FDI Attacks against Real-Time DLMP in CPS-Based Smart Distribution Systems
Bibliographic record
Abstract
In this paper, the impacts of false data injection (FDI) attacks against real-time distribution locational marginal pricing (DLMP) in cyber-physical systems (CPS)-based smart distribution systems are analyzed. Firstly, an explicit expression of real-time DLMP is derived based on radial structure, small bus voltage phase angle changes, and high R/X ratio features of practical smart distribution systems, by leveraging the distribution system state estimation results. This derived explicit expression of real-time DLMP is analyzed based on the KarushKuhn-Tucker conditions, which reveals that the real-time DLMP can be modified by manipulating bus voltage information through FDI attacks. Then, the practical construction of FDI attacks against real-time DLMP is investigated based on an optimization problem under the constraints of minimum sparsity for FDI attacks and voltage regulation and power balance for system operation. Further, based on the features of practical power distribution system, this optimization problem is transformed into a convex-concave fractional programming problem, which is solved by using the Dinkelbach's algorithm at low computational complexity. A case study based on modified IEEE 13-bus test feeder is conducted to illustrate the potential impacts of FDI attacks in CPS-based smart distribution systems.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".