Greater sage-grouse habitat selection, survival, and wind energy infrastructure

Abstract Over the last century, increasing human populations and conversion of grassland to agriculture have had severe consequences for numbers of Greater Prairie-Chicken (Tympanuchus cupido). Understanding Greater Prairie-Chicken response to human disturbance, including the effects of anthropogenic noise and landscape modification, is vital for conserving remaining populations because these disturbances are becoming more common in grassland systems. Here, we evaluate the effect of low-frequency noise emitted from a wind energy facility on habitat selection. We used the Normalized Difference Soundscape Index, a ratio of human-generated and biological acoustic components, to determine the impact of the dominant acoustic characteristics of habitat relative to physical landscape features known to influence within–home range habitat selection. Female Greater Prairie-Chickens avoided wooded areas and row crops but showed no selection or avoidance of wind turbines based on the availability of these features across their home range. Although the acoustic environment near the wind energy facility was dominated by anthropogenic noise, our results show that acoustic habitat selection is not evident for this species. In contrast, our work highlights the need to reduce the presence of trees, which have been historically absent from the region, as well as decrease the conversion of grassland to row-crop agriculture. Our findings suggest physical landscape changes surpass altered acoustic environments in mediating Greater Prairie-Chicken habitat selection.

Download Full-text

Prognostics and Health Management of Wind Energy Infrastructure Systems

ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg ◽

10.1115/1.4053422 ◽

2022 ◽

Author(s):

Celalettin Yuce ◽

Ozhan Gecgel ◽

Oguz Dogan ◽

Shweta Dabetwar ◽

Yasar Yanik ◽

...

Keyword(s):

Machine Learning ◽

Wind Energy ◽

Data Augmentation ◽

Health Management ◽

Prognostics And Health Management ◽

Quality Of Data ◽

Energy Infrastructure ◽

Infrastructure Systems ◽

The Impact

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.

Download Full-text