Design of economic <i>X</i> chart for monitoring electric power loss through transmission and distribution system
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
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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