基于VMD的风机次同步振荡模态辨识方法研究  

Research on sub-synchronous oscillation mode identification method based on VMD for wind turbines

作　　者：张彬彬[1] 李聪[1] 陈超波[1] 王坤[1] 李继超[1] Zhang Binbin;Li Cong;Chen Chaobo;Wang Kun;Li Jichao(School of Electronic Information Engineering,Xi'an University of Technology,Xi'an 710021,China)

机构地区：[1]西安工业大学电子信息工程学院,西安710021

出　　处：《电子测量技术》2024年第14期186-194,共9页Electronic Measurement Technology

基　　金：陕西省教育厅专项科研计划项目(22JK041)资助。

摘　　要：针对变分模态分解算法在对风电并网过程中产生的次同步振荡信号进行分解时受噪声干扰、关键参数难以确定,导致的辨识精度不足问题,本文提出一种基于小波阈值去噪(WTD)和遗传算法(GA)优化变分模态分解(VMD)的信号分解算法,结合自回归滑动模型(ARMA)的次同步振荡模态辨识方法。首先,采用小波阈值对风电机组输出的有功功率进行去噪处理;其次,使用VMD对去噪后的信号进行分解,得到K个本征模态分量,为得到最优VMD分解效果,采用自适应遗传算法对惩罚因子α及模态分解层数K进行优化;最后,将信号重构并建立ARMA模型,直接辨识出次同步振荡信号的频率和阻尼比。通过仿真实验平台搭建直驱风电机组并网模型,采集次同步振荡信号进行模态辨识。仿真结果表明,与其他辨识算法相对比,所提出的基于VMD的方法具有更好的可行性和优越性。Regarding the problem of insufficient identification accuracy caused by noise interference and difficult determination of key parameters when using the variational mode decomposition(VMD)algorithm to decompose the sub-synchronous oscillation signals generated during the grid connection process of wind power,this paper proposes a signal decomposition algorithm based on wavelet threshold denoising(WTD)and genetic algorithm(GA)optimized VMD,combining with the sub-synchronous oscillation mode identification method of autoregressive moving average model(ARMA).Firstly,wavelet threshold denoising is used to process the active power output of the wind turbine;secondly,VMD is used to decompose the denoised signal,obtaining K intrinsic mode components.In order to achieve the optimal VMD decomposition effect,an adaptive genetic algorithm is used to optimize the penalty factorαand the number of decomposition layers K.Finally,the signal is restructured and an ARMA model is established to directly identify the frequency and damping ratio of the sub-synchronous oscillation signal.By building a simulation experiment platform for direct-drive wind turbine grid connection model and collecting sub-synchronous oscillation signals for mode identification,the simulation results show that,compared with other identification algorithms,the proposed VMDbased method has better feasibility and superiority.

关 键 词：直驱风电机组 次同步振荡 变分模态分解 参数辨识 

分 类 号：TM933[电气工程—电力电子与电力传动] TN751.3[电子电信—电路与系统]