Migrating whooping cranes avoid wind‐energy infrastructure when selecting stopover habitat

2021 ◽  
Author(s):  
Aaron T. Pearse ◽  
Kristine L. Metzger ◽  
David A. Brandt ◽  
Jill A. Shaffer ◽  
Mark T. Bidwell ◽  
...  
Author(s):  
Celalettin Yuce ◽  
Ozhan Gecgel ◽  
Oguz Dogan ◽  
Shweta Dabetwar ◽  
Yasar Yanik ◽  
...  

Abstract The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute towards the Prognostics and Health Management (PHM) in this industry. These advancements are driven by the need to fully explore the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT). All these aspects need to be taken into consideration to perform an effective PHM of wind energy infrastructure. To address these aspects, four research questions were formulated. What is the role of uncertainty in machine learning (ML) in diagnostics and prognostics? What is the role of data augmentation and quality of data for ML? What is the role of PBML? What is the role of the DT in diagnostics and prognostics? The methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA). A total of 143 records, from the last five years, were analyzed. Each of the four questions was answered by discussion of literature, definitions, critical aspects, benefits and challenges, the role of aspect in PHM of wind energy infrastructure systems, and conclusion.


2017 ◽  
Vol 81 (4) ◽  
pp. 690-711 ◽  
Author(s):  
Chad W. LeBeau ◽  
Gregory D. Johnson ◽  
Matthew J. Holloran ◽  
Jeffrey L. Beck ◽  
Ryan M. Nielson ◽  
...  

2021 ◽  
Vol 9 ◽  
Author(s):  
Christopher J. W. McClure ◽  
Leah Dunn ◽  
Jennifer D. McCabe ◽  
Brian W. Rolek ◽  
André Botha ◽  
...  

Energy infrastructure, particularly for wind power, is rapidly expanding in Africa, creating the potential for conflict with at-risk wildlife populations. Raptor populations are especially susceptible to negative impacts of fatalities from wind energy because individuals tend to be long-lived and reproduce slowly. A major determinant of risk of collision between flying birds and wind turbines is the altitude above ground at which a bird flies. We examine 18,710 observations of flying raptors recorded in southern Africa and we evaluate, for 49 species, the frequency with which they were observed to fly at the general height of a wind turbine rotor-swept zone (50–150 m). Threatened species, especially vultures, were more likely to be observed at turbine height than were other species, suggesting that these raptors are most likely to be affected by wind power development across southern Africa. Our results highlight that threatened raptor species, particularly vultures, might be especially impacted by expanded wind energy infrastructure across southern Africa.


2019 ◽  
Author(s):  
Tom Mueller ◽  
Matthew M Brooks

The transition towards renewable energy is likely to be uneven across social and spatial dimensions. To ensure this transition is equitable and just, energy injustice has become the key framework for analyzing and interpreting the distribution of energy infrastructure. Wind energy development has experienced a significant gap between broad public support for increased development but persistent localized opposition to proposed projects, indicating that wind represents a locally unwanted land use. We argue that although the negative impacts of wind energy infrastructure are less extreme than those posed by other, more toxic, unwanted land uses, their status as a locally unwanted land use will produce similar distributional injustices as have been found throughout the environmental injustice literature. Using data from both the American Community Survey and the U.S. Wind Turbine Database, we use logistic and Poisson regressions, fixed effects, and temporal lags to evaluate the current landscape of wind energy injustice along the social dimensions of income, race and ethnicity, age, education, labor force participation, and rurality at three spatial scales: between all counties within the contiguous United States, between counties within states with wind energy, and between census tracts within counties with wind energy. We find results vary by scale and whether the model is comparing the presence of any development or the size of that development. The most evidence of injustice is visible at the within-county level related to whether or not there is any wind energy development, with few relationships present when evaluating the absolute size of development.


2013 ◽  
Vol 28 (2) ◽  
pp. 541-550 ◽  
Author(s):  
J. AMY BELAIRE ◽  
BETTY J. KREAKIE ◽  
TIMOTHY KEITT ◽  
EMILY MINOR

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