scholarly journals Wind Farm Noise Management Based on Determinants of Annoyance

2022 ◽  
Author(s):  
Frits van den Berg
Keyword(s):  
Wind Farm ◽  
Sound Level ◽  
Farm Management ◽  
Environmental Sound ◽  
Management Planning ◽  
Wind Turbine Noise ◽  
Individual Perceptions ◽  
Statistical Level ◽  
Different Dimensions ◽  
Noise Management

Wind energy in Europe is aimed to grow at a steady, high pace. Wind turbine noise is an important issue for residents. Environmental noise management aims to reduce the exposure of the population, usually based on acoustics and restricted to a limited number of sources (such as transportation or industry) and sound descriptors (such as Lden). Individual perceptions are taken into account only at an aggregate, statistical level (such as percentage of exposed, annoyed or sleep-disturbed persons in the population). Individual perceptions and reactions to sound vary in intensity and over different dimensions (such as pleasure/fear or distraction). Sound level is in fact a weak predictor of the perceived health effects of sound. The positive or negative perception of the sound (source) is a better predictor of its effects. This article aims to show how the two perspectives (based on acoustics and on perception) can lead to a combined approach in the management of environmental sound. In this approach the reduction of annoyance, not primarily of level, is the main aim. An important aspect in this approach is what a sound means to people: does it lead to anxiety or worry, is it appropriate? The available knowledge will be applied to wind farm management: planning as well as operation.

Effect of Wind Turbine Noise on Workers' Sleep Disorder: A Case Study of Manjil Wind Farm in Northern Iran

2015 ◽  
Vol 14 (02) ◽  
pp. 1550020 ◽  
Author(s):  
Milad Abbasi ◽  
Mohammad Reza Monnazzam ◽  
SayedAbbolfazl Zakerian ◽  
Arsalan Yousefzadeh
Keyword(s):  
Wind Turbine ◽  
Sleep Disorder ◽  
Wind Turbines ◽  
Daytime Sleepiness ◽  
Wind Farm ◽  
Wind Farms ◽  
Sound Level ◽  
Wind Turbine Noise ◽  
The Impact

Noise from wind turbines is one of the most important factors affecting the health, welfare, and human sleep. This research was carried out to study the effect of wind turbine noise on workers' sleep disorder. For this, Manjil Wind Farm, because of the greater number of staff and turbines than other wind farms in Iran, was chosen as case study. A total number of 53 participants took part in this survey. They were classified into three groups of mechanics, security, and official. In this study, daytime sleepiness data of workers were gathered using Epworth Sleepiness Scales (ESS) was used to determine the level of daytime sleepiness among the workers. The 8-h equivalent sound level (LAeq,8h) was measured to determine the individuals' exposure at each occupational group. Finally, the effect of sound, age, and workers' experience on individuals' sleep disorder was analyzed through multiple regression analysis in the R software. The results showed that there was a positive and significant relationship between age, workers' experience, equivalent sound level, and the level of sleep disorder. When age is constant, sleep disorder will increase by 26% as per each 1 dB increase in equivalent sound level. In situations where equivalent sound level is constant, an increase of 17% in sleep disorder is occurred as per each year of work experience. Because of the difference in sound exposure in different occupational groups. The effect of noise in repairing group was about 6.5 times of official group and also 3.4 times of the security group. Sleep disorder effect caused by wind turbine noise in the security group is almost two times more than the official group. Unlike most studies on wind turbine noise that address the sleep disorder among inhabitants nearby wind farms, this study, for the first time in the world, examines the impact of wind turbine noise on sleep disorder of workers who are more closer to wind turbines and exposed to higher levels of noise. So despite all the good benefits of wind turbines, it can be stated that this technology has health risks for all those exposed to its sound. However, further research is needed to confirm the results of this study.

scholarly journals Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 170
Author(s):  
Robin Kraft ◽  
Manfred Reichert ◽  
Rüdiger Pryss
Keyword(s):  
Mobile Devices ◽  
Sound Level ◽  
Sensor Data ◽  
Environmental Sound ◽  
Mobile Crowdsensing ◽  
Sound Levels ◽  
Level Data ◽  
Combined Use ◽  
Smartphone Sensors ◽  
Ecological Momentary

The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users’ individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.

