Posters - WindEurope Annual Event 2024

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We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in the academic community. We look forward to seeing you there!

PO273: Methodology for performance losses detection due to yaw misalignment caused by disturbances in wind vane measurements as a result of blade effects and control optimization strategy

Roberto Del Campo Arzoz, Innovation Project Manager, Acciona Energía


There are several scientific publications that have evaluated the possibility of increasing energy production in wind turbines by detecting deviations in the orientation of the nacelle relative to the wind direction. Detecting and assessing the misalignment and production impact caused by such deviation allows for recommending orientation correction strategies to improve production. This concern is prevalent in the industry, leading to various environmental condition measurement solutions at the hub's nose position to determine the actual misalignment of each unit. However, these solutions can be expensive when installing equipment on each wind turbine. Therefore, at ACCIONA Enegía, we wanted to assess the possibility of detecting potential misalignment in each wind turbine through the analysis of data recorded by the equipment's PLC. In our studies, it was observed that most wind turbines would produce more energy with a counterclockwise misalignment. By applying a fixed correction (around 4°) at the fleet level or optimizing the correction for each wind turbine, production could be improved (over 1%) on the potentially improvable production. This yaw orientation correction can be optimized for each wind turbine getting bigger performance improvements. Having identified this effect and the methodology to measure it, an opportunity emerged to optimize the control strategy for real-time correction of wind turbine misalignment, aiming for continuous production increase. The developed methodology employs machine learning algorithms tailored to each wind turbine's specific conditions, proposing a customized orientation strategy based on historical data and other operational variables such as temperature, wind direction, etc.

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