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文章摘要
基于ICA-EMD和Prony算法的区域电网低频振荡模式分析
Analysis of Regional Power System Low Frequency Oscillation ModeBased on ICA-EMD and Prony Algorithm
Received:August 26, 2014  Revised:August 26, 2014
DOI:
中文关键词: 独立分量分析  经验模态分解  Prony算法  振荡模式辨识
英文关键词: independent  component analysis (ICA) ,empirical  mode decomposition (EMD), Prony  algorithm, oscillation  mode identification
基金项目:
Author NameAffiliationE-mail
PENG Zhanggang* 1. School of Electrical and Information,Sichuan University pengswpu@163.com 
ZHOU Buxiang 1. School of Electrical and Information,Sichuan University  
LI Shixin 1. School of Electrical and Information,Sichuan University  
WANG Jingwei 1. School of Electrical and Information,Sichuan University  
ZHOU Haizhong 1. School of Electrical and Information,Sichuan University  
TANG Hao 1. School of Electrical and Information,Sichuan University  
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中文摘要:
      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。
英文摘要:
      Due to the issues that low-frequency oscillation occurs frequently in interconnected grid it’s important to analyze the oscillation modes the fluctuation signal contains. In this work, a novel signal analysis method based on the combination of Independent Component Analysis (ICA) and Empirical Mode Decomposition (EMD) and Prony algorithm was presented. 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 extract key oscillation modes from each IMF by Prony algorithm. Simulation results show that this method merged the advantages of both ICA and 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.
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