Assessment of the noise produced by wind farms with the acoustically analogous techniques without stopping the noise source

2018 ◽  
Vol 43 (2) ◽  
pp. 201-209
Author(s):  
Gino Iannace ◽  
Amelia Trematerra ◽  
Umberto Berardi
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Noise Source ◽  
Sound Field ◽  
Wind Farms ◽  
Sound Level ◽  
Acoustic Measurements ◽  
Relative Contribution

In Italy, wind turbines with a nominal power below 1 MW can be installed following simplified authorization procedures and are therefore becoming the preferred choice for promoters. The assessment of the noise of wind farms is not easy, due to economic reasons, with it being difficult to stop and assess the relative contribution of each wind turbine. Several acoustic measurements were taken inside homes located near a wind farm consisting of three wind turbines, each with a nominal power of 1 MW. The acoustic measurements were taken by placing sound level meters inside the houses at different wind speed values and wind directions. The acoustic measurements were taken using the acoustically analogous place technique. For economic reasons, the plant cannot be switched off. In this case, the sound field generated by the operation of the wind turbines was measured by placing two sound level meters in a house.

scholarly journals Multilevel Annoyance Modelling of Short Environmental Sound Recordings

2021 ◽  
Vol 13 (11) ◽  
pp. 5779
Author(s):  
Ferran Orga ◽  
Andrew Mitchell ◽  
Marc Freixes ◽  
Francesco Aletta ◽  
Rosa Ma Alsina-Pagès ◽  
...  
Keyword(s):  
Sound Source ◽  
Smart City ◽  
Online Survey ◽  
Life Forms ◽  
Sound Level ◽  
Environmental Sound ◽  
Psychoacoustic Model ◽  
Source Type ◽  
Acoustic Sensor Networks

The recent development and deployment of Wireless Acoustic Sensor Networks (WASN) present new ways to address urban acoustic challenges in a smart city context. A focus on improving quality of life forms the core of smart-city design paradigms and cannot be limited to simply measuring objective environmental factors, but should also consider the perceptual, psychological and health impacts on citizens. This study therefore makes use of short (1–2.7 s) recordings sourced from a WASN in Milan which were grouped into various environmental sound source types and given an annoyance rating via an online survey with N=100 participants. A multilevel psychoacoustic model was found to achieve an overall R2=0.64 which incorporates Sharpness as a fixed effect regardless of the sound source type and Roughness, Impulsiveness and Tonality as random effects whose coefficients vary depending on the sound source. These results present a promising step toward implementing an on-sensor annoyance model which incorporates psychoacoustic features and sound source type, and is ultimately not dependent on sound level.

Evaluation of noise levels of two micro-wind turbines using a randomised experiment

2014 ◽  
Vol 25 (1) ◽  
pp. 19-25
Author(s):  
Chantelle May Clohessy ◽  
Warren Brettanny ◽  
Gary Sharp ◽  
Frederik Vorster
Keyword(s):  
Wind Turbines ◽  
General Linear Model ◽  
Time Of Day ◽  
Sound Level ◽  
Environmental Concerns ◽  
Noise Levels ◽  
Wind Turbine Noise ◽  
Level Data ◽  
The Relationship ◽  
Noise Impacts

One of the more contentious environmental concerns of wind turbines is the wind turbine noise. This study assesses the noise impacts of two micro-wind turbines on the environment by comparing the noise generated by these turbines to traditionally accepted surrounding sounds. The sound level data was collected using a randomised experiment and fitted using a general linear model (GLM). The GLM was used to determine the relationship between the sound level generated at a given site to the time of day, the wind speed, the wind direction and a fixed predetermined distance from the sound source.

Wind Farm Management using Artificial Intelligent Techniques

2017 ◽  
Vol 7 (3) ◽  
pp. 1133 ◽  
Author(s):  
Boualam Benlahbib ◽  
Farid Bouchafaa ◽  
Saad Mekhilef ◽  
Noureddine Bouarroudj
Keyword(s):  
Performance Management ◽  
Management System ◽  
Wind Farm ◽  
Reactive Power ◽  
Optimization Methods ◽  
Pi Controller ◽  
Farm Management ◽  
Wind Generators ◽  
Management Algorithm ◽  
Induction Generators

This paper presents a comparative study between genetic algorithm and particle swarm optimization methods to determine the optimal proportional–integral (PI) controller parameters for a wind farm management algorithm. This study primarily aims to develop a rapid and stable system by tuning the PI controller, thus providing excellent monitoring for a wind farm system. The wind farm management system supervises the active and reactive power of the wind farm by sending references to each wind generator. This management system ensures that all wind generators achieve their required references. Furthermore, the entire management is included in the normal controlling power set points of the wind farm as designed by a central control system. The performance management of this study is tested through MATLAB/Simulink simulation results for the wind farm based on three doublyfed induction generators

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