Data-Driven Modeling and Analysis of Dynamic Wake for Wind Farm Control

A Comparison Study

Conference paper (2020)

Authors

Z. Chen North China Electric Power University

B.M. Doekemeijer Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering

Zhongwei Lin North China Electric Power University

Zhen Xie North China Electric Power University

Zongming Si CHN ENERGY (Shandong) New Energy Co.

Jizhen Liu North China Electric Power University

J.W. van Wingerden Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering

Research Group

Team Jan-Willem van Wingerden (Mechanical, Maritime and Materials Engineering) (TU Delft)

Frequency domain Identification Dynamic mode decomposition Dynamic system Wake effect

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http://resolver.tudelft.nl/uuid:8ec11254-f6ca-4300-af09-ebee3418659c

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Published Date

2020

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Jan-Willem van Wingerden

Abstract

For the study of wind farm and wake effect, the steady-state wake models like FLORIS were proposed and used during wind farm operations to achieve higher wind power utilization and conversion. However, the dynamic performance of the wake should also be involved in favor of better optimizations. In this paper, a data-driven analysis and modeling method, dynamic mode decomposition (DMD), is used to construct dynamic flow model with high-fidelity flow data. Two DMD-derived models are constructed based on flow data in three-dimensional and two-dimensional spaces, respectively. The obtained models and respective flows are compared in the time and frequency domain. Results show that both models have apparent differences in the frequency domain, but the dominant wake characteristics' consistency is maintained.

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