Canadian experience in the collection of transmission and distribution component unavailability data
Bibliographic record
Abstract
Equipment and system performance data are usually collected for two basic reasons. The first, and possibly the most obvious reason, is to assess past performance. The second reason is to provide the required information to estimate future performance. Consistent collection of data is essential as it forms the input to relevant reliability models, techniques and equations. Consistent data are required to continuously monitor the performance of an electric power system and to measure its ability to provide reliable service to its customers. Many utilities have established comprehensive procedures for assessing the performance of their systems. In Canada, these procedures have been formulated through the Canadian Electricity Association (CEA). The CEA is an organization for exchanging information on technical, marketing and management problems of mutual interest to its members. In 1975, CEA adopted a proposal to create a facility for centralized collection, processing and reporting of reliability and outage statistics for electric generation, transmission and distribution equipment. The outage statistics made available through this collection and analysis process provide the requisite data to evaluate the reliability of generation, transmission and distribution systems. This paper briefly illustrates the philosophies adopted by Canadian utilities in the collection of component and system outage data. It also presents a summary of the transmission and distribution component unavailability data in the CEA database
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.045 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".