Design of economic <i>X</i> chart for monitoring electric power loss through transmission and distribution system
Bibliographic record
Abstract
This article presents a model for the optimisation design of the X¯ chart based on Duncan’s model for identifying unusual power loss through transmission and distribution system so that an immediate action can be taken to maintain the power network stability. The model optimises the chart parameters in order to minimise the mean cost of power loss (MLC) under random process shifts (e.g. mean shifts), and ensures that the false alarm probability of the control chart will not exceed the allowable level and extra inspection resources (e.g. operators and measurement instruments) will be avoided. A comparative study based on fractional factorial experiment shows that, from an overall viewpoint, the optimal X¯ chart reduces the MLC by about 40% compared to the traditional X¯ chart. The effects of design specifications on the charting parameters and optimal MLC of the proposed chart are also investigated through sensitivity analysis. Finally, the design and application of the proposed chart is illustrated through an example. In general, this article will help reduce the cost of power loss and broaden the literature on the application of control chart in the service organisations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".