Assessment of component criticality in customer delivery systems
Bibliographic record
Abstract
The customer delivery system (CDS) is defined as the component of the bulk transmission system which delivers power from the bulk transmission system to large municipalities, large industrial customers and the retail (distribution) system. The delivery system can be simple or complicated and redundancy may or may not be provided depending on the size of the load and the needs of customers connected to the system. The CDS is made up of different components such as lines, transformers, buses, breakers, etc. and these components are electrically connected together to transport electrical energy from the bulk transmission system to various load points. Depending on system planning criteria used, a single component failure may or may not affect the reliability of supply to customers. All components of the system are important and some components are more critical than others. In a competitive electricity market, owners of these systems are required by regulatory rules to maintain their systems to specific performance standards. Meeting these standards could be a challenging task for transmission owners or providers particularly if they want to maintain a good investment rate of return for their shareholders. One approach of tackling this problem is to rank components of the system in terms of their importance or criticality with respect to supply reliability. Such a ranking list can be used for various planning, operational and maintenance purposes. One of the more important purposes is to establish the most effective areas of the system to invest in to meet reliability targets. In this way, money could be spent in a more effective and efficient way on various projects. The purpose of this paper is to describe the recent study that has been performed at Hydro One to assess the component criticality in the CDS and to show how the study findings can be used in supporting investment decisions.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".