Bibliographic record
Abstract
Reliability is an important issue in power systems and historically has been assessed using deterministic criteria and indexes. However, these approaches can be, and in many cases have been, replaced by probabilistic methods that are able to respond to the actual stochastic factors that influence the reliability of the system. In the days of global, completely integrated and/or nationalized electricity supply industries, the only significant objective was the reliability seen by actual end users. Also, the system was structured in a relatively simple way such that generation, transmission, and distribution could be assessed as a series of sequential hierarchical levels. Failures at any level could cause interruptions of supply to the end user. All planning and operational criteria were intended to minimize such interruptions within economic limits. The system has been, or is being, restructured and now many individual parties are involved, often competitively, including generators, network owners, network operators, energy suppliers, regulators, as well as the end users. Each of these parties has a need to know the quality and performance of the system sector or subsector for which they are responsible. Therefore, there is now a need for a range of reliability measures; the actual measure(s) needed varying between the different system parties. This paper addresses these issues and, in particular, reviews existing approaches and how these may be used and/or adapted to suit the needs and the required indexes of the new competitive industry and the different parties associated with it.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".