Securing FACTS-Based Wide Area Damping Controllers Using Modified Conditional Generative Adversarial Networks
Bibliographic record
Abstract
The performance of wide-area damping controllers (WADCs) heavily depends on the accuracy and authenticity of the measurements received from phasor measurement units (PMUs). These controllers receive PMU data and send the control commands back to grid actuators, e.g., flexible AC transmission systems (FACTS) devices. The use of cyber systems required for transferring PMU measurements, however, makes the controller and entire power system prone to a variety of cyber attacks, e.g., false data injection attacks (FDIAs). On this basis, this paper (i) proposes an FDIA model against FACTS-based WADCs and (ii) develops detection and mitigation methods for the proposed attacks. First, FDIAs are designed to destabilize the system, considering realistic limitations on the power grids. Then, a modified conditional generative adversarial network (MCGAN) is utilized for the detection and mitigation of these FDIAs. To detect this attack, a detector is developed from the discriminator of MCGAN, using the fine-tuning technique. The use of this proposed method enhances detection performance in imbalanced datasets and effectively identifies unseen high-risk attacks. Following the detection, a mitigation method is implemented based on the coordination of a graph-based interpolation and the tuned generator of the developed MCGAN. This method effectively mitigates the impact of the FDIAs on the FACTS-based WADCs. The effectiveness of the attack model, as well as the detection and mitigation methods, is assessed using the two-area Kundur and New England 39-Bus test 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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".