基于ICA-EMD和Prony算法的区域电网低频振荡模式分析  

Analysis of low frequency oscillation mode for regional power system based on ICA-EMD and Prony algorithm

作　　者：彭章刚 周步祥[1] 李世新[1] 王精卫 周海忠[1] 唐浩[1] 

机构地区：[1]四川大学电气信息学院,成都610065

出　　处：《电测与仪表》2015年第23期16-22,共7页Electrical Measurement & Instrumentation

摘　　要：针对互联电网低频振荡频现,已有低频振荡模式分析方法对噪声较为敏感和难以处理非线性、非平稳信号等问题,提出一种基于独立分量分析(ICA)与经验模态分解(EMD)有机结合的Prony关键振荡模式辨识法。通过对观测到的功角信号进行滤波预处理,并对其进行经验模态分解提取得到固有模态函数(IMF),将已得原始固有模态函数白化,接着用独立分量分析处理得到真正的IMF,用Prony算法辨识各IMF分量提取出观测信号中关键振荡模式。研究结果表明,该方法综合利用了ICA的去相关性和噪声抑制优势及EMD对复杂信号的分解能力,克服了Prony算法难以去除噪声和分解频率相近模式的缺陷,有利于提高辨识精度和准确性,更能满足实际应用需求。Due to the issues that low-frequency oscillation occurs frequently in interconnected grid and the existing mode analysis method of low-frequency oscillation is sensitive to noise and incapable of dealing with the nonlinear and non-stationary signal, an identification method of Prony key oscillation modes based on the combination of ICA and EMD is proposed in this paper. Through pretreating the observed power angle signals with filtering operation and its empirical mode decomposition to extract the intrinsic mode function （IMF）, then processing the IMFs with whitening method, followed by using independent component analysis to get true IMFs, finally the key oscillation modes from each IMF is extracted by Prony algorithm. Simulation results show that this method merged the advantages of de-corre- lation and noise suppression of ICA and the capacity of complex signal decomposition of EMD which showed more competitiveness over the previous method, and help to improve the precision and accuracy of identification, to better meet the needs of practical application.

关 键 词：独立分量分析 经验模态分解 PRONY算法 振荡模式辨识 

分 类 号：TM71[电气工程—电力系统及自动化]