False Data Injection Attacks Against State-of-Charge Estimation of Battery Energy Storage Systems in Smart Distribution Networks
Bibliographic record
Abstract
Advancement of battery energy storage systems (BESSs) has made BESSs typical cyber-physical systems (CPSs), which exposes BESSs, especially for the state of charge (SoC) estimation function, to severe cyber attacks. This article investigates the vulnerability of SoC estimation of BESSs in smart distribution networks (SDNs) to false data injection attacks (FDIAs) to provide a basis to study the attack mechanisms against BESSs and a theoretical guide to develop countermeasures. The mechanism of FDIAs against SoC estimation is investigated by theoretically analyzing the SoC estimation errors caused by compromised measurements, and a static FDIA model targeting one snapshot of SoC estimation is formulated. Considering temporal correlation among measurements, a detection method using the innovation test is proposed for static FDIAs, where the innovations are derived statistically. Considering the error accumulation effect, a novel sequential FDIA is proposed, which consists of a sequence of static FDIAs with small magnitudes. They can bypass most of the existing bad data detection algorithms, including the innovation test, with significant attack effects. An online approach is proposed for the practical construction of sequential FDIAs, which is formulated as a linear programming problem. Case studies based on modified IEEE 13 bus test feeder demonstrate the vulnerability of SoC estimation to FDIAs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".