Partial Discharge Signal Denoising Based on Singular Value Decomposition and Empirical Wavelet Transform

Zhong, Jun; Bi, Xiaowen; Shu, Qin; Chen, Minwei; Zhou, Dianbo; Zhang, Dakun

doi:10.1109/tim.2020.2996717

Cited by 79 publications

(35 citation statements)

References 20 publications

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“…In the following, we use a dynamic thresholding based activation function, which sets near-zero inputs (between −τ and τ ) to zeros, since these inputs have a high probability of being noise without any useful information according to the theory of signal de-noising. Specifically, our idea is borrowed from the Wavelet threshold de-noising method [25]. Because wavelets localize data features to different scales, important signal or data features can be preserved after removing noise.…”

Section: Accuracy Of Rf Fingerprint Recognitionmentioning

confidence: 99%

DSLN: Securing Internet of Things Through RF Fingerprint Recognition in Low-SNR Settings

Lin

et al. 2022

IEEE Internet Things J.

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The explosive growth of Internet of things (IoT) has mandated the security of data access. Although authentication methods can enhance network security, their vulnerability to malicious attacks may be a barrier for the wide deployments in IoT scenarios. To address the security issue, we advocate the use of physical layer security through radio-frequency (RF) fingerprint recognition. Observing that most RF fingerprint recognition methods show a degradation of performance under low signal-to-noise ratio (SNR) environments, we present a dynamic shrinkage learning network (DSLN) to enhance security for IoT applications, particularly in the setting of low SNR. We design a novel dynamic shrinkage threshold for improving the accuracy of recognition under low-SNR environments. Additionally, we design an identity shortcut for reducing the running time of RF fingerprint recognition. In comparison with convolutional neural network (CNN), recurrent neural network (RNN) and a hybrid CNN+RNN network (CRNN), our proposed DSLN yields accuracy improvements of up to 20%. Moreover, DSLN can reduce running time by up to 60%, indicating its great potential to a real-time IoT system, e.g., an intelligent automotive system.

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Section: Accuracy Of Rf Fingerprint Recognitionmentioning

confidence: 99%

DSLN: Securing Internet of Things Through RF Fingerprint Recognition in Low-SNR Settings

Lin

et al. 2022

IEEE Internet Things J.

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“…In engineering practice, most measured PD signals are oscillation exponential attenuated type signals [6]. Therefore, we use the single exponential decaying oscillation 1 D and double exponential decaying oscillation 2 D to simulate the PD signal:…”

Section: Simulated Pd Signalmentioning

confidence: 99%

“…Due to its advantages such as high detection sensitivity and strong anti-interference ability, UHF detection method is widely used in the detection of partial discharge in GIS [4]. The field detection environment is accompanied by various background noise, among which Gaussian white noise and the periodic narrowband noise are the most serious [5]- [6]. Ultra-high frequency (UHF) signals generated by PD are extremely weak and easy to be covered by noise, which greatly increases the difficulty of PD UHF signal detection.…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Signal Extraction Method Based on EDSSV and Low Rank RBF Neural Network

et al. 2021

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“…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]. Artificial neural networks (ANN) are also used widely with various algorithms and structures.…”

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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Partial Discharge Signal Denoising Based on Singular Value Decomposition and Empirical Wavelet Transform

Cited by 79 publications

References 20 publications

DSLN: Securing Internet of Things Through RF Fingerprint Recognition in Low-SNR Settings

DSLN: Securing Internet of Things Through RF Fingerprint Recognition in Low-SNR Settings

Partial Discharge Signal Extraction Method Based on EDSSV and Low Rank RBF Neural Network

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

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