基于状态扩张输入输出动态模态分解的风力机尾流降阶模型

魏赏赏; 李智寒; 陈一凯; 许昌; 赵振宙; 许波峰

doi:10.19912/j.0254-0096.tynxb.2023-0975

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (10) : 580-587. DOI: 10.19912/j.0254-0096.tynxb.2023-0975

基于状态扩张输入输出动态模态分解的风力机尾流降阶模型

魏赏赏, 李智寒, 陈一凯, 许昌, 赵振宙, 许波峰

作者信息 +

REDUCED-ORDER WAKE MODEL OF WIND TURBINES BASED ON STATE EXPANSION INPUT-OUTPUT DYNAMIC MODE DECOMPOSITION

Wei Shangshang, Li Zhihan, Chen Yikai, Xu Chang, Zhao Zhenzhou, Xu Bofeng

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文章历史 +

摘要

基于数据驱动范式,采用输入输出动态模态分解方法（IODMD）构建机组偏航动作下风力机尾流降阶模型,此外为应对传统输入输出动态模态分解方法（IODMD）在局部流场精度不足的问题,提出扩张风力机转速的输入输出动态模态分解方法（EIODMD）,从而使得降阶模型能充分利用机组运行特性。研究结果表明,所提EIODMD较传统IODMD方法在流场重构与预测精度方面均有所提高,证明了EIODMD尾流降阶模型的优越性。

Abstract

Based on the data and considering the influence of yaw control on wake model, this paper constructs a reduced-order wind turbine wake model with input and output. At the same time, to solve the inefficiency of the input-output dynamic mode decomposition （IODMD） method in reconstructing the local flow field, an extended state input-output dynamic mode decomposition （EIODMD） approach is proposed by integrating wind turbine speed, so that the characteristics of the wind turbines can be considered in the mode decomposition. The results show that compared with the rtaditional IODMD method, the proposed EIODMD can improve the flow field reconstruction and prediction accuracy, demonstrating the superiority of the reduced-order model based on the proposed EIODMD approach.

导出引用

魏赏赏, 李智寒, 陈一凯, 许昌, 赵振宙, 许波峰. 基于状态扩张输入输出动态模态分解的风力机尾流降阶模型[J]. 太阳能学报. 2024, 45(10): 580-587 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0975

Wei Shangshang, Li Zhihan, Chen Yikai, Xu Chang, Zhao Zhenzhou, Xu Bofeng. REDUCED-ORDER WAKE MODEL OF WIND TURBINES BASED ON STATE EXPANSION INPUT-OUTPUT DYNAMIC MODE DECOMPOSITION[J]. Acta Energiae Solaris Sinica. 2024, 45(10): 580-587 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0975

中图分类号： TK8

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