement signals and in control signals. Simulation results in an IEEE 13 node test feeder demonstrate the effectiveness of the proposed method.

AB - The cyber security of Inverter Based Resources (IBRs) has received increasing attention in recent years. In this paper, a cyber attack detection method is proposed based on dynamic state estimation for IBRs. The state-space models of the physical inverter system and the digital controller are separately derived and then coupled by the data flows in between, i.e., measurement signals and control signals. Based on Kalman Filtering (KF), two dual dynamic state estimators are developed for tracking the cyber and physical state variables, respectively. By checking the model-data consistency of both the physical and cyber layers via hypothesis testing, the proposed method features the capability of distinguishing between false data in measurement signals and in control signals. Simulation results in an IEEE 13 node test feeder demonstrate the effectiveness of the proposed method.

KW - Cyber security

KW - Dynamic state estimation

KW - Event detection

KW - Inverter based resources

KW - Kalman filter

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ER -

Dynamic-State-Estimation-Based Cyber Attack Detection for Inverter-Based Resources

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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