Annoyance and Partial Masking of Wind Turbine Noise from Ambient Sources

Current literature suggests that wind turbine noise is more annoying than transportation noise. To date, however, it is not known which acoustic characteristics of wind turbines alone, i.e., without effect modifiers such as visibility, are associated with annoyance. The objective of this study was therefore to investigate and compare the short-term noise annoyance reactions to wind turbines and road traffic in controlled laboratory listening tests. A set of acoustic scenarios was created which, combined with the factorial design of the listening tests, allowed separating the individual associations of three acoustic characteristics with annoyance, namely, source type (wind turbine, road traffic), A-weighted sound pressure level, and amplitude modulation (without, periodic, random). Sixty participants rated their annoyance to the sounds. At the same A-weighted sound pressure level, wind turbine noise was found to be associated with higher annoyance than road traffic noise, particularly with amplitude modulation. The increased annoyance to amplitude modulation of wind turbines is not related to its periodicity, but seems to depend on the modulation frequency range. The study discloses a direct link of different acoustic characteristics to annoyance, yet the generalizability to long-term exposure in the field still needs to be verified.

Can road traffic mask sound from wind turbines? Response to wind turbine sound at different levels of road traffic sound

Despite their alignment with sustainable development principles, wind farms often provoke controversy and misinformation, particularly regarding the noise they produce and their potential impact on human functioning. Concerns have been raised about the possible effects of this noise on irritation levels, psychological well-being, and cognitive functioning. Yet, there is still a lack of controlled, comprehensive studies that could substantiate these concerns. Here, a cognitive neuroscience approach is proposed to experimentally and multifactorial explore wind turbine noise’s impact on various mental functions. We used recordings from an actual wind turbine to investigate its effects on the dynamics of brain waves crucial for complex cognitive tasks, as well as on sustained attention and inductive reasoning in healthy adult volunteers. We also tested the subjective evaluation of the stress induced by wind turbine noise and the annoyance it causes. Control conditions included silence and road traffic noise (participants were blind to the nature of acoustic variables). The findings of this pilot study reveal that short-term exposure to wind turbine noise with a sound pressure level corresponding to the real-world situation (i.e., 65 dB SPL) does not adversely affect any of the examined cognitive functions and is not perceived as more stressful or bothersome than road traffic noise. Furthermore, we utilized various psychological scales and found that even tendencies towards rumination or reduced capacity for reflection and ambiguity tolerance did not lead to maladaptive perceptions of wind turbine noise and, therefore, to a state that might indirectly influence mental functioning. Although these results cannot be generalized, they support the concept that the interlinkage between exposure to wind turbine noise and human cognitive functioning is not a cause-and-effect relationship. We discuss the mediating role of socially constructed beliefs about wind farms in this interrelation. We also indicate how important the use of a cognitive neuroscience approach in future research may be for an objective assessment of the impact of wind farms on human cognition.

Variables associated with wind turbine noise annoyance and sleep disturbance

Noise from wind turbines is of concern in the planning process of new wind farms, and accurate estimations of immission noise levels at residents nearby are required. Sound propagation from wind turbine to receiver could be modelled by a simplified standard model assuming constant meteorological conditions, by an engineering method taking atmospheric and ground propagation conditions into account, or by a more exact model. Epidemiological studies have found a higher frequency of annoyance due to wind turbine noise than to other community noise sources at equal noise levels, indicating that the often used simplified model is not sufficient. This paper evaluates the variation of immission sound levels under the influence of meteorological variation and explores if the prediction of levels could be improved by taking the effect of wind speed on sound propagation into account. Long-term sound recordings and measurements at a distance of 530 m from a wind turbine show that the simplified standard model predicts the average sound pressure levels satisfactorily under downwind conditions, and that a more complex propagation model might not be needed for wind turbine noise at a relatively short distance. Large variations of sound immission levels at the same wind speed were however present. Statistical analysis revealed that these variations were influenced by meteorological parameters, such as temperature, static pressure and deviation from ideal downwind direction. The overall results indicate that meteorological factors influence the noise generated by the wind turbine rather than the sound propagation.

