Probabilistic Modeling of Cyber-Physical Microgrid Systems to Evaluate the Reliability and Resiliency Implications of Cyber Attacks
Bibliographic record
Abstract
The integration of cyber and physical layer of the grid has not only introduced a microscopic spectacle to observe and ensure the efficient flow of electricity but has also exposed the interdependencies of the network. These cyber-physical interdependencies are often exploited in the form of cyber-attacks that can disable a grid introducing substantial financial losses and observable social repercussions. Thus, it is important to address the impending Achilles heel by devising pragmatic approaches to comprehensibly upgrade the grid preventing huge financial and societal repercussions. In this regard, this paper proposes important methodologies in assessing the resiliency of a smart microgrid enabled distribution system in case of a cyber-attack and also steers discussion towards mitigation strategies and their influence in increasing the reliability and resiliency of the system. While doing so, it also aims to clarify the different principles of reliability and resiliency assessment. The paper describes an efficient bad-data detection strategy and its necessity in improving the reliability and resiliency of the system. The paper finds that a precipitous drop in reliability and resiliency is observed which can be effectively mitigated by the deployment of bad-data detection strategies and proposes efficient resiliency assessment methodologies to conduct similar studies.
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 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 | 0.001 |
| 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) | 0.003 | 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".