Critical meter identification and network embedding based attack detection for power systems against false data injection attacks. (December 2022)

• Record Type: Journal Article
• Authors: Lian, Xianglong; Qian, Tong; Zhang, Yin; Tang, Wenhu; Wu, Qinghua
• Is Part Of: International journal of electrical power & energy systems. Volume 143(2022)
• Publication Date: 2022-12
• Subjects: False data injection attacks (FDIAs) -- Network embedding -- State estimation -- Power system; 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.2022.108389
• Languages: English
• ISSNs: 0142-0615

Abstract

Abstract: Modern power systems are becoming vulnerable to false data injection attacks (FDIAs) due to the high penetration of communication devices. To deal with such threats and challenges, this research develops a multi-task framework for power system operations against FDIAs, which aims to: (1) investigate the mechanism for the grid response model to FDIAs; (2) identify and protect critical meter measurements to reduce the loss of grids to such attacks; (3) detect attacks which may lead to severe consequences. To this end, a quantitative critical meter index (CMI ) and a severe attack detection method based on network embeddings are proposed. The performance of the proposed method is evaluated by FDIAs simulations in the Institute of Electrical and Electronic Engineers (IEEE) 30- and 118-bus systems. Results show that protecting the identified meters can reduce the load shedding of 118-bus system greatly, and the accuracy rate of the proposed method to detect severe attacks reaches 96.62% and 98.91% for 30- and 118-bus systems against FDIAs, respectively. Highlights: Develope a response model of a power grid against FDIAs Propose a index evaluating the importance ranking of meter measurements Propose a network embedding based detection method to detect severe FDIAs