基于改进MVMD-SOBI算法的直驱风电机组多通道次同步振荡模态辨识  

Multi-channel Sub-synchronous Oscillation Mode Identification of Direct-drive Wind Turbines Based on Improved MVMD and SOBI Algorithms

作　　者：赵峰[1,2] 王雅娴[1] 王英 陈小强 ZHAO Feng;WANG Yaxian;WANG Ying;CHEN Xiaoqiang(School of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China;Key Laboratory of Optoelectronic Technology and Intelligent Control,Ministry of Education,Lanzhou 730070,China)

机构地区：[1]兰州交通大学自动化与电气工程学院,兰州730070 [2]光电技术与智能控制教育部重点实验室,兰州730070

出　　处：《高电压技术》2022年第4期1365-1374,共10页High Voltage Engineering

基　　金：国家自然科学基金(51767013;52067013);教育部光电技术与智能控制重点实验室开放课题(KFKT2020-12);兰州交通大学“天佑创新团队”项目(TY202010)。

摘　　要：针对直驱风电机组(direct-drive permanent magnet synchronous wind turbines,D-PMSG)产生的次同步振荡(sub synchronous oscillation,SSO)辨识问题,该文将用于机械故障检测的多元变分模态分解(multivariate variational mode decomposition,MVMD)进行改进,并与二阶盲辨识(second order blind identification,SOBI)相结合,实现直驱风电机组的次同步振荡模态辨识。针对具有多通道特性的广域量测系统(wide area measurement system,WAMS)量测的SSO信号,提出了一种多通道次同步振荡模态辨识方法。首先,由于MVMD的模态数K值和惩罚因子α值对算法的精确性有绝对的影响,所以对MVMD算法进行改进,建立综合指标Sy来确定K和α;其次,在已知参数基础上,利用改进MVMD对SSO信号进行分解,得到多个本征模态函数分量(intrinsic mode function,IMFs),并借助Fréchet距离筛选出主导IMF分量并去除噪声干扰,同时为提高运算效率,直接辨识出SSO信号模态,以随机子空间思想为基础,将SOBI算法改进,直接辨识出SSO信号的频率、阻尼比和衰减因子;最后,分别利用理想算例、仿真算例和电网实测数据对所提方法进行分析和验证。结果表明,对于直驱风电机组产生的多通道次同步振荡信号,该文方法可高效准确地辨识其参数,为次同步振荡抑制问题的研究奠定基础。In order to solve the problem of sub-synchronous oscillation(SSO)generated by direct-drive permanent magnet synchronous wind turbines(D-PMSG),the multivariate variational mode decomposition(MVMD)method used in fault detection of mechanical fault detection is improved and combined with the second order blind identification(SOBI)to realize the subsynchronous oscillation mode identification of direct driven wind turbine.For wide area measurement system(WAMS)which has multi-channel characteristics,a method of multichannel synchronous oscillation mode identification is proposed.First,the mode number K and penalty factorαof MVMD have absolute influence on the accuracy of the algorithm,thus the MVMD algorithm is improved and a comprehensive index Sy is established to determine K andα.Secondly,On the basis of known parameters,the improved MVMD is used to decompose the SSO signal to obtain multiple intrinsic mode function components(IMFs),and the dominant IMFs are selected with the help of Fréchet distance to remove noise interference.At the same time,in order to improve the operation efficiency,the SSO signal modes are directly identified,the improved MVMD is used to decompose the SSO signal.Based on the idea of random subspace,the SOBI algorithm is improved to directly identify the frequency,damping ratio and attenuation factor of SSO signal.Finally,the proposed method is analyzed and verified by using ideal examples,simulation examples and measured data of power grid.The results show that the proposed method can be adopted to effectively and accurately identify the parameters of multi-channel sub-synchronous oscillation signals generated by direct-drive wind turbines,which lays a foundation for the research of sub-synchronous oscillation suppression.

关 键 词：多元变分模态分解 多通道信号 二阶盲辨识 次同步振荡 直驱风电机组 

分 类 号：TM712[电气工程—电力系统及自动化]