Towards a Customized Resiliency Framework for Smart Grid
Bibliographic record
Abstract
Introducing resiliency in a legacy power grid is one of the core mandates of smart grid architecture. Resiliency in power grid enables electrical distribution companies to maintain their services to the customers in the event of a grid failure. Its main advantages appear during the fault event: balance the power load and overcome the fault state by minimizing its impact. Although researchers have proposed many schemes for post-failure events to isolate faults (under certain conditions), yet there is no overall framework to evaluate the resiliency of the existing power grid and to improve it if required to recover from fault incidents successfully. In this paper, we propose a two-phase resiliency framework for the smart power grid. The first phase aims to assess the resiliency of a power grid. This phase provides important information regarding the weaknesses and the strengths of a power grid. Phase two will process the input of phase one to increase resiliency by employing redundant grid's elements such as power lines and poles to back up/strengthen the vulnerable components. This preventive framework increases the overall grid resiliency and enables providing an efficient self-recovery policy upon any possible power failure. To achieve this goal, we utilize graph-theoretic metrics, mainly distortion metric, to implement our proposed framework. The proposed framework is implemented and its effectiveness with link failure events is demonstrated through three IEEE bus systems: 14, 30, and 118.
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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".