Mathematical Schemes to Linearize Operating Times of Overcurrent Relays by Sequentially Fixing Plug Settings and Time Multiplier Settings
Bibliographic record
Abstract
Nowadays, many studies have been presented in the literature to solve various optimal relay coordination (ORC) problems. These highly constrained nonlinear nonconvex optimization problems are commonly solved using evolutionary algorithms (EAs). For online applications, where the processing speed is very crucial, some studies suggest using different types of linear programming (LP), including simplex and interior-point methods, while others suggest hybridizing both EAs and LPs. For these approaches, the ANSI/IEEE and IEC/BS standard models used to calculate the operating times of overcurrent relays (OCRs) are linearized by just fixing their plug settings (PS) and varying time multiplier settings (TMS). This study presents another way to linearize these models by doing the opposite where TMS are fixed and PS are varied for both standard models. These linearized models can be used to effectively tune the objective functions of ORC problems to achieve both performance criteria; the solution quality and the processing speed.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".