Kalman-based interacting multiple-model wind speed estimator for wind turbines

Wai Hou Lio*, Fanzhong Meng*

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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    The use of state estimation technique offers a means of inferring the rotor-effective wind speed based upon solely standard measurements of the turbine. For the ease of design and computational concerns, such estimators are typically built based upon simplified turbine models that characterise the turbine with rigid blades. Large model mismatch, particularly in the power coefficient, could lead to degradation in estimation performance. Therefore, in order to effectively reduce the adverse impact of parameter uncertainties in the estimator model, this paper develops a wind sped estimator based on the concept of interacting multiple-model adaptive estimation. The proposed estimator is composed of a bank of extended Kalman filters and each filter model is developed based on different power coefficient mapping to match the operating turbine parameter. Subsequently, the algorithm combines the wind speed estimates provided by each filter based on their statistical properties. In addition, the proposed estimator not only can infer the rotor-effective wind speed, but also the uncertain system parameters, namely, the power coefficient. Simulation results demonstrate the proposed estimator achieved better improvement in estimating the rotor-effective wind speed and power coefficient compared to the standard Kalman filter approach.
    Original languageEnglish
    Title of host publicationProceedings of 21th IFAC World Congress 2020
    EditorsRolf Findeisen , Sandra Hirche , Klaus Janschek , Martin Mönnigmann
    Number of pages6
    Publication date2021
    Publication statusPublished - 2021
    SeriesIFAC Proceedings Volumes (IFAC-PapersOnline)


    • Control of renewable energy resouces
    • Estimation and filtering
    • Control system design


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