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An improved Empirical Mode Decomposition method for monitoring electromechanical oscillations

机译:一种改进的经验模态分解方法,用于监测机电振荡

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The use of Hilbert-Huang Transform (HHT) demonstrated to be effective in detecting time-varying electromechanical oscillations. HHT is a two-step algorithm, consisting of Empirical Mode Decomposition (EMD) and Hilbert Transform. EMD decomposes a signal into a set of Intrinsic Mode Functions, each containing the one oscillatory function. In this paper, the focus is on improving the EMD operation. The proposed enhancements increase the resistance of EMD against mode mixing. Mode mixing is defined as the intermittency of oscillatory dynamics due to operating conditions or abrupt disturbances. The improved EMD (IEMD) is comparatively evaluated with the conventional EMD (CEMD) for tracking simple synthetic signals and simulated system measurements. Based on observations, IEMD provides better mode tracking capability than CEMD.
机译:Hilbert-Huang变换(HHT)的使用已证明可有效检测随时间变化的机电振荡。 HHT是一个两步算法,由经验模式分解(EMD)和希尔伯特变换组成。 EMD将信号分解为一组固有模式函数,每个函数都包含一个振荡函数。在本文中,重点是改善EMD操作。提出的增强功能增加了EMD对模式混合的抵抗力。模式混合被定义为由于工作条件或突然干扰而引起的振荡动力学的间歇性。改进的EMD(IEMD)与常规EMD(CEMD)进行了比较评估,以跟踪简单的合成信号和模拟系统测量结果。基于观察,IEMD提供了比CEMD更好的模式跟踪功能。

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