基于改进多点线性化的直驱风电场非线性系统大扰动降阶研究

周乐明; 甄永赞; 曹天植; 谢欢; 梁倍华; 卢文清

doi:10.19912/j.0254-0096.tynxb.2025-0665

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基于改进多点线性化的直驱风电场非线性系统大扰动降阶研究

更新时间：2026-10-15

- 基于改进多点线性化的直驱风电场非线性系统大扰动降阶研究
- Vol. 47, Issue 9, Pages: 690-704(2026)
- 作者机构：
- 作者简介：
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- DOI：10.19912/j.0254-0096.tynxb.2025-0665
  CLC： TM71
- Online First：10 October 2026，
  
  Published：2026
- 稿件说明：
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周乐明, 甄永赞, 曹天植, et al. 基于改进多点线性化的直驱风电场非线性系统大扰动降阶研究[J]. 2026, 47(9): 690-704.
DOI：

周乐明, 甄永赞, 曹天植, et al. 基于改进多点线性化的直驱风电场非线性系统大扰动降阶研究[J]. 2026, 47(9): 690-704. DOI： 10.19912/j.0254-0096.tynxb.2025-0665.

摘要

在大干扰场景下

基于单一平衡点的线性化模型降阶方法无法取得良好的近似效果。为解决该问题

提出一种高精度、保结构、兼顾系统可观性的直驱风电场系统非线性降阶方法。首先

建立多点的局部线性化模型

保留原系统的非线性信息。其次

为避免数值计算中的误差积累和矩阵病态问题

依据广义参与因子划分强弱相关变量。然后根据奇异摄动理论

在非线性动力学方程的基础上

忽略弱相关变量的动态过程

保留强相关状态变量和能观状态变量的动态过程

从而得到兼顾可观性且保留非线性结构的降阶模型。随后

通过非侵入式建模

采用粒子群算法对低阶非线性降阶模型进行改进

使改进后的降阶模型在保持非线性结构的同时能兼顾精度要求。最后

在3种大干扰场景进行仿真验证。结果表明

基于所提降阶方法得到的降阶系统模型与全阶系统模型的动态响应特性高度相似

降阶效率达到50%

响应误差在1%以内

能大幅降低控制策略优化与数值计算的复杂度

可为区域自动电力控制系统的设计与优化提供更为可行与高效的途径。

Abstract

In large-disturbance scenarios

linearized model order reduction methods based on a single equilibrium point cannot achieve satisfactory approximation performance. To address this problem

a nonlinear model order reduction method for direct-drive wind farm systems with high accuracy

structure preservation

and system observability is proposed. First

local linearized models at multiple operating points are established to retain the nonlinear information of the original system. Second

to avoid error accumulation and matrix ill-conditioning in numerical computation

the state variables are classified into strongly and weakly correlated variables according to generalized participation factors. Then

based on singular perturbation theory and the nonlinear dynamic equations

the dynamic processes of the weakly correlated variables are neglected

while those of the strongly correlated state variables and observable state variables are retained

thereby obtaining a reduced-order model that preserves both system observability and nonlinear structure. Subsequently

through non-intrusive modeling

particle swarm optimization is employed to improve the low-order nonlinear reduced-order model

enabling the improved reduced-order model to satisfy the accuracy requirements while preserving the nonlinear structure. Finally

simulations are conducted under three large-disturbance scenarios. The results show that the reduced-order system model obtained using the proposed method exhibits dynamic response characteristics highly similar to those of the full-order system model

with a model-order reduction rate of 50% and response errors within 1%. The proposed method can significantly reduce the complexity of control strategy optimization and numerical computation

providing a more feasible and efficient approach for the design and optimization of regional automatic power control systems.

关键词

Keywords

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