Response to Noise Emitted by Wind Farms in People Living in Nearby Areas
Małgorzata Pawlaczyk-ŁuszczyńskaKamil ZaborowskiAdam DudarewiczMałgorzata Zamojska-DaniszewskaMałgorzata Waszkowska
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The aim of this study was to evaluate the perception and annoyance of noise from wind turbines in populated areas of Poland. A questionnaire inquiry was carried out among 517 subjects, aged 18–88, living within 204–1726 m from the nearest wind turbine. For areas where respondents lived, A-weighted sound pressure levels (SPLs) were calculated as the sum of the contributions from the wind power plants in the specific area. It has been shown that the wind turbine noise at the calculated A-weighted SPL of 33–50 dB was perceived as annoying or highly annoying by 46% and 28% of respondents, respectively. Moreover, 34% and 18% of them said that they were annoyed or highly annoyed indoors, respectively. The perception of high annoyance was associated with the A-weighted sound pressure level or the distance from the nearest wind turbine, general attitude to wind farms, noise sensitivity and terrain shape (annoyance outdoors) or road-traffic intensity (annoyance indoors). About 48–66% of variance in noise annoyance rating might be explained by the aforesaid factors. It was estimated that at the distance of 1000 m the wind turbine noise might be perceived as highly annoying outdoors by 43% and 2% of people with negative and positive attitude towards wind turbines, respectively. There was no significant association between noise level (or distance) and various health and well-being aspects. However, all variables measuring health and well-being aspects, including stress symptoms, were positively associated with annoyance related to wind turbine noise.Keywords:
Annoyance
As high-wind energy potential regions are less common now; it is becoming more crucial to generate wind energy in places where the wind velocity is light to moderate. This study uses the WERA model to estimate and compare the performances of 4 commercial wind turbines under low power density wind regimes. Wind turbines of 5 kW-rated capacity, from four prominent manufacturers, were considered in the study. The turbine's velocity power response and the site's Rayleigh probability density of wind velocity were used to model these turbines' performance at four typical sites with different average wind speeds in Kerala namely Thiruvananthapuram, Kollam, Kottayam, Pathanamthitta. The turbine's performances are quantified with the energy production and capacity factor at different locations. It was revealed that the turbine's velocity power response is a crucial factor influencing the system performance. Reduction in the cut-in and rated wind speeds seems to improve the system's output in areas with low wind velocity.
Rayleigh distribution
Small wind turbine
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Annoyance
QUIET
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Wind‐turbine operations are associated with bat mortality worldwide; minimizing these fatalities is critically important to both bat conservation and public acceptance of wind‐energy development. We tested the effectiveness of raising wind‐turbine cut‐in speed – defined as the lowest wind speed at which turbines generate power to the utility system, thereby reducing turbine operation during periods of low wind speeds – to decrease bat mortality at the Casselman Wind Project in Somerset County, Pennsylvania, over a 2‐year period. Observed bat mortality at fully operational turbines was, on average, 5.4 and 3.6 times greater than mortality associated with curtailed (ie non‐operating) turbines in 2008 and 2009, respectively. Relatively small changes to wind‐turbine operation resulted in nightly reductions in bat mortality, ranging from 44% to 93%, with marginal annual power loss (≤ 1% of total annual output). Our findings suggest that increasing turbine cut‐in speeds at wind facilities in areas of conservation concern during times when active bats may be at particular risk from turbines could mitigate this detrimental aspect of wind‐energy generation.
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Heipang in Plateau State is classified under moderate wind speed regime in Nigeria, thus, has high potential for wind electricity generation. Due to high cost, it is difficult to design a wind turbine for a particular site; therefore, the designer of the wind energy project has to choose from the available options in markets, which come in different sizes and speed characteristics. This paper is aimed at evaluating the performance of some selected wind turbines in Heipang wind speed regime. The method used is based on wind speed analysis and computation of the capacity factors of wind turbines expressed as a product of wind turbines’ power output models and probability distribution of wind speed regime of Heipang; and the total annual energy generation of the wind turbines using Wind Energy Resources Analysis (WERA) software. Results showed that Heipang has an annual mean wind speed of 6.3 m/s and its wind speed regime best fitted into Weibull probability distribution function with average Weibull shape and scale parameters of 3.05 and 7.03 m/s respectively at 10 m height. For small (<10kW), medium (10kW-250kW) and large (>250kW) wind turbines classifications; WT4, WT14 and WT25 have the highest capacity factors of 0.61, 0.7 and 0.53 respectively and WT2, WT22, WT27 have the highest total annual energy generation of 0.0054, 1.12 and 4.03 GWh/year respectively. In conclusion, Heipang has high wind speed potential for wind power technology and wind turbines with higher annual energy generation are better options for selections for wind power generation applications
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Modern utility-scale wind farms consist of a large number of wind turbines. In order to improve the power generation efficiency of wind turbines, accurate quantification of power generation levels of multi-turbines is critical, in both wind farm design and operational controls. One challenging issue is that the power output levels of multiple wind turbines are different, due to complex interactions between turbines, known as wake effects. In general, upstream turbines in a wind farm absorb kinetic energy from wind. Therefore, downstream turbines tend to produce less power than upstream turbines. Moreover, depending on weather conditions, the power deficits of downstream turbines exhibit heterogeneous patterns. This study proposes a new statistical approach to characterize heterogeneous wake effects. The proposed approach decomposes the power outputs into the average pattern commonly exhibited by all turbines and the turbine-to-turbine variability caused by multi-turbine interactions. To capture the wake effects, turbine-specific regression parameters are modeled using a Gaussian Markov random field. A case study using actual wind farm data demonstrates the proposed approach's superior performance.
