Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model
The cells in battery energy storage systems are monitored, protected, and controlled by battery management systems whose sensors are susceptible to cyberattacks. False data injection attacks (FDIAs) targeting batteries’ voltage sensors affect cell protection functions and the estimation o...
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| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Access Journal of Power and Energy |
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| Online Access: | https://ieeexplore.ieee.org/document/10746526/ |
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| author | Victoria A. O'Brien Vittal S. Rao Rodrigo D. Trevizan |
| author_facet | Victoria A. O'Brien Vittal S. Rao Rodrigo D. Trevizan |
| author_sort | Victoria A. O'Brien |
| collection | DOAJ |
| description | The cells in battery energy storage systems are monitored, protected, and controlled by battery management systems whose sensors are susceptible to cyberattacks. False data injection attacks (FDIAs) targeting batteries’ voltage sensors affect cell protection functions and the estimation of critical battery states like the state of charge (SoC). Inaccurate SoC estimation could result in battery overcharging and over discharging, which can have disastrous consequences on grid operations. This paper proposes a three-pronged online and offline method to detect, identify, and classify FDIAs corrupting the voltage sensors of a battery stack. To accurately model the dynamics of the series-connected cells a single particle model is used and to estimate the SoC, the unscented Kalman filter is employed. FDIA detection, identification, and classification was accomplished using a tuned cumulative sum (CUSUM) algorithm, which was compared with a baseline method, the chi-squared error detector. Online simulations and offline batch simulations were performed to determine the effectiveness of the proposed approach. Throughout the batch simulations, the CUSUM algorithm detected attacks, with no false positives, in 99.83% of cases, identified the corrupted sensor in 97% of cases, and determined if the attack was positively or negatively biased in 97% of cases. |
| format | Article |
| id | doaj-art-1e5803ddd4004701a1fef71172834a8e |
| institution | Kabale University |
| issn | 2687-7910 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Access Journal of Power and Energy |
| spelling | doaj-art-1e5803ddd4004701a1fef71172834a8e2025-01-21T00:03:22ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102024-01-011157158210.1109/OAJPE.2024.349375710746526Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle ModelVictoria A. O'Brien0https://orcid.org/0000-0001-9884-1374Vittal S. Rao1https://orcid.org/0000-0002-7332-4570Rodrigo D. Trevizan2https://orcid.org/0000-0003-2885-1213Electric Grid Security and Communications Department, Sandia National Laboratories, Albuquerque, NM, USADepartment of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USAEnergy Storage Technology and Systems Department, Sandia National Laboratories, Albuquerque, NM, USAThe cells in battery energy storage systems are monitored, protected, and controlled by battery management systems whose sensors are susceptible to cyberattacks. False data injection attacks (FDIAs) targeting batteries’ voltage sensors affect cell protection functions and the estimation of critical battery states like the state of charge (SoC). Inaccurate SoC estimation could result in battery overcharging and over discharging, which can have disastrous consequences on grid operations. This paper proposes a three-pronged online and offline method to detect, identify, and classify FDIAs corrupting the voltage sensors of a battery stack. To accurately model the dynamics of the series-connected cells a single particle model is used and to estimate the SoC, the unscented Kalman filter is employed. FDIA detection, identification, and classification was accomplished using a tuned cumulative sum (CUSUM) algorithm, which was compared with a baseline method, the chi-squared error detector. Online simulations and offline batch simulations were performed to determine the effectiveness of the proposed approach. Throughout the batch simulations, the CUSUM algorithm detected attacks, with no false positives, in 99.83% of cases, identified the corrupted sensor in 97% of cases, and determined if the attack was positively or negatively biased in 97% of cases.https://ieeexplore.ieee.org/document/10746526/Anomaly detectionanomaly identificationchi-squaredconcentration modelcumulative sumfalse data injection attacks |
| spellingShingle | Victoria A. O'Brien Vittal S. Rao Rodrigo D. Trevizan Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model IEEE Open Access Journal of Power and Energy Anomaly detection anomaly identification chi-squared concentration model cumulative sum false data injection attacks |
| title | Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model |
| title_full | Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model |
| title_fullStr | Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model |
| title_full_unstemmed | Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model |
| title_short | Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model |
| title_sort | online and offline identification of false data injection attacks in battery sensors using a single particle model |
| topic | Anomaly detection anomaly identification chi-squared concentration model cumulative sum false data injection attacks |
| url | https://ieeexplore.ieee.org/document/10746526/ |
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