Considering the Actual Settings of Different Relay Technologies in the Same Network
Bibliographic record
Abstract
This chapter helps the industry professionals to consider the effect of relay technology in solving optimal relay coordination (ORC) problems. It aims to combine electromechanical, static, digital, and numerical relays in one network. The chapter also aims to explain the existing ORC model to deal with different relay technologies. It argues that the correct relay technology is optimized rather than pre-defined by users. The pre-defined relay type approach is more realistic because it is a network-dependent ORC solver. The goal of satisfying selectivity constraint is to ensure that each primary protective relay has enough chance to isolate the fault occurred in its zone. The clear duplication stage is completely disabled because the realistic settings of time multiplier setting and plug setting of all the relay technologies are discrete. If all the relay technologies are known and fixed before initiating the optimizer, then the process will be easier with less problem dimension.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".