Abstract
Continued development, expansion, and operation of wind energy installations must be managed in conjunction with effects on local wildlife, with special attention paid to protected avian and bat species that may be affected by wind turbine collisions [1]. Ongoing efforts to measure and mitigate wind turbine impacts on wildlife include improved pre-construction siting and post-construction monitoring, development of deterrent technologies and curtailment strategies, and improved quantitative assessment of wildlife mortality due to wind turbines [2]–[4].
This report summarizes the design, implementation, and test of an integrated system for automated detection and deterrence of eagles, with included wind turbine blade strike detection and imaging functionality. A machine learning approach was used in conjunction with a 360º camera system for automated detection and classification of golden eagles. This was developed using footage obtained from trained golden eagles and other raptors, in collaboration with wildlife biologists and professional bird handlers. Oregon State University developed a visual deterrent system, which uses inflatable anthropomorphic sculptures with random, kinetic motion to deter eagles, and conducted limited field testing on live eagles; the deterrent can be triggered by the visual detection of eagles using the vision system. Finally, a multi-sensor module was developed that is mounted at the turbine blade root. This module measures vibration and other motions to detect blade strikes, and an integrated on-blade camera captures an image of any impacting objects. Long-term, this blade strike detection system is intended to support an automatic monitoring and certification system for the eagle detection and deterent system. Independent field testing of each system component is described.
Visual eagle detection testing was done in collaboration with the High Desert Museum in Bend, OR using trained birds, including both eagles and other raptors. The final eagle classification algorithm (eagle/noneagle) had a per-frame accuracy of 91.54% using the 360º 4K camera.
Visual deterrent testing was conducted in two field tests with wild eagles near Klamath Falls, OR. The deterrent was deployed from the ground when an eagle flys overhead, and observers noted any perceived change in flight trajectory. Interactions were also recorded on video. Results were overall inconclusive due to the small number of eagles that were close enough to the field test for observed interactions.
Testing of the integrated system on an operational wind turbine was conducted across three separate field tests. This includes multi-day fields tests on a General Electric 1.5MW wind turbine at the National Renewable Energy Laboratory (NREL) National Wind Technology Center (NWTC) in Boulder, CO in October 2018 and July 2019; installation procedures, test procedures, and a summary of collected data are presented. A third multi-day on-turbine field test is also presented, which was performed using a General Electric 1.5MW wind turbine at the North American Wind Research and Training Center (NAWRTC) at Mesalands Community College, Tucumcari, NM in April 2019. Across these field tests, the vision system was demonstrated using unmanned aerial vehicles (UAV), and the eagle classification algorithm was not tested; the visual deterrent system was demonstrated, including automatic, remote deployment following surrogate visual detections; and, multi-sensor on-blade data was recorded across multiple wind turbine operational conditions and through more than 100 surrogate blade strikes using soft projectiles, including the successful demonstration of automatic image capture of striking objects. This data set was also used for offline development and validation of enhanced collision detection algorithms.
As summarized in this report, the development and field validation of an integrated detection, deterrent, and blade collision detection system represents a critical proof of concept for future technology development of related detection and deterrent technologies, where both deterrent as well as collision detection recording devices are needed for future siting, monitoring, and operation of wind turbine installations, both onshore and offshore.