Phase Swapping for Distribution System Using Tabu Search
Bibliographic record
Abstract
Abstract: In power distribution systems, feeders frequently exhibit some imbalance between phases. Feeder imbalance occurs when the currents (Ia, Ib and Ic) of a three-phase system do not have the same magnitude at any load point along the feeder, because some phases are more heavily loaded than others. A state of imbalance in power systems may cause excessive voltage drops, unnecessary energy losses, and increased risk of feeder overload. It may also affect system power quality and electricity price. In order to correct this state of disproportion, phase balancing can be utilized. One solution to phase balancing is to swap single-phase loads from one phase to another to make the currents identical at each load point on the feeder. This technique is called phase swapping. Balancing loads in a distribution system can enhance utilities competitiveness by improving reliability and by reducing costs. The determination of the optimal swapping scheme is a non-linear problem. The efficiency of using Tabu Search to solve this non-linear phase balancing problem is demonstrated. Tabu Search is a heuristic method that enhances the performance of a basic local search technique by using a memory structure. The Tabu Search algorithm to find the optimal phase swapping scheme with the minimal cost was developed using a model from an unbalanced feeder from a typical Local
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".