On the Resiliency of Power and Gas Integration Resources Against Cyber Attacks
Bibliographic record
Abstract
Integration of power and gas systems has been recently proposed as a portfolio solution to deal with the sporadic availability of renewables and enhance the flexibility of power systems. In an integrated system, where critical operating information and control signals of both systems need to be communicated, the risk of cyber attack is intensified. In this article, we present a new model for the integration of power and gas systems using power-to-gas (PtG) and gas-fired generation (GfG) facilities. We demonstrate how the operation of the integrated system can be adversely impacted during cyber attacks that may not be detected using traditional methods. We propose two new detection schemes for false data injection attacks against the input and output signals of the PtG/GfG facility scheduler. In the first scheme, a supervised machine-learning technique, based on the convolutional neural network and wavelet transforms, is adopted to detect attacks on the information received by the facility scheduler. In the second scheme, a hybrid neural network is developed, based on an unsupervised learning technique, that requires no labeled training information to detect attacks on the output control signals issued by the scheduler. In both schemes, information acquired from local sensors and deterministic estimation methods is utilized for signal evaluation. The proposed schemes are incorporated into the facilities' scheduler to create a cyber-attack resilient scheduling model in an integrated power and gas grid. The efficacy and feasibility of the proposed model are evaluated via numerical studies using the IEEE30-bus power system integrated with the Belgian gas grid as the test bed using historical operating parameters.
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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 | 0.003 |
| 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 | 0.001 |
| 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".