A Novel Detection and Identification Mechanism for Malicious Injection Attacks in Power Systems

• Published in: Symmetry (Basel) Vol. 15; no. 12; p. 2104
• Main Authors: Zhang, Hongfeng, Wang, Xinyu, Ban, Lan, Sun, Molin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-12-2023
• Subjects: Analysis; attack identification; Control systems; cosine similarity theorem; Design; Design parameters; Electric power systems; Electricity distribution; false data injection attack; Identification; Methods; power system; Sensors; Similarity theorem; Simulation methods; State estimation; Symmetry; unknown input observer
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym15122104

The integration of advanced sensor technology and control technology has gradually improved the operational efficiency of traditional power systems. Due to the undetectability of these attacks using traditional chi-square detection techniques, the state estimation of power systems is vulnerable to cyber–physical attacks, For this reason, this paper presents a novel detection and identification framework for detecting malicious attacks in power systems from the perspective of cyber–physical symmetry. To consider the undetectability of cyber–physical attacks, a physical dynamics detection model using the unknown input observers (UIOs) and cosine similarity theorem is proposed. Through the design of UIO parameters, the influence of attacks on state estimation can be eliminated. A cosine similarity value-based detection criterion is proposed to replace the traditional detection threshold. To further cut down the effects caused by malicious attacks, an observer combination-based attack identification framework is established. Finally, simulations are given to demonstrate that the proposed security method can detect and identify the injected malicious attacks quickly and effectively.