Detecting False Data Injection Attacks to Battery State Estimation Using Cumulative Sum Algorithm

Victoria Obrien, Rodrigo D. Trevizan, Vittal S. Rao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Estimated parameters in Battery Energy Storage Systems (BESSs) may be vulnerable to cyber-attacks such as False Data Injection Attacks (FDIAs). FDIAs, which typically evade bad data detectors, could damage or degrade Battery Energy Storage Systems (BESSs). This paper will investigate methods to detect small magnitude FDIA using battery equivalent circuit models, an Extended Kalman Filter (EKF), and a Cumulative Sum (CUSUM) algorithm. A priori error residual data estimated by the EKF was used in the CUSUM algorithm to find the lowest detectable FDIA for this battery equivalent model. The algorithm described in this paper was able to detect attacks as low as 1 mV, with no false positives. The CUSUM algorithm was compared to a chi-squared based FDIA detector. In this study the CUSUM was found to detect attacks of smaller magnitudes than the conventional chi-squared detector.

Original languageEnglish
Title of host publication2021 North American Power Symposium, NAPS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665420815
DOIs
StatePublished - 2021
Event2021 North American Power Symposium, NAPS 2021 - College Station, United States
Duration: Nov 14 2021Nov 16 2021

Publication series

Name2021 North American Power Symposium, NAPS 2021

Conference

Conference2021 North American Power Symposium, NAPS 2021
Country/TerritoryUnited States
CityCollege Station
Period11/14/2111/16/21

Keywords

  • anomaly detection
  • battery management systems
  • cumulative sum
  • false data injection attacks
  • smart grid

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