具有良好时频性能的电力系统次同步振荡模态在线辨识算法  

An Online Modal Identification Algorithm with Good Time-Frequency Performances for Subsynchronous Oscillation in Power Systems

作　　者：李菁 李娟[1] 白淑华 LI Jing;LI Juan;BAI Shuhua(College of Automation,Beijing Information Science and Technology University,Beijing 100192,China)

机构地区：[1]北京信息科技大学自动化学院,北京100192

出　　处：《传感器世界》2021年第6期28-34,共7页Sensor World

基　　金：国家自然科学基金项目(No.51477010)。

摘　　要：介绍了一种适用于电力系统次同步振荡在线监测的非平稳信号模态辨识算法,该算法综合了经典IIR窄带滤波器组算法及经验模态分解(empirical mode decomposition,EMD)算法的优点,利用次同步振荡分析获得的预知频率信息,对可能导致模态混叠的相邻模态做数字滤波器(Infinite Impulse Response,IIR)预处理,再经EMD获得无混叠的固有模态分量IMF1。该算法具有理想的窄带选频特性,且能够快速反映次同步振荡及机组扭振发生最初的时域过程,计算量很小,可用于现场在线提取SSO模态特征,有利于更好地实现对次同步振荡的稳定性判别及闭环控制、机组扭振保护等。最后,利用PSCAD产生的仿真数据及某电厂发生次同步振荡的现场数据验证了该算法的有效性。A modal identification algorithm of the nonstationary signal is designed for the online Subsynchronous Oscillation(SSO)monitoring in the power system.Combining the advantages of both the classical Infinite Impulse Response(IIR)narrow band filter bank algorithm and the Empirical Modal Decomposition(EMD)algorithm and making use of the known frequency information gained through the SSO analysis,it does IIR pretreatment for adjacent modes which may cause modal aliasing,and then obtains the first Intrinsic Mode Function(IMFl)without aliasing by way of EMD.The IIR-EMD algorithm has the ideal performance of distinguishing the modes whose frequencies are very close to each other.Meanwhile,it can effectively reflect the initial dynamic process of the SSO and the generator unit torsional vibration and has a small computation amount.So it can be used to extract SSO modal characteristics online and is advantageous to the better implementation of the judgement of SSO stability,closed-loop control and the protection against generator unit torsional vibration.Finally,this paper uses the simulation data generated by PSCAD and the field data of a subsynchronous oscillation in a power plant to verify the effectiveness ofthe algorithm.

关 键 词：次同步振荡 扭振 IIR EMD 模态辨识算法 

分 类 号：TM712[电气工程—电力系统及自动化] TH17[机械工程—机械制造及自动化]