Countering FDI Attacks on DERs Coordinated Control System Using FMI-Compatible Cosimulation
Bibliographic record
Abstract
In this article, the resilience of a coordinated control system for a set of PV-based distributed energy resources (DERs) against false data injection (FDI) attacks is evaluated. The evaluation is performed using a functional mock-up interface (FMI)-compatible cosimulation platform which enables the interaction of multi-domain simulators (EMTP, MATLAB/Simulink, and NS-3). The cosimulation platform permits rigorous analysis of cybersecurity through detailed modeling of all system components. The DER coordinated control and communication systems implemented on the IEEE-34 bus benchmark consist of measurement, control and monitoring components including substation central controller, DER local controllers, synchrophasor network and advanced metering infrastructure (AMI). Some DERs are equipped with an energy storage system (ESS) and coordinated by the central control unit in order to correct voltage disturbances resulting from the intermittent solar photovoltaic (PV) generation. The FDI attack targets the AMI system and aims at manipulating the load profile messages reported by the smart meter collector, thus yielding a central control failure. To detect the attacks and mitigate their impacts, a neural network-based algorithm is proposed and incorporated in the central control unit. The effectiveness of the proposed detection and mitigation algorithm is confirmed through simulations using the proposed FMI-compatible cosimulation platform.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".