Prediction of wake and output power for an offshore wind turbine under various wind conditions is essential to maximize energy generation and minimize wake effects. In previous studies, deep learning (DL) models developed using high-fidelity computational fluid dynamics (CFD) simulation results have demonstrated high accuracy and efficiency in predicting the wake characteristics and output power of offshore wind turbines. However, these existing DL models were typically developed for specific types of wind turbines, which cannot be directly used for a new turbine type. Development of a new DL model relying solely on simulation data from a new wind turbine would waste the valuable information contained in previous datasets and existing DL models, leading to increased computational costs for data collection. To address this issue, this paper aims to leverage a previously established simulation dataset and a pre-trained DL model for a wind turbine to develop a new model for another type of wind turbine. The proposed method requires only a small number of new simulations for model development using transfer learning with fine-tuning and model-agnostic meta-learning (MAML), thereby enhancing computational efficiency. The results demonstrate that the models developed using fine-tuning and MAML achieve lower prediction errors and less model training time compared to those trained directly on new wind turbine data. Furthermore, the model trained using MAML outperforms the fine-tuning model when the dataset size is extremely small, while the fine-tuning model has lower prediction errors with relatively larger datasets.

Objectives: To evaluate the prevalence of perception and annoyance due to wind turbine noise among people living near the turbines, and to study relations between noise and perception/annoyance, with focus...

The Canadian Environmental Assessment Act (CEAA) requires certain projects with federal government triggers to undergo an environmental assessment before receiving federal government approval. On request under CEAA, Health Canada provides advice on the health effects of noise to responsible authorities for wind turbine projects. The advice that Health Canada provides on the health effects of noise is generally based only on well-accepted scientific evidence for a link between noise exposure and health. For quiet rural areas, in which annoyance reactions towards intruding noise may be augmented, this paper proposes noise mitigation if predicted wind turbine noise levels exceed 45 dBA at noise sensitive receptors. In this proposal, a cautious approach is adopted by using predicted noise levels that are evaluated at the wind speed that produces the highest wind turbine noise, and background noise is evaluated in calm winds. This accounts for sheltering by obstructions. Wind speed gradient effects related to stable atmospheric conditions are also accounted for with this approach. The proposal is based on predicted project-noise related changes in long-term high annoyance, rattle and sleep disturbance. Noise mitigation for wind turbine construction noise is proposed based on potential for expectation of complaints.

Despite increasing concern about low-frequency noise of modern large horizontal-axis wind turbines (HAWTs), few studies have focused on its origin or its prediction methods. In this paper, infra- and low-frequency (the ILF) wind turbine noise are closely examined and an efficient method is developed for its prediction. Although most previous studies have assumed that the ILF noise consists primarily of blade passing frequency (BPF) noise components, these tonal noise components are seldom identified in the measured noise spectrum, except for the case of downwind wind turbines. In reality, since modern HAWTs are very large, during rotation, a single blade of the turbine experiences inflow with variation in wind speed in time as well as in space, breaking periodic perturbations of the BPF. Consequently, this transforms acoustic contributions at the BPF harmonics into broadband noise components. In this study, the ILF noise of wind turbines is predicted by combining Lowson’s acoustic analogy with the stochastic wind model, which is employed to reproduce realistic wind speed conditions. In order to predict the effects of these wind conditions on pressure variation on the blade surface, unsteadiness in the incident wind speed is incorporated into the XFOIL code by varying incident flow velocities on each blade section, which depend on the azimuthal locations of the rotating blade. The calculated surface pressure distribution is subsequently used to predict acoustic pressure at an observing location by using Lowson’s analogy. These predictions are compared with measured data, which ensures that the present method can reproduce the broadband characteristics of the measured low-frequency noise spectrum. Further investigations are carried out to characterize the IFL noise in terms of pressure loading on blade surface, narrow-band noise spectrum and noise maps around the turbine.

