Bibliographic record
Abstract
In recent years, many transmission companies have established sets of performance measures for their customer delivery systems. Such measures are used for various purposes such as investment, comparison of performance, etc. When it comes to transmission stations, there are no performance measures in place at the present time that reflect the performance of a station as a whole. In addition, outages to station-related equipment may have different consequences. A station related-outage can affect customers directly in case of a load station or the transfer capability between two locations in the system in case of a transmission station. The asset manager of a transmission system needs to use any available data on asset performance, conditions, utilization and available analysis tools in driving business decisions. He or she may need to know, for example, how stations of different sizes with the same voltage level are compared from the different point of views such as equipment performance, station utilization, station security and personnel safety. He or she may need to identify the worst performing stations (outliers) so that fund may be allocated appropriately. Also, performance measures can help quantify benefits of investments over time. This paper proposes some new quantitative performance measures for transmission and load stations. The proposed measures covers a variety of station related performance aspects such as reliability, utilization, security and safety to personnel. The new measures was used to determine relative station performance, ranking of stations, transmission station component unavailability for the entire transmission network and performance trends. The new performance measures was illustrated by examples.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".