Annoyance, recognition and detection of noise from a single wind turbine were studied by means of a two-stage listening experiment with 50 participants with normal hearing abilities. In-situ recordings made at close distance from a 1.8-MW wind turbine operating at 22 rpm were mixed with road traffic noise, and processed to simulate indoor sound pressure levels at LAeq 40 dBA. In a first part, where people were unaware of the true purpose of the experiment, samples were played during a quiet leisure activity. Under these conditions, pure wind turbine noise gave very similar annoyance ratings as unmixed highway noise at the same equivalent level, while annoyance by local road traffic noise was significantly higher. In a second experiment, listeners were asked to identify the sample containing wind turbine noise in a paired comparison test. The detection limit of wind turbine noise in presence of highway noise was estimated to be as low as a signal-to-noise ratio of -23 dBA. When mixed with local road traffic, such a detection limit could not be determined. These findings support that noticing the sound could be an important aspect of wind turbine noise annoyance at the low equivalent levels typically observed indoors in practice. Participants that easily recognized wind-turbine(-like) sounds could detect wind turbine noise better when submersed in road traffic noise. Recognition of wind turbine sounds is also linked to higher annoyance. Awareness of the source is therefore a relevant aspect of wind turbine noise perception which is consistent with previous research.
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http://dx.doi.org/10.1016/j.scitotenv.2013.03.095 | DOI Listing |
Heliyon
July 2024
Centre for Ultrasonic Engineering, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, G1 1XQ, UK.
This study explores the inspection of bolted connections in wind turbines, specifically focusing on the application of Phased Array Ultrasonic Testing (PAUT). The research comprises four sections: Acoustoelastic Constant calibration, high tension investigation on bolts, blind tests on larger bolts, and Finite Element Analysis (FEA) verification. The methodology shows accurate results for stress while the bolt is under operative loads, and produces a clear indication of when it is above these loads and beginning to deform.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
Health Canada, Consumer and Clinical Radiation Protection Bureau, Non-Ionizing Radiation Health Sciences Division, Ottawa, Ontario K1A 1C1, Canada.
The World Health Organization Environmental Noise Guidelines provide source-based nighttime sound level (Lnight) recommendations. For non-aircraft sources, the recommended Lnight is where the absolute prevalence of high sleep disturbance (HSD) equals 3%. The Guideline Development Group did not provide an Lnight for wind turbines due to inadequate data.
View Article and Find Full Text PDFSci Rep
January 2025
Electrical Power and Machines Department, Egyptian Chinese University, Cairo, Egypt.
This research is dedicated to improving the control system of wind turbines (WT) to ensure optimal efficiency and rapid responsiveness. To achieve this, the fuzzy logic control (FLC) method is implemented to control the converter in the rotor side (RSC) of a doubly fed induction generator (DFIG) and its performance is compared with an optimized proportional integral (PI) controller. The study demonstrated an enhancement in the performance of the DFIG through the utilization of the proposed FLC, effectively overcoming limitations and deficiencies observed in the conventional controllers, this approach significantly improved the performance of the wind turbine.
View Article and Find Full Text PDFHeliyon
January 2025
John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary.
Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30-059, Krakow, Poland.
In this study, a predictive maintenance (PdM) system focused on feature selection for the detection and classification of simulated defects in wind turbine blades has been developed. Traditional PdM systems often rely on numerous, broadly chosen diagnostic indicators derived from vibration data, yet many of these features offer little added value and may even degrade model performance. General feature selection methods might not be suitable for PdM solutions, as information regarding observed faults is often misinterpreted or lost.
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