Posters
Come meet the poster presenters to ask them questions and discuss their work
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 industry and the academic community.
On 9 April at 17:15, we’ll also hold the main poster session and distinguish the 7 best posters of this year’s edition with our traditional Poster Awards Ceremony. Join us at the poster area to cheer and meet the laureates, and enjoy some drinks with all poster presenters!
We look forward to seeing you there!
PO168: An efficient iterative learning algorithm for calibration of the internal aerodynamic model for wind turbine control by including dynamic effects and using subsampling
Guido Lazzerini, Post-doctoral researcher, TU Delft
Abstract
The work focuses on improving wind turbine efficiency, through the recalibration of the internal aerodynamic model for the control scheme. Wind turbines often suffer performance degradation over time due to blade wear and residue buildup, leading to inaccuracies in aerodynamic models in the control scheme and reduced power output. To tackle this issue, the study introduces a new iterative learning algorithm designed to recalibrate these aerodynamic models. Unlike traditional methods that may need extensive external tests or long data collection periods, this innovative approach uses only data from routine turbine operations and wind measurements. This makes the algorithm highly practical for real-time application without the necessity of interrupting turbine operation. The algorithm is particularly notable for its ability to work with shorter data collection windows with respect to traditional methodologies. By incorporating dynamic effects and using subsampling techniques, it allows for quick and precise estimation of the current aerodynamic model. Tests showed that the algorithm could quickly converge to accurate solutions with data measured in a time frame of 25 seconds, significantly outperforming other schemes. Its ability to estimate the aerodynamic model with shorter data sets makes it a valuable tool for maintaining the performance of wind turbines, thereby supporting the broader goal of maximizing renewable energy production.
No recording available for this poster.
