A learning algorithm for the calibration of internal model uncertainties in advanced wind turbine controllers: A wind speed measurement-free approach

S. P. Mulders, L. Brandetti, F. Spagnolo, Y. Liu, P.B. Christensen, J. W. van Wingerden

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


Wind turbine partial-load controllers have evolved from simple static nonlinear function implementations to more advanced dynamic controller structures. Such dynamic control schemes have the potential to improve power production performance in realistic environmental conditions and allow for a more granular trade-off between loads and energy capture. The control structure generally consists of a wind speed estimator (WSE) combined with a controller aiming to track the commanded tip-speed ratio (TSR) reference. The performance and resulting closed-loop system stability are however highly dependent on the accuracy of the internal model in the WSE-TSR tracking scheme. Therefore, developing learning algorithms to calibrate the internal model is of particular interest. Previous works have proposed such algorithms; however, they all rely on the availability of (rotor-effective) wind speed measurements. For the first time, this paper proposes an excitation-based learning algorithm that exploits the closed-loop dynamic structure of the WSE-TSR tracking scheme. This algorithm calibrates the internal model without the need for wind speed measurements. Analysis and simulations show that the proposed algorithm corrects for model uncertainties in the form of magnitude scaling errors under ideal constant and realistic turbulent wind conditions.
Original languageEnglish
Title of host publicationProceedings 2023 American Control Conference (ACC)
ISBN (Print)979-8-3503-2806-6
Publication statusPublished - 2023
EventAmerican Control Conference, ACC 2023 - San Diego, United States
Duration: 31 May 20232 Jun 2023


ConferenceAmerican Control Conference, ACC 2023
Country/TerritoryUnited States
CitySan Diego

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