Abstract
This study investigates how artificial intelligence (AI) at offshore wind farms could potentially both improve meeting Annual Energy Production (AEP) targets as well as reduce avian mortality rates resulting from turbine collision. While turbine-related bird deaths are widely skewed in the current literature, this research aims to completely reduce the already low bird mortality statistic caused via wind power operation by providing solid evidence of true mortality rates. Additionally, securing long-term investments with stakeholders and increasing market size will be a side effect of the undergone investigation. This research took place along the Coastal Regions of Denmark and in California, USA, where heavy migratory flyways lie near offshore Vestas wind parks. As a collaborative research partner to Aarhus University, Vestas Wind Systems A/S engaged in research and testing throughout the duration of this study. A literature review comparing existing bird tracking technologies used for collision-avoidance purposes is examined and a SWOT analysis performed. This paper addresses gaps in the existing technologies while also introducing a new and improved approach to siting future wind projects. Since wind turbine curtailment can cost manufacturers and owners up to $100 per turbine per hour, this research additionally aims to reduce curtailment onset thanks to AI technology learning site-specific spatial patterns. By combining multi-sensory information from motion-sensor cameras, eBird database, magnetic fields and Doppler radar the following paper illustrates how such information can deem useful in improving collision-avoidance systems while also providing more knowledge of local conditions for both existing and future wind projects.
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Salkanović, E., Enevoldsen, P., Xydis, G. (2020). Applying AI-Based Solutions to Avoid Bird Collisions at Wind Parks. In: Vasel-Be-Hagh, A., Ting, DK. (eds) Complementary Resources for Tomorrow. EAS 2019. Springer Proceedings in Energy. Springer, Cham. https://doi.org/10.1007/978-3-030-38804-1_7
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