Comparison of Substation Uprating Techniques Using a Novel Graphical Method Based on System Planning Constraints
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Bibliographic record
Abstract
This paper presents a comparison of different fault-current mitigation techniques used for adding an extra transformer to an existing substation whose fault-current level is close to nominal. Emphasis is placed on evaluating the net increase in substation loading capability provided by each technique while taking into account apparatus and system planning constraints. Operation under unusual peak loading conditions is investigated in terms of maximizing the use of substation assets. A graphical method based on linear programming is introduced to simplify the comparison of the techniques studied based on conventional passive technologies. The main strengths of the method are its ability to assess all techniques with a single general formulation and its possible generalization to complex problems. This paper shows that the avoided cost for reconfiguring a network and balancing the loads on the substation transformers could potentially justify more expensive techniques.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it