Reliability-based appraisal of Smart Grid challenges and realization
Bibliographic record
Abstract
Several studies on the migration strategies to enable the realization of various visions of Smart Grids (SGs) from the existing legacy power systems are on the anvil. A subjective treatment of `reliability' as encountered in the several existing working definitions on SGs leaves much to be desired. Only a quantification of envisioned reliability benefits and impacts can justify the rationale for embracing the SG philosophy that relies on anticipated improvement in power system reliability as one of its foundations. Identifying the scope and means to extend/revamp traditional reliability studies in light of the increasing functional interdependencies brought on by inter disciplinary technologies is a key beginning step. Towards this goal, the paper puts forward an architectural composition of SGs from a reliability perspective. Based on this, a qualitative discussion is initiated to identify the foreseeable challenges in quantitative reliability estimation. There is also an imminent need to evolve an integrated framework that can accommodate realistic reliability appraisals that will be useful in decision making processes. A proposal for a potential framework that can be expanded upon in due course of time for a comprehensive reliability evaluation of SGs is then outlined.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".