Threshold selection for wavelet denoising of partial discharge data

Agoris, P.D.; Meijer, S.; Gulski, Ε.; Smit, J.J.

doi:10.1109/elinsl.2004.1380450

Cited by 13 publications

(7 citation statements)

References 9 publications

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“…db20 is adopted as mother wavelet with decomposition level of 5. Soft heuristic SURE threshold [17] is selected to shrink wavelet coefficients in different decomposition layers. The denoising results of the proposed SSA-based method and TWS method are plotted in Fig.…”

Section: Methodsmentioning

confidence: 99%

Environmental noise elimination of heart sound based on singular spectrum analysis

Zeng

Dong

2014

2014 Cairo International Biomedical Engineering Conference (CIBEC)

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Automatic heart sound (HS) auscultation enjoys advantageous features in terms of high intelligence, accuracy, and efficiency over traditional way. Unfortunately, sensitivity to noise corruption exposes automatic auscultation to misdiagnosis risks since original pathological features are vulnerable to miscellaneous HS noise. Therefore, HS denoising is pivotal to obtain qualified HS signal for further analysis and precise diagnosis. Traditional wavelet shrinkage (TWS) method achieves good performance on eliminating Gaussian distributed noise, yet it is powerless against randomly distributed environmental noise. To tackle such a bottleneck problem, an environmental HS noise elimination method based on singular spectrum analysis (SSA) is proposed in this paper. With the aid of singular value decomposition (SVD), effective eigenvalues related to the principle components (PC) of pure HS signal are selected to reconstruct HS signal while eliminating environmental noise efficiently. Validated using both normal and pathological HS signals with diversified environmental noises, the proposed method exhibits better denoising performance than TWS in most cases. As such, this work provides an attractive alternative for HS environmental HS noise denoising.

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Section: Methodsmentioning

confidence: 99%

Environmental noise elimination of heart sound based on singular spectrum analysis

Zeng

Dong

2014

2014 Cairo International Biomedical Engineering Conference (CIBEC)

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“…As an important signal processing method, wavelet transform has the characteristics of multi-layer and multi-resolution analysis. By zooming and panning the wavelet function, signal details in the time and frequency domains can be analyzed [26]- [28]. Wavelet transform mainly includes continuous wavelet transform (CWT), discrete wavelet transform (DWT) and discrete wavelet packet transform (DWPT).…”

Section: The Signal Noise Reduction Based On Dwtmentioning

confidence: 99%

Fault Detection on Insulated Overhead Conductors Based on DWT-LSTM and Partial Discharge

Zuo

et al. 2020

IEEE Access

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Insulated conductors can improve the stability of power transmission and reduce the construction space compared with traditional bare conductors. Therefore, insulated conductors are used more and more in overhead power transmission. However, a major challenge of using insulated overhead conductors (IOC) is that the ordinary protection devices are not able to detect the phase-to-ground faults and something, such as tree branch, hitting conductor events. This may cause an accident such as power failure or electrical fire and result in serious damage. In this paper, a new approach, which is based on Discrete Wavelet Transform (DWT) and Long Short Term Memory network (LSTM) for detecting of IOC fault according to partial discharge, is presented. Firstly, the original signal is denoised by DWT. Secondly, the denoised signal is decomposed and extracted features on different layers by DWT. Finally, IOC fault is detected by LSTM. This method can improve the detection accuracy of IOC fault which is tested on the ENET public data set and compared with other classification methods. INDEX TERMS Insulated overhead conductors, partial discharge, discrete wavelet transform, long short term memory network, fault detection.

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“…the threshold algorithm for real part and imaginary part may be different. For example, δ r is calculated by using Stein's unbiased risk estimation (SURE) algorithm and δ i by minimax algorithm 25, 28, 29. Calculation of the complex mask operator M r + i M i .Mask operator is the noise estimation of wavelet coefficients, which is the calculation of absolute deviation of detail coefficients. It is defined as where S represents wavelet transform, J is decomposition level.The real part and imaginary part of coefficients are regarded as uncorrelated ones as well.…”

Section: Construction Of Complex Thresholdmentioning

confidence: 99%

Suppressing white‐noise in partial discharge measurements—part 1: construction of complex Daubechies wavelet and complex threshold

Tang

Zhou

2009

Int Trans Elec Energy Syst

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SUMMARYOnline partial discharge (PD) detection still remains a very challenging task because of the strong electromagnetic interferences. In this paper, a new method of de-noising, using complex Daubechies wavelet (CDW) transform, has been proposed. It is a relatively recent enhancement to the real-valued wavelet transform because of two important properties, which are nearly shift invariant and availability of phase information. Those properties give CDW transform superiority over other real-valued wavelet transform. On the basis of CDW transform, complex threshold algorithm and combined information are devised. In order to take into account the difference of the real part and imaginary part of complex wavelet coefficients, complex threshold is devised to modify the real part and imaginary part respectively. Because the restored signal by inverse CDW transform is complex one, combined information is devised to combine the real part and imaginary part of complex signal to construct a real one, furthermore making full use of the simple information from complex signal.

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Threshold selection for wavelet denoising of partial discharge data

Cited by 13 publications

References 9 publications

Environmental noise elimination of heart sound based on singular spectrum analysis

Environmental noise elimination of heart sound based on singular spectrum analysis

Fault Detection on Insulated Overhead Conductors Based on DWT-LSTM and Partial Discharge

Suppressing white‐noise in partial discharge measurements—part 1: construction of complex Daubechies wavelet and complex threshold

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