Genetic algorithm technique on wind turbine and sensitive equipment placement against lightning
Bibliographic record
Abstract
Lightning as a natural phenomenon represents directly and indirectly a risk for wind turbines and other objects as the transient voltages may reach dangerous levels for people, equipment, and transmission lines. It is important to consider lightning and its consequences on the placement of wind turbines, buildings, and other sensitive electrical installations. The objective of this study is to optimize the placement of a wind farm for minimum lightning occurrence on the turbines. The method is also applied to optimize the location of any other sensitive installation for minimum electromagnetic interference from lightning current in any geographic area. The process involves gathering accurate lightning data information for the interested area from North American Lightning Detection Network (NALDN) over a period of time and using the genetic algorithm optimization technique using MATLAB to determine the wind farm location and other sensitive equipment placement. It is found that genetic algorithm optimization technique produced results which are very consistent considering different scenarios. The findings in this study could help wind farm designers in addition to other criteria for energy collecting efficiency of wind turbines in order to determine the best location of wind farms.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".