The aim of the present study is to establish exposure-response relationships reflecting the percentage highly annoyed (%HA) as functions of road traffic, railway, and aircraft noise exposure, measured as day-evening-night level (Lden), as well as to elucidate the degree to which the acoustic indicator Intermittency Ratio (IR), which reflects the “eventfulness” of a noise situation, predicts noise annoyance. We conducted a mixed-mode representative population survey in a stratified random sample of 5592 residents exposed to transportation noise all over Switzerland. Source-specific noise exposure was calculated for each floor and each façade based on comprehensive traffic data. Noise annoyance was measured using the ICBEN 11-point scale. The survey was carried out in 4 waves at different times of the year. We hypothesized that in addition to Lden, the effects of noise on annoyance can be better explained when also considering the intensity of short-term variations of noise level over time. We therefore incorporated the acoustic indicator IR in the statistical models. For all noise sources, results revealed significant associations between Lden and %HA after controlling for confounders and independent predictors such as IR (measured over 24 h), exposure to other transportation noise sources, sex and age, language, home ownership, education level, living duration, temperature, and access to a quiet side of the dwelling. Aircraft noise annoyance scored markedly higher than annoyance to railway and road traffic noise at the same Lden level. Railway noise elicited higher percentages of highly annoyed persons than road traffic noise. Results furthermore suggest that for road traffic noise, IR has an additional effect on %HA and can explain shifts of the exposure-response curve of up to about 6 dB between low IR and high IR exposure situations, possibly due to the effect of different durations of noise-free intervals between events. For railway and aircraft noise annoyance, the predictive value of IR was limited.

Abstract. Wind turbine noise propagation in a hilly terrain is studied through numerical simulation in different scenarios. Linearized Euler equations are solved in a moving frame that follows the wavefront, and wind turbine noise is modeled with an extended moving source. We employ large-eddy simulations to simulate the flow around the hill and the wind turbine. The sound pressure levels (SPLs) obtained for a wind turbine in front of a 2D hill and a wind turbine on a hilltop are compared to a baseline flat case. First, the source height and wind speed strongly affect sound propagation downwind. We find that topography influences the wake shape, inducing changes in the sound propagation that drastically modify the SPL downwind. Placing the turbine on the hilltop increases the average sound pressure level and amplitude modulation downwind. For the wind turbine placed upstream of a hill, a strong shielding effect is observed. But, because of the refraction by the wind gradient, levels are comparable with the baseline flat case just after the hill. Thus, considering how terrain topography alters the flow and wind turbine wake is essential to accurately predict wind turbine noise propagation.

Tonality evaluation of wind turbine noise by filter-segmentation

Amplitude modulation in wind turbine noise refers to the distinct periodic variation in amplitude which can be perceived more than a constant level. A method for rating amplitude modulation in wind turbine noise, intended for noise in 1/3 octave bands between 50 and 800 Hz, is described in Final Report A Method for Rating Amplitude Modulation in Wind Turbine Noise 9 Aug 2016 Version 1, Institute of Acoustics IOA Noise Working Group (Wind Turbine Noise) Amplitude Modulation Working Group. This paper examines the measurement and rating of amplitude modulation in wind turbine noise below 50 Hz, below the intended range of the method described in the Institute of Acoustics report. A tonal analysis as per ETSU-R-97 is also made, and the results compared.

Effect of nocturnal road traffic noise exposure and annoyance on objective and subjective sleep quality

This paper investigates the influence of atmospheric turbulence on the propagation of wind turbine noise using an aeroacoustic source model coupled with a parabolic equation propagation model. Sets of simulations with and without atmospheric turbulence are performed, allowing the determination of a simple formulation that quantifies the uncertainties of the A-weighted sound pressure level (SPL) when the modelling does not account for atmospheric turbulence. For the case study, the results show that atmospheric turbulence has a negligible effect on SPL up to 800 m from the wind turbine, even under upwind conditions. While the conclusions are specific to this case study, the method appears promising for simplifying the calculation of atmospheric turbulence effect in wind turbine noise studies. A freely accessible online application has been developed to present additional results.