Denoising of electrical shock fault signal based on empirical mode decomposition thresholding

Wang, Jinli; Liu, Yongmei; Han, Xiaohui; Du, Songhuai; Wang, Li; Su, Juan; Liu, Guangeng; Guan, Haiou

doi:10.1109/ciced.2016.7576002

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…The fifth layer is the output layer, which is the output of all the input residual current signals after processing. As shown in (6).…”

Section: A Structure Of the Dfnnmentioning

confidence: 92%

“…The traditional RCD only acts according to the peak current, which easily leads to false action and refusing action, and these modern signal processing methods overcome the shortcomings of traditional RCD and further improve the reliability of RCD. For example, J. Wang and H. Guan proposed an EMD-thresholding (EMD-T) residual current detection model based on the Hilbert-Huang transform, which can extract the residual current more effectively than the traditional FIR filtering method [6], [7]. C. Li proposed the combination of wavelet transform (WT) and back propagation neural network (BPNN) to preprocess the signal with multiscale wavelets, and then used the processed signal as a sample for detection and analysis by BPNN [8].…”

Section: Introductionmentioning

confidence: 99%

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Residual Current Detection Method Based on Variational Modal Decomposition and Dynamic Fuzzy Neural Network

et al. 2021

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To further improve the detection ability of residual current in low-voltage distribution networks, an adaptive residual current detection method based on variational mode decomposition (VMD) and dynamic fuzzy neural network (DFNN) is proposed. First, using the general K -value selection method of VMD proposed in this study, the residual current signal is decomposed into K intrinsic mode functions (IMFs). By introducing the cross-correlation coefficient R and the time-domain energy entropy ratio E as two classification indexes, IMFs are divided into three categories: effective IMFs, noise IMFs and aliasing IMFs. Then, the aliasing IMFs are denoised by recursive least squares (RLS), and the denoised IMFs are superimposed with the effective IMFs to obtain the reconstructed signal. Finally, the dynamic fuzzy neural network (DFNN) is adjusted by the minimum output method to achieve the detection of the reconstructed residual current signal, and the network is used to predict the residual current according to the detection results. The detection results of the simulation and measured data show that the proposed algorithm has high detection accuracy and is superior to the wavelet neural network, empirical mode decompositionthresholding, and wavelet entropy-auto encoder-back propagation neural network methods in terms of mean square error, goodness of fit and running time. This method provides a reference for further research on new adaptive residual current protection devices.

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“…The fifth layer is the output layer, which is the output of all the input residual current signals after processing. As shown in (6).…”

Section: A Structure Of the Dfnnmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Residual Current Detection Method Based on Variational Modal Decomposition and Dynamic Fuzzy Neural Network

et al. 2021

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Artificial intelligence recognition method of living body electric shock in low voltage distribution networks

Wei

Guo

2020

2020 IEEE 1st China International Youth Conference on Electrical Engineering (CIYCEE)

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ECG signal de-noising using complementary ensemble empirical mode decomposition and Kalman smoother

Keshavamurthy

Eshwarappa

2017

2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

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Denoising of electrical shock fault signal based on empirical mode decomposition thresholding

Cited by 3 publications

References 8 publications

Residual Current Detection Method Based on Variational Modal Decomposition and Dynamic Fuzzy Neural Network

Residual Current Detection Method Based on Variational Modal Decomposition and Dynamic Fuzzy Neural Network

Artificial intelligence recognition method of living body electric shock in low voltage distribution networks

ECG signal de-noising using complementary ensemble empirical mode decomposition and Kalman smoother

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