Cyber-Physical Interdependence for Power System Operation and Control
Bibliographic record
Abstract
This Task Force (TF) report aims to analyze the dependence of cyber and physical systems on power system operation and control. The study of cyber and physical system interdependency has become increasingly important with the evolution of energy systems. Modeling and analyzing the interdependency between cyber and physical components in energy systems is crucial for ensuring their reliability, resiliency, and security. The TF aims to cover different layers of interdependence between cyber and physical systems through different modeling techniques and analysis methods to identify potential threats and vulnerabilities. Co-simulation methods and tools are effective in analyzing the dependence between cyber and physical systems. Co-simulation is an approach to modeling and simulation that combines different simulation models to create a more comprehensive system model. With the integration of distributed energy resources (DERs) and advancements in sensing devices and communication networks, co-simulation methods and tools can help energy systems operators to better understand the complex interactions between cyber and physical components. This TF report also covers the use of digital twin technology to model and simulate the interactions between cyber and physical components. Digital twins create a virtual replica of the physical system and integrate it with real-time data and simulation models, which allows operators to monitor the system's performance, identify potential vulnerabilities, and develop strategies to improve its resilience and security. Operation and stability control, which consider the interactions between cyber and physical components in an energy system, involve a coordinated approach. This is crucial because the dependable and efficient operation of modern energy systems relies heavily on communication and computer infrastructures for sensing, protection, control, and real-time operation. The objective of this TF is to utilize data analytics, artificial intelligence, and machine learning (ML) to attain optimal operation and stability control in an interdependent cyber-physical energy system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".