Bibliographic record
Abstract
Society is becoming increasingly dependent on a cost-effective reliable electric power supply. Unreliable electric power supplies can be extremely costly to electric utilities and their customers. Predictive reliability assessment combines historical outage data and mathematical models to estimate the performance of specific network and system configurations [e.g., IEEE Std. 493-1990]. This paper is concerned with the value-based assessment of proposed modifications to an existing industrial distribution system configuration to minimize the costs of interruptions to both the utility and its industrial customers. This paper presents a series of case studies of an actual industrial load area supplied by two feeder circuits originating from two alternate substations. Each case study reveals the impact on the cost of industrial load point interruptions and the frequency and duration of industrial load point interruptions when various system constraints (e.g., ideal and nonideal protection coordination schemes, substation capacity restrictions, etc.) are imposed on the distribution system. The paper discusses in some detail the variance in reliability performance indices and their impact on the cost of load point interruptions. A basic conclusion of this paper is that expansion plans of an industrial distribution system can be optimized in terms of reliability by using an economic criterion in which the sum of both the industrial facility interruptions and the utility system costs are minimized.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".