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The aim of the current paper is to present an approach to a wind turbine selection based on an annual wind measurements. The proposed approach led to a choice of an optimal device for the given wind conditions. The research was conducted for two potential wind farm locations, situated on the north of Poland. The wind measurements pointed out a suitability of the considered localizations for a wind farm development. Six types of wind turbines were investigated in each localization. The power of the wind turbines were in the range of 2.0 to 2.5 MW and with a medium size of the rotor being in the range of 82 to 100 m. The purpose of the research was to indicate a wind turbine with the lowest sensitivity to the variation of wind speed and simultaneously being most effective energetically. The Weibull density distribution was used in the analyses for three values of a shape coefficients k. The energy efficiency of the considered turbines were also assessed. In terms of the hourly distribution of the particular wind speeds, the most effective wind turbines were those with a nominal power of 2 MW, whereas the least effective were those with the nominal power of 2.3–2.5 MW. The novelty of the proposed approach is to analyze the productivity for many types of wind turbines in order to select the one which is the most effective energy producer. The analyses conducted in the paper allowed to indicate a wind turbine which generates the highest amount of energy independently on the wind speed variation.
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This paper examines the effect of different wind turbine classes on the electricity production of wind farms in two areas of Cyprus Island, which present low and medium wind potentials: Xylofagou and Limassol. Wind turbine classes determine the suitability of installing a wind turbine in a particulate site. Wind turbine data from five different manufacturers have been used. For each manufacturer, two wind turbines with identical rated power (in the range of 1.5 MW–3 MW) and different wind turbine classes (IEC II and IEC III) are compared. The results show the superiority of wind turbines that are designed for lower wind speeds (IEC III class) in both locations, in terms of energy production. This improvement is higher for the location with the lower wind potential and starts from 7%, while it can reach more than 50%.
Installation
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With the rise of new energy power generation technology, the installed capacity of wind turbines continues to increase. At the same time, the potential faults of wind turbines have also increased with the increase of wind turbines. Therefore, early prediction of potential faults of wind turbines and ensuring the safe and stable operation of wind turbines is of great significance for improving power generation efficiency and reducing maintenance costs. In order to realize the fault early warning of the main bearing of the wind turbine, an early warning method of the main bearing of the wind turbine based on Stacked Auto encoder (SAE) is proposed.
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This paper focus is on the small wind turbines resource potential estimation. Assessment is done for seven selected small wind turbines and one measured set of wind speed data with the micropower optimization modeling tool HOMER. Goal was to investigate how estimated energy production and economical parameters are sensitive to the selection of small wind turbine. Selected turbines have similar rated power, but different blades diameter and aerodynamic characteristics. Energy production was quantified for one year with hourly resolution. Results from all different wind turbines were compared on the power production base, and on the economical base. Two sensitivity cases related to the wind speed and installation lifetime were also simulated. Results are showing significant importance of the small wind turbine selection for the both total energy production and economical feasibility. This makes small wind turbine characteristics such as reliability and power curves testing very important.
Small wind turbine
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PP-29-180 Background/Aims: Subjective annoyance from exposure to low frequency noise is more prevalent because of bulk machine or facility installation in indoor acoustic quality assessment. The purpose of this study is to propose criteria for the judgment of low frequency noise annoyance in the integrated circuit industry on the basis of RC Mark II noise rating by octave-band frequency analysis. Methods: On-site survey of octave-band frequency in the range of 1–16,000 Hz was measured by sound analyzer according to the locations of workers' complaint. All these data were used to figure out A-weighted; C-weighted sound pressure levels (20–20,000 Hz); room criteria; and sound pressure of LF (16–63 Hz), MF (125–500 Hz), and HF (1000–4000 Hz). Results: The difference between C-weighted and A-weighted sound pressure levels greater than 8 dB and low frequency noise (its spectrum in 16–63 Hz) above 65 dB indicated significantly subjective annoyance of exposed worker. Conclusion: In this study, the following criteria were proposed to find the source of workers' annoyance caused by exposure to low-frequency noise. C-weighted sound pressure level is 10 dB greater than A-weighted sound pressure level. Noise in the low frequency range (16–63 Hz) is greater than 65 dB. LF ≥ MF ≥ HF.
Annoyance
Octave band
Frequency band
Frequency analysis
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