稳态和动态混合信号的在线低频振荡模式辨识方法  

An Online Low Frequency Oscillation Estimation Method for Ringdown Signals Mixed Ambient Ones

作　　者：徐玉韬[1] 卢继平[1] 陈刚 刘贵富[1] 王予疆[1] 何潜[3] 

机构地区：[1]输配电装备及系统安全与新技术国家重点实验室,重庆大学,重庆市400044 [2]南方电网科学研究院,广东省广州市510080 [3]重庆市电力公司,重庆市400015

出　　处：《电力系统自动化》2012年第2期31-35,105,共6页Automation of Electric Power Systems

基　　金：国家重点基础研究发展计划(973计划)资助项目(2009CB724505-1);高等学校学科创新引智计划(“111计划”)资助项目(B08036);重庆电力公司科技项目(20100658)~~

摘　　要：针对目前自回归滑动平均(ARMA)法结合Prony方法方案存在的扰动检测和算法切换的难题,文中提出并验证了可以基于归一化峰度判断滑动窗内是否包含动态信号的方法,然后提出一种基于归一化峰度在常规ARMA法和高阶ARMA法之间自适应切换的低频振荡模式辨识方案。通过对电网实测信号的分析,将所提出方法与Prony方法和常规ARMA法分析结果进行对比,验证了此方案的可行性。It is well known that it is difficult to achieve disturbance-detecting and algorithm switching in autoregressive moving average （ARMA） method combined with Prony method. A new method is proposed to solve this difficulty. The method is based on normalization kurtosis to judge whether ringdown signal exists in the sliding window. With this method, a low frequency identification scheme is developed to achieve adaptive switching between the common ARMA method and the higher order ARMA method. Case studies are given to compare the performances of the proposed method with Prony method as well as normal ARMA method. The results demonstrate the validity of the proposed scheme.

关 键 词：低频振荡 振荡模式 在线辨识 高阶自回归滑动平均法 超高斯信号 归一化峰度 

分 类 号：TM712[电气工程—电力系统及自动化]