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考虑多领域耦合特性的风电机组一次调频动态建模与仿真

季湛洋 胡阳 孔令行 宋子秋 邓丹 刘吉臻

中国电力2025,Vol.58Issue(4):56-67,12.
中国电力2025,Vol.58Issue(4):56-67,12.DOI:10.11930/j.issn.1004-9649.202411044

考虑多领域耦合特性的风电机组一次调频动态建模与仿真

Dynamic Modeling and Simulation of Wind Turbine Unit Primary Frequency Regulation Considering Multi-domain Coupling Characteristics

季湛洋 1胡阳 1孔令行 2宋子秋 1邓丹 3刘吉臻1

作者信息

  • 1. 新能源电力系统全国重点实验室(华北电力大学),北京 102206||华北电力大学控制与计算机工程学院,北京 102206
  • 2. 中国电力科学研究院有限公司,北京 100192
  • 3. 华北电力大学控制与计算机工程学院,北京 102206
  • 折叠

摘要

Abstract

During the rapid frequency regulation process of wind turbine units,the transient active power release can induce load fluctuations in aerodynamic,transmission,and tower components.In order to reasonably characterize the fluctuation characteristics and serve the optimization of frequency regulation control,this paper presents a fast dynamic modeling method for wind turbine units that takes into account the coupling characteristics of blades,main shaft,generator,and control systems.Firstly,a wind farm-turbine coordinated primary frequency regulation control strategy is set up,and a rapid frequency regulation controller at the unit level is developed for both below and above the rated wind speed based on a refined 5MW wind turbine model.And then,the Spilman correlation analysis algorithm is used to select input and output variables with consideration of input and output delay orders,and the operational domain partitioning is completed,enabling adaptive identification and switching between operation regions both above and below the rated wind speed.Thirdly,based on balanced sampling of simulation operating data under discrete operating conditions,and guided by physical prior information,subspace identification and deep neural network algorithms are employed to conduct multi-input-multi-output modeling and simulation verification of the unit's primary frequency modulation dynamics across the full range of operating conditions.The results show that the state space model obtained has good interpretability,but the model structure inherently limits its approximation accuracy to a finite degree;in comparison,the temporal neural network model demonstrates a better ability to capture dynamic characteristics,providing a robust model foundation for subsequent optimization control of the unit's primary frequency modulation.

关键词

风电机组/一次调频/载荷动态/LSTM神经网络/子空间辨识

Key words

wind turbine unit/primary frequency regulation/load dynamics/LSTM neural network/subspace identification

引用本文复制引用

季湛洋,胡阳,孔令行,宋子秋,邓丹,刘吉臻..考虑多领域耦合特性的风电机组一次调频动态建模与仿真[J].中国电力,2025,58(4):56-67,12.

基金项目

国家电网有限公司科技项目(考虑安全约束的电网故障过程风电机组机电耦合机理及控制方法研究,4000-202355454A-3-2-ZN). This work is supported by Science and Technology Project of SGCC(Research on Electromechanical Coupling Mechanism and Control Method of Wind Turbine During Grid Fault Considering Security Constraints,No.4000-202355454A-3-2-ZN). (考虑安全约束的电网故障过程风电机组机电耦合机理及控制方法研究,4000-202355454A-3-2-ZN)

中国电力

OA北大核心

1004-9649

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