Data-driven detection for GPS spoofing attack using phasor measurements in smart grid. (July 2021)

• Record Type: Journal Article
• Authors: Xue, Ancheng; Xu, Feiyang; Chow, Joe H.; Leng, Shuang; Kong, He; Xu, Jingsong; Bi, Tianshu
• Is Part Of: International journal of electrical power & energy systems. Volume 129(2021)
• Publication Date: 2021-07
• Subjects: GPS spoofing attack -- Attack detection -- Phasor measurement unit -- Line reactance -- Time synchronization problem; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2021.106883
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Two detection methods for the time synchronization attack (TSA) are proposed. The adaptive thresholds are robust to noise and make the methods automatic. The adaptive thresholds balance the sensitivity and robustness for TSA detection. The methods are simple but effective and have high detection sensitivity. Abstract: As the "GridEye", the wide area measurement system (WAMS) can provide the synchronized measurements for many applications in smart grid based on the geographically dispersed phasor measurement units (PMUs). However, most PMUs rely on the civil GPS/BDS (BeiDou navigation satellite system) signal as the time source and are vulnerable to GPS spoofing attack (GSA), which would deteriorate the PMU based applications. Compared to cyber-attack, the GSA can destroy the data integrity without the need of access to the physical network which makes the GSA more threatening. In this paper, two simple but effective and automatic detection methods for the GSA are proposed, which are based on monitoring the changes of estimated line reactance using PMU data: 1) method based on historical statistics and 2) method based on abrupt change index which is widely used in digital relays. In addition, the types of GSA can be identified. The proposed methods are data-driven, i.e., it needs the PMU data at both ends of the transmission line and does not require knowing the line parameters and the system topology. The adaptive thresholds are used to make the methods determine the threshold automatically and balance the sensitivity and robustness for GSA detection. The reliability and superiority of the proposed methods are demonstrated with simulated and measured PMU data.