Victoria O’Brien, Rodrigo Trevizan, and Vittal Rao from Texas Tech University conducted a study focused on detecting, identifying, and classifying false data injection attacks that corrupt voltage sensors in battery stacks. The results of this study were published in a journal article entitled “Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model” in the IEEE Open Access Journal of Power and Energy on November 7, 2024.
Monitoring the sensors of grid-scale battery energy storage systems for malicious cyberattacks, such as false data injection attacks, is crucial for ensuring their safe operation. Previous studies have enabled the detection of such attacks, but they often only flagged an attack somewhere in the system. The novel approach discussed in this paper utilizes a single particle battery model, allowing for the detection of false data injection attacks, identification of the corrupted sensors, and classification of the bias of the false data injection attack as either positive or negative. This method is effective in both online and offline applications, enabling real-time detection of cyberattacks. The proposed approach demonstrated high accuracy, achieving a false alarm rate of 0%, detection rates of 99.83%, and identification and classification rates of 97% each.
The IEEE Open Access Journal of Power and Energy is a high-quality technical journal that covers topics related to power systems and has an impact factor of 3.3, validating the significance of this research.
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This work was supported by the U.S. Department of Energy, Office of Electricity (OE), Energy Storage Division.
Sandia National Laboratories is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.