Modeling Cascading Failures in Coupled Smart Grid Networks
Bibliographic record
Abstract
The smart grid connects components of power systems and communication networks in an interdependent two-way system that delivers electricity to consumers and collects data that enables it to react to usage levels and interference from threats, such as cyber-attacks. In this paper, we propose a novel cyber-attack failure propagation model in smart grids. Our realistic failure propagation model addresses the system’s heterogeneity by assigning different roles to its components. We define rules for and interdependencies of failure propagation and propose a new approach to studying cascading failures. In addition, our graph model identifies the most-vulnerable nodes. The model implements power flow analysis to guarantee that all transmission lines work below capacity and remove lines exceeding capacity. The model also considers that control packets could encounter different delays regarding the communication network structure and investigates the impact of communication delay on the failure of power components. Our results establish that by considering both power and communication characteristics and interdependencies, cascading failures can be modeled more accurately. We show that when we run the power flow analysis, there are a negligible number of failed nodes, which means that our model accurately identifies system failures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".