Onshore industrial wind turbine locations for the United States

Diffendorfer, James E.; Compton, Roger H.; Kramer, Louisa; Ancona, Zach; Norton, Donna E.

doi:10.3133/ds817

Cited by 22 publications

(26 citation statements)

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“…As the actual operational parameters of the RMWF's WTs are not publicly available, we cannot corroborate that the spectral characteristics of the observed signal correspond to the actual operation parameters of the WTs. However, using equation and the publicly available information [ Diffendorfer et al ., ], we determined ω = 17.58 rpm, which is within the range of ω r for the RMWF's WTs (Table ). We cannot distinguish with the present instrumentation if the signal is TS and/or amplitude modulation of other high‐frequency WT‐sound types (with sufficiently low frequency to support long‐distance propagation) with similar spectral characteristics (peaks at the blade‐passing frequency).…”

Section: Discussionmentioning

confidence: 85%

On infrasound generated by wind farms and its propagation in low‐altitude tropospheric waveguides

et al. 2015

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Infrasound from a 60‐turbine wind farm was found to propagate to distances up to 90 km under nighttime atmospheric conditions. Four infrasound sensor arrays were deployed in central New Mexico in February 2014; three of these arrays captured infrasound from a large wind farm. The arrays were in a linear configuration oriented southeast with 13, 54, 90, and 126 km radial distances and azimuths of 166°, 119°, 113°, and 111° from the 60 1.6 MW turbine Red Mesa Wind Farm, Laguna Pueblo, New Mexico, USA. Peaks at a fundamental frequency slightly below 0.9 Hz and its harmonics characterize the spectrum of the detected infrasound. The generation of this signal is linked to the interaction of the blades, flow gradients, and the supporting tower. The production of wind‐farm sound, its propagation, and detection at long distances can be related to the characteristics of the atmospheric boundary layer. First, under stable conditions, mostly occurring at night, winds are highly stratified, which enhances the production of thickness sound and the modulation of other higher‐frequency wind turbine sounds. Second, nocturnal atmospheric conditions can create low‐altitude waveguides (with altitudes on the order of hundreds of meters) allowing long‐distance propagation. Third, night and early morning hours are characterized by reduced background atmospheric noise that enhances signal detectability. This work describes the characteristics of the infrasound from a quasi‐continuous source with the potential for long‐range propagation that could be used to monitor the lower part of the atmospheric boundary layer.

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Section: Discussionmentioning

confidence: 85%

On infrasound generated by wind farms and its propagation in low‐altitude tropospheric waveguides

et al. 2015

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“…Turbine location data were from Diffendorfer et al (2014). Winter and summer mortality from wind turbines only considered the mortality from the cell containing the colony because of the species small home range during non-migratory seasons (Pruitt & TeWinkel, 2007).…”

Section: Methodsmentioning

confidence: 99%

“…“Clark et al observations” are capture data from Clark, Bowles & Clark (1987) “Hibernacula” data refers the winter hibernacula data from the US Fish and Wildlife Service. “Wind turbine data (2014)” comes from Diffendorfer et al (2014). The white-to-blue color gradient depicts low to high suitability from the occurrence model.…”

Section: Introductionmentioning

confidence: 99%

Effects of wind energy generation and white-nose syndrome on the viability of the Indiana bat

Erickson

Thogmartin

Diffendorfer

et al. 2016

PeerJ

Self Cite

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Wind energy generation holds the potential to adversely affect wildlife populations. Species-wide effects are difficult to study and few, if any, studies examine effects of wind energy generation on any species across its entire range. One species that may be affected by wind energy generation is the endangered Indiana bat (Myotis sodalis), which is found in the eastern and midwestern United States. In addition to mortality from wind energy generation, the species also faces range-wide threats from the emerging infectious fungal disease, white-nose syndrome (WNS). White-nose syndrome, caused by Pseudogymnoascus destructans, disturbs hibernating bats leading to high levels of mortality. We used a spatially explicit full-annual-cycle model to investigate how wind turbine mortality and WNS may singly and then together affect population dynamics of this species. In the simulation, wind turbine mortality impacted the metapopulation dynamics of the species by causing extirpation of some of the smaller winter colonies. In general, effects of wind turbines were localized and focused on specific spatial subpopulations. Conversely, WNS had a depressive effect on the species across its range. Wind turbine mortality interacted with WNS and together these stressors had a larger impact than would be expected from either alone, principally because these stressors together act to reduce species abundance across the spectrum of population sizes. Our findings illustrate the importance of not only prioritizing the protection of large winter colonies as is currently done, but also of protecting metapopulation dynamics and migratory connectivity.

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“…More than 314 000 wind turbines (WTs) are now operating around the world and supply more than 4.3% of 2015 global electricity demand . Onshore wind is the lowest cost, nonhydropower renewable energy source, and approximately 97% of turbines installed globally and all but 5 of the 48 500 installed in the US are deployed on land (Figure A). WTs are distributed across all continents (except Antarctica) and all areas of the US except the southeast (Figure A).…”

Section: Introductionmentioning

confidence: 99%

“…Distribution of wind turbine (WT) in the contiguous US, along with installed capacity by state and the location of the wind farm (indicated by the yellow triangle) from which data are presented. A, Locations of industrial scale onshore WT in the contiguous US, installed year Y, rated power P, number of WT N, from the US geological survey (as of July 2013) . Statewide installed wind capacities as of the end of 2016 (gray shading) derive from the American Wind Energy Association .…”

Section: Introductionmentioning

confidence: 99%

A new seismic‐based monitoring approach for wind turbines

Barthelmie

Letson

et al. 2018

Wind Energy

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Wind turbine performance and condition monitoring play vital roles in detecting and diagnosing suboptimal performance and guiding operations and maintenance. Here, a new seismic‐based approach to monitoring the health of individual wind turbine components is presented. Transfer functions are developed linking key condition monitoring properties (drivetrain and tower acceleration) to unique, robust, and repeatable seismic signatures. Predictive models for extreme (greater than 99th percentile) drivetrain and tower acceleration based on independent seismic data exhibit higher skill than reference models based on hub‐height wind speed. The seismic models detect extreme drivetrain and tower acceleration with proportions correct of 96% and 93%, hit rates of 91% and 82%, and low false alarm rates of 4% and 6%, respectively. Although new wind turbines incorporate many diagnostic sensors, seismic‐based condition/performance monitoring may be particularly useful in extending the productive lifetime of previous generation wind turbines.

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Onshore industrial wind turbine locations for the United States

Cited by 22 publications

References 0 publications

On infrasound generated by wind farms and its propagation in low‐altitude tropospheric waveguides

On infrasound generated by wind farms and its propagation in low‐altitude tropospheric waveguides

Effects of wind energy generation and white-nose syndrome on the viability of the Indiana bat

A new seismic‐based monitoring approach for wind turbines

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