Phase Swapping for Distribution System Using Tabu Search
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 it