In this chapter, the data-driven methodologies introduced in Chap. 4 are used to derive a Reduced Order Model (ROM) for a set of two wind turbines where Wake Redirection Control is tested in a simulation environment using SOWFA. The contents of this chapter follow the various steps taken in the systems identification procedure already introduced. The dataset and code used in this chapter are available in https://github.com/nassircassamo/IODMD_SOWFA and Cassamo and van Wingerden (Dataset used in article: On the Potential of Reduced Order Models for Wind Farm Control: A Koop-man Dynamic Mode Decomposition Approach. Zenodo, 2020), Cassamo and van Wingerden (Dataset used in article: Model Predictive Control for Wake Redirection in Wind Farms: a Koop-man Dynamic Mode Decomposition Approach. Zenodo, 2021) , and the reader is invited to explore and test it with the aim of making the learning process a hands-on and enjoyable experience.

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Data-Driven Modelling of Wake Redirection Control

  • Nassir Cassamo,
  • Jan-Willem van Wingerden

摘要

In this chapter, the data-driven methodologies introduced in Chap. 4 are used to derive a Reduced Order Model (ROM) for a set of two wind turbines where Wake Redirection Control is tested in a simulation environment using SOWFA. The contents of this chapter follow the various steps taken in the systems identification procedure already introduced. The dataset and code used in this chapter are available in https://github.com/nassircassamo/IODMD_SOWFA and Cassamo and van Wingerden (Dataset used in article: On the Potential of Reduced Order Models for Wind Farm Control: A Koop-man Dynamic Mode Decomposition Approach. Zenodo, 2020), Cassamo and van Wingerden (Dataset used in article: Model Predictive Control for Wake Redirection in Wind Farms: a Koop-man Dynamic Mode Decomposition Approach. Zenodo, 2021) , and the reader is invited to explore and test it with the aim of making the learning process a hands-on and enjoyable experience.