Optimization study of cable wiring scheme for photovoltaic power plant based on TS algorithm in the era of low-carbon development
Bibliographic record
Abstract
As an important clean energy project, the optimization of the construction and operation of photovoltaic (PV) power plants is crucial in the context of the global active promotion of low-carbon development.This paper focuses on the optimization of cable wiring scheme for PV power plants based on the taboo search (TS) algorithm.A mathematical model is established by comprehensively considering the constraints such as power loss objective and tidal current calculation in the wiring optimization process.The wild dog optimization algorithm is improved using the Levy light algorithm, and the initialization phase of the taboo search algorithm is improved by the improved wild dog optimization algorithm, and the established cabling optimization model is solved using the improved taboo search algorithm (LDOA-TS).The experimental results show that the LDOA-TS algorithm has a signi icant performance advantage over other algorithms in the model solving process.At the same time, the simulation results obtained from the optimization model in this paper are basically consistent with the actual wiring pattern under different working conditions.And through the model of this paper for cable optimization wiring compared to the original wiring scheme in the point cable length and power loss were reduced by 30.30% and 49.95%, to meet the constraints at the same time to effectively achieve the model objectives, and has obvious economic bene its, in line with the needs of the low-carbon era of photovoltaic power plant construction and operation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".