Computationally Efficient Modeling of Resistance-Grounded Electrically Short DC Cables
Bibliographic record
Abstract
This paper introduces a novel approach for computationally efficient modeling of resistance-grounded electrically short DC cables through mathematical conductor elimination. The Kron reduction technique for conductor elimination is only applicable to solidly grounded cables. The proposed approach enables the application of the Kron reduction technique for the elimination of resistance-grounded conductors by incorporating the effects of the externally connected grounding resistances into the Per-Unit-Length (PUL) parameters. As a result, the dimensions of the matrices representing the PUL parameters are reduced, while their accuracies and frequency dependencies are maintained. The resulting parameters can be utilized in any cable model. The proposed conductor elimination approach is applied to the Frequency-Dependent Phase (FDP) model of PSCAD, a lumped frequency-dependent parameter model, and the PI model, and evaluated through various simulation studies. The results indicate that the proposed approach significantly enhances the computational efficiencies of these models without adversely affecting their accuracies.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".