Distribution System Reliability Risk Assessment Using Historical Utility Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article describes the research conducted on the use of historical performance data in assessing the financial risk for a power distribution utility in a performance based regulation (PBR) regime. The historical utility data used in this research are taken from the Canadian Electrical Association (CEA) annual reports. The objectives of this article are to examine and analyze the variations in the annual performance indices of the participating utilities including the overall indices and the cause code contributions, and to examine the possible utilization of historic utility reliability indices to create suitable reward/penalty structures in a PBR protocol. The potential financial risk analyses for these selected utilities are conducted using their historical performance data imposed on a number of possible reward/penalty structures developed in this article. An approach to recognize adverse utility performance in the form of major outage years (MOY) is developed considering the influence of the MOY performance in PBR decision making.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it