Cyberattack detection methods for battery energy storage systems

Nina Kharlamova*, Chresten Træhold, Seyedmostafa Hashemi

*Corresponding author for this work

Research output: Contribution to journalReviewpeer-review

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Abstract

Battery energy storage systems (BESSs) play a key role in the renewable energy transition. Meanwhile, BESSs along with other electric grid components are leveraging the Internet-of-things paradigm. As a downside, they become vulnerable to cyberattacks. The detection of cyberattacks against BESSs is becoming crucial for system redundancy. We identified a gap in the existing BESS defense research and formulated new types of attacks against a BESS and their detection methods. The attack detection is divided into a forecast-based approach and long-term pattern analysis. We perform a main factor analysis of machine-learning-based methods to forecast the behavior of a BESS. In addition, we observe approaches that can be adapted for the BESS cyber secure design. To provide a thorough investigation, the attacks are classified based on a targeted battery service along with data features that the attack targets.
Original languageEnglish
Article number107795
JournalJournal of Energy Storage
Volume69
Number of pages12
ISSN2352-152X
DOIs
Publication statusPublished - 2023

Keywords

  • Artificial intelligence
  • Battery energy storage system
  • Battery state estimation
  • Cyberattack
  • False data injection attack
  • Machine learning

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