Detection and isolation of false data injection attack via adaptive Kalman filter bank

Abstract

Due to the integration of cyber–physical systems, smart grids have faced the new security risks caused by false data injection attacks (FDIAs). FDIAs can bypass the traditional bad data detection techniques by falsifying the process of state estimation. For this reason, this paper studies the detection and isolation problem of FDIAs based on the adaptive Kalman filter bank (AKFB) in smart grids. Taking the covert characteristics of FDIAs into account, a novel detection method is proposed based on the designed AKF. Moreover, the adaptive threshold is proposed to solve the detection delay caused by a priori threshold in the current detection methods. Considering the case of multiple attacked sensor nodes, the AKFB-based isolation method is developed. To reduce the number of isolation iterations, a logical decision matrix scheme is designed. Finally, the effectiveness of the proposed detection and isolation method is demonstrated on an IEEE 22-bus smart grids.

Keywords:

• Smart grids
• false data inject attack
• detection and isolation
• Kalman filter bank

Acknowledgements

We are very thankful to anonymous reviewers and editor for their valuable suggestions and comments which helped us to improve the quality of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by the National Nature Science Foundation of China under 61873228 and 62103357, by the Science and Technology Plan of Hebei Education Department under QN2021139, by the Nature Science Foundation of Hebei Province under F2021203043, and by the Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network, Nanjing Institute of Technology under XTCX202203.

Notes on contributors

Xiaoyuan Luo

Xiaoyuan Luo received the M.Eng. and Ph.D. degrees from the Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China, in 2001 and 2004, respectively. His research interests include fault detection and fault tolerant control, and multiagent and networked control systems.

Minggao Zhu

Minggao Zhu received the master's degree in measurement control technology and instrument from the Yanshan University, Qinhuangdao, China, in 2019. His research interests cover in detection and defence of cyber attack of CPS.

Xinyu Wang

Xinyu Wang received Ph.D. degrees from the Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China, in 2020. His research interests include cyber–physical attack detection and defense.

Xinping Guan

Xinping Guan received the M.Eng. and Ph.D. degrees from the Harbin Industrial University, Harbin, China, in 1991 and 1999, respectively. He is currently a Chair Professor with Shanghai Jiao Tong University, Shanghai, China, He is a National Outstanding Youth Honored by NSF of China, Changjiang Scholar by the Ministry of Education of China and State-level Scholar of New Century Bai Qianwan Talent Program of China. As a Principal Investigator, he has finished/been working on many national key projects.