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

• Published in: IEEE systems journal Vol. 17; no. 2; pp. 2477 - 2488
• Main Authors: Sadi, Mohammad Ashraf Hossain, Zhao, Dongbo, Hong, Tianqi, Ali, Mohd. Hasan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-06-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anomalies; Cloud computing; Clustering; Computer crime; Condition monitoring; Cyberattack; Cybersecurity; Denial of service attacks; Distributed generation; Electric potential; Electric power grids; Electric power systems; Energy sources; Generators; Intrusion detection systems; Inverters; Machine learning; Remote control; Signal measurement; Silicon; smart inverter; time sequence analysis; Voltage; Voltage control; voltage source converter; wide area measurement and control; Wind farms; Wind power; Wind power generation; Windpowered generators
• ISSN: 1932-8184; 1937-9234
• DOI: 10.1109/JSYST.2022.3186619

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.