A correlated empirical mode decomposition method for partial discharge signal denoising

Abstract

Empirical mode decomposition (EMD) is a signal processing method used to extract intrinsic mode functions (IMFs) from a complicated signal. For a measurement with two or more correlated inputs, finding and capturing the correlated IMFs is a critical challenge that must be confronted. In this paper, a new correlated EMD method is proposed. The cross-correlation method was employed to determine dependence between the IMFs. To verify feasibility, an analysis was performed on simulated test signals and practically measured partial discharge (PD) signals collected from several acoustic emission sensors. At the surface of the gas-insulated transmission line, the PD signal arrived at the AE sensors with varying time delays and unique mechanism vibrations. Following an abnormal detection using the standard-deviation variation, the PD signal and the background signal of each sensor were applied using the correlated-EMD method. A twice correlated-EMD calculation was applied to the signals for the purpose of noise elimination. In addition, the unwanted low-frequency IMFs induced from the EMD calculations were excluded. The experimental results reveal that the correlated-EMD method performs well on both selecting and denoising the correlated IMFs. The results further provide analysis on correlated-input applications with a precise signal completely induced from the disturbance.

Disturbance elimination in near-infrared spectroscopy by correlated empirical mode decomposition

“…These methods have been proposed to filter out noise components from a signal. This paper proposes the CEMD method [7], a variant of the EMD method, to determine the correlation between two signals and whether common mode disturbances or uncorrelated or common background noises can be extracted by an appropriate screening process. The next section presents a detailed description of this method.…”

Self-adaptive partial discharge signal de-noising based on ensemble empirical mode decomposition and automatic morphological thresholding

“…Instead, it decomposes the original signal into a number of monocomponent signals called intrinsic mode functions (IMFs) by using the local characteristic time scale of the signal itself. Recently, Tang et al applied EMD to PD de-nosing [12]. However, the inherent limitations of EMD such as mode mixing and end effect were not considered in [12].…”

“…Recently, Tang et al applied EMD to PD de-nosing [12]. However, the inherent limitations of EMD such as mode mixing and end effect were not considered in [12]. Due to these two limitations, an IMF may consist of more than one frequency component combining both real signals and noise.…”

Advanced signal processing techniques for online power transformer insulation diagnosis

“…Instead, it decomposes a signal into a number of mono-component signals called intrinsic mode functions (IMFs) by using the local characteristic time scale of the signal itself. EMD has been applied to PD signal de-noising [127]. However, the inherent limitations of EMD such as mode mixing and end effect…”

“…were not considered in [127]. Due to these two limitations, an IMF may consist of more than one frequency component combining both PD signals and noise.…”