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Analysis of Low Frequency Oscillations using improved Hilbert-Huang Transform

机译:使用改进的Hilbert-Huang变换分析低频振荡

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As a non-linear and time-varying tool, Hilbert-Huang Transform (HHT) has been widely used to analyze Low Frequency Oscillation (LFO) signals in power systems. It utilizes Empirical Mode Decomposition (EMD) to decompose the LFO signals into a collection of Intrinsic Mode Functions (IMFs), and then the instantaneous parameters including magnitude, frequency of every IMF can be calculated by applying the Hilbert Transform. However, HHT suffers from a number of shortcomings. In order to dispose the inherent problems of conventional HHT, an improved HHT is proposed based on Symmetrical Extrema Extension (SEE) method and frequency heterodyne technique. In this paper, SEE method is employed to expand the original signal during the processing of EMD and frequency heterodyne technique is used to overcome the mode-mixing phenomena. Next, the principle and influences of different shifting frequency factors are introduced. Based on these, the steps and flow chart of improved HHT are proposed. The results of testing signals and simulation model show that the improved HHT not only diminishes the influences of End Effect, but also expands the application of HHT. It is feasible and effective to overcome special mode-mixing problem.
机译:作为一种非线性且时变的工具,希尔伯特-黄变换(HHT)已被广泛用于分析电力系统中的低频振荡(LFO)信号。它利用经验模式分解(EMD)将LFO信号分解为一组固有模式函数(IMF),然后可以通过应用希尔伯特变换来计算每个IMF的瞬时参数,包括幅度,频率。然而,HHT具有许多缺点。为了解决传统HHT的固有问题,提出了一种基于对称极值扩展(SEE)和频率外差技术的改进型HHT。本文采用SEE方法在EMD处理过程中扩展原始信号,并采用频率外差技术克服了模式混合现象。接下来,介绍了不同的移频因子的原理和影响。在此基础上,提出了改进的HHT的步骤和流程图。测试信号和仿真模型的结果表明,改进后的HHT不仅减少了端效应的影响,而且扩大了HHT的应用范围。克服特殊的模式混合问题是可行和有效的。

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