Time Sequence Machine Learning-Based Data Intrusion Detection for Smart Voltage Source Converter-Enabled Power Grid

Sadi, Mohammad Ashraf Hossain; Zhao, Dongbo; Hong, Tianqi; Ali, Mohd. Hasan

doi:10.1109/jsyst.2022.3186619

Title: Time Sequence Machine Learning-Based Data Intrusion Detection for Smart Voltage Source Converter-Enabled Power Grid

Journal Article · Fri Jul 29 00:00:00 EDT 2022 · IEEE Systems Journal

DOI:https://doi.org/10.1109/jsyst.2022.3186619· OSTI ID:1995402

Sadi, Mohammad Ashraf Hossain ^[1]; Zhao, Dongbo ^[2]; Hong, Tianqi ^[2]; Ali, Mohd. Hasan ^[3]

University of Central Missouri, Warrensburg, MO (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States)
University of Memphis, Memphis, TN (United States)

Smart inverters of distributed energy resources can enable cloud computing, condition monitoring, result visualization, remote control, and peer-to-peer energy trading in advanced power systems. However, the advent of data injection attacks in the communication architecture can alter measurement characteristics of power grids and have devastating consequences. In this article, we propose a time sequence machine learning-based anomaly detection methodology for detecting cyber intrusion into control signal setpoints and dc voltage signal measurement bias of the voltage source converter (VSC) in wind generators. We first investigated the effects of four types of denial of service, tampering signal, and stealthy-type data intrusion attacks on smart VSCs and overall wind farms. We then proposed a novel time sequence machine learning-based intrusion detection framework that can be implemented to detect different cyberattacks in the VSCs. The performance of the proposed framework has been compared with that of autoencoder and clustering-based intrusion detection framework. The proposed framework was validated by using the IEEE 39 bus power system in the presence of four wind farms in different locations. Using several metrics for intrusion detection performance, we validated the effectiveness of the proposed framework.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1995402

Journal Information:: IEEE Systems Journal, Vol. 17, Issue 2; ISSN 1932-8184

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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Title: Time Sequence Machine Learning-Based Data Intrusion Detection for Smart Voltage Source Converter-Enabled Power Grid

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