Ensemble Empirical Mode Decomposition for the denoising of partial discharges measured in UHF

Rojas-Moreno, M. V.; Robles, Guillermo Cortés; Martínez-Tarifa, Juan Manuel; Fresno, José Manuel

doi:10.1109/icd.2016.7547777

Cited by 2 publications

(2 citation statements)

References 20 publications

(22 reference statements)

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“…The key to ensuring the accurate extraction of signals based on the EMD algorithm is to select IMFs from the decomposition results that are significantly representative of PD signals. Currently, the effective selection of significant IMFs relies mainly on the autocorrelation coefficient [19], the energy threshold [20], and the kurtosis value [21]. However, these methods for selecting IMFs may fail to detect significant components or incorrectly select irrelevant components when the IMF contains a high proportion of narrowband noise.…”

Section: Effective Selection Of Significant Imfsmentioning

confidence: 99%

Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet

et al. 2019

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While both periodic narrowband noise and white noise are significant sources of interference in the detection and localization of partial discharge (PD) signals in power cables, existing research has focused nearly exclusively on white noise suppression. This paper addresses this issue by proposing a new signal extraction method for effectively detecting random PD signals in power cables subject to complex noise environments involving both white noise and periodic narrowband noise. Firstly, the power cable signal was decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the periodic narrowband noise and frequency aliasing in the obtained signal components were suppressed using singular value decomposition. Then, signal components contributing significantly to the PD signal were determined according to the cross-correlation coefficient between each component and the original PD signal, and the PD signal was reconstructed solely from the obtained significant components. Finally, the wavelet packet threshold method was used to filter out residual white noise in the reconstructed PD signal. The performance of the proposed algorithm was demonstrated by its application to synthesized PD signals with complex noise environments composed of both Gaussian white noise and periodic narrowband noise. In addition, the time-varying kurtosis method was demonstrated to accurately determine the PD signal arrival time when applied to PD signals extracted by the proposed method from synthesized signals in complex noise environments with signal-to-noise ratio (SNR) values as low as −6 dB. When the SNR was reduced to −23 dB, the arrival time error of the PD signal was only one sampling point.

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Section: Effective Selection Of Significant Imfsmentioning

confidence: 99%

Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet

et al. 2019

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“…Another one of these methods is the empirical mode decomposition (EMD). This method analyses the signal envelopes and decomposes the signal into several intrinsic mode functions, which makes it easy to differentiate the high-frequency and the low-frequency components [12]. The main problem with this method is that it suffers from serious modal aliasing [13,14].…”

Section: Introductionmentioning

confidence: 99%

Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform

2021

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Online detection of partial discharges (PD) is imperative for condition monitoring of high voltage equipment as well as power cables. However, heavily contaminated sites often burden the signals with various types of noise that can be challenging to remove (denoise). This paper proposes an algorithm based on the maximal overlap discrete wavelet transform (MODWT) to denoise PD signals originating from defects in power cables contaminated with various levels of noises. The three most common noise types, namely, Gaussian white noise (GWN), discrete spectral interference (DSI), and stochastic pulse shaped interference (SPI) are considered. The algorithm is applied to an experimentally acquired void-produced partial discharge in a power cable. The MODWT-based algorithm achieved a good improvement in the signal-to-noise ratio (SNR) and in the normalized correlation coefficient (NCC) for the three types of noises. The MODWT-based algorithm performance was also compared to that of the empirical Bayesian wavelet transform (EBWT) algorithm, in which the former showed superior results in denoising SPI and DSI, as well as comparable results in denoising GWN. Finally, the algorithm performance was tested on a PD signal contaminated with the three type of noises simultaneously in which the results were also superior.

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Ensemble Empirical Mode Decomposition for the denoising of partial discharges measured in UHF

Cited by 2 publications

References 20 publications

Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet

Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet

Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform

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