Analysis and Mitigation of Current Unbalance Due to Induction in Heavily Loaded Multicircuit Power Lines
Bibliographic record
Abstract
High current unbalance may exist due to induction between circuits in a multicircuit right-of-way, especially when some of the circuits have heavy loads. This paper studies the problem and presents a mitigation technique to reduce the current unbalance. A brief analytical derivation is carried out to show the principle of the mitigation technique, which is based on transposition of the lines. The mitigation technique is then applied to an actual right-of-way with six circuits. Comparisons have been made between the untransposed circuits and the transposed circuits. It is shown that the unbalance level can be reduced significantly by appropriate phase transpositions based on the proposed principle. The mitigation technique presented in this paper can be applied to other right-of-way configurations to reduce current unbalances, therefore ensuring stable operation of the heavily loaded power lines.
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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 | 0.001 | 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 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".