Noise Polution Prevention in Wind Turbines: Status and Recent Advances
Bibliographic record
Abstract
The global trend towards sustainability has led to increased interest in alternative power sources to coal and other fossil fuels. One of these sustainable sources is wind energy, which we can harness through wind turbines. However, a significant hindrance preventing the widespread use of wind turbines is the noise they produce. This study reviews recent advances in the area of noise pollution from wind turbines. To date, there have been many different noise control studies. In order to successfully reduce or prevent the noise generated, the sources of noise must be identified. Two major sources of noise are present during operation: mechanical noise and aerodynamic noise. Mechanical noise generally originates from the many different components within the wind turbine, such as the generator, the hydraulic systems and the gearbox. Different mechanical noise prevention strategies such as vibration suppression, vibration isolation and fault detection techniques are presented in this paper. Aerodynamic noise prevention strategies are also discussed. Aerodynamic noise is the dominant source of noise from wind turbines, with a sound power level of 99.2 dB A. Breaking this noise source down, the largest contributor to aerodynamic noise comes from the trailing edge of wind turbine blades. Strategies for reducing aerodynamic noise include adaptive solutions and wind turbine blade modification methods. There are a number of adaptive noise reduction solutions including varying the speed of rotation of the blades and increasing the pitch angle. Although such strategies have been successfully implemented for noise reduction purposes, they can cause significant power loss. Therefore, alternative methods of adaptive solutions are sought. Blade modification methods such as adding serrations have proven to be beneficial in reducing noise without any power loss. The aim of this paper is to critically analyse and compare the different methods currently being implemented and investigated to reduce noise production from wind turbines, with a focus on the noise generated from the trailing edge.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".