提升经验模态分解检测低频振荡模式精度的改进算法研究  

Research on an improved algorithm to enhance the detection accuracy of low-frequency oscillation modes by empirical mode decomposition

作　　者：易建波[1] 黄琦[1] 丁理杰[2] 张华[2] 

机构地区：[1]电子科技大学电力系统广域测量与控制四川省重点实验室,四川成都611731 [2]四川电力科学研究院,四川成都610072

出　　处：《电力系统保护与控制》2013年第22期71-78,共8页Power System Protection and Control

基　　金：国家自然科学基金(51277022);教育部"新世纪优秀人才支持计划"(NCET-09-0262)~~

摘　　要：基于实测受扰轨迹分析低频振荡问题已成为现代电力系统稳定性分析的重要手段。针对经验模态分解(EMD)方法分析低频振荡信号时,影响该方法精度的端点效应、频率漂移、模态混叠、阻尼损失等问题,分别提出基于ARMA模型的端点延拓法、B样条插值法、精细化复小波分析法及能量微差因子控制法加以解决。通过这些方法的混成应用,设计出一套改进的应用算法,有效提高了低频振荡信号非平稳特性参数检测精度。最后,通过EPRI-36仿真算例和RTDS实时算例测试,验证了所提算法检测复杂低频振荡信号模式信息具备很高的有效性和精度。The characteristic analysis of low-frequency oscillation based on disturbed measured trajectory gradually becomes an important means of modem large grid stability analysis. When empirical mode decomposition （EMD） method is used to analyze the low-frequency oscillation signal, the problems, such as end effect, frequency drift, mode mixing and damping loss, will affect the analysis accuracy of the method. So, the ARMA model-based endpoint extension method, the B spline interpolation method, the fine complex wavelet analysis and the energy difference factor control method are proposed separately to solve the above problems. An improved algorithm is designed by the hybrid application of the above method, which effectively improves the accuracy of detecting signal non-stationary characteristics. Finally, through the testing and analysis of EPRI-36 simulation case and RTDS real-time case, it is verified that the proposed algorithm has high accuracy and effectiveness to detect the modes information of complex low-frequency oscillation signal.

关 键 词：低频振荡 非平稳信号 经验模态分解 模态混叠 复小波分析 

分 类 号：TM712[电气工程—电力系统及自动化]