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The important thing to do after we detect the existence of partial discharges inside an isolation system is signal processing. One form of signal processing that needs to be carried out on the generated waveform is denoising or noise removal from the wave obtained. In this study, an experiment will be conducted to denounce partial discharge waves u...
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... Early detection of insulation defects is critical because PD can cause damage to underground cables. Extensive research on the phenomenon of PD that has been carried out includes feature extraction techniques [2][3][4][5], defect location and identification techniques [6,7], physical and chemical processes [8,9], denoising techniques [10][11][12][13][14][15], and pulse classification techniques [16][17][18][19]. ...
Discrete Wavelet Transform (DWT) de-noising method is widely used for one-dimension partial discharge (PD) signals measured from medium voltage underground cable. However, DWT de-noising has several drawbacks that prevent the DWT de-noising from improving its de-noising effectiveness In DWT de-noising, the two most important parameters are decomposition level and mother wavelet. The aforementioned parameters must be varied according to the noise level in the measured PD signal in order to effectively suppress the noise of the measured PD signal. In this paper, an adaptive DWT de-noising algorithm based on the Absolute Difference Optimizing (ADO) technique is presented to effectively suppress the varying noise levels in measured PD signal. First, the measured PD signal will be de-noised using a Daubechies 3 (db3) mother wavelet and a DWT decomposition level ranging from 1 to 10. Second, the de-noised PD signal will be subjected to the ADO technique. The sum of the absolute difference of local maxima in the de-noised PD signal will be used as an indicator to select the best decomposition level for the de-noised PD signal. Finally, the best-selected de-noised PD signal by using the ADO technique will be used to estimate the PD location on the underground cable. The results of PD location error using the ADO technique and normal DWT de-noising will be compared. The findings show that the ADO-based adaptive DWT de-noising algorithm significantly improved the de-noising process of the measured PD signal.
... According to the wavelet denoising principle, it is known that the wavelet denoising effect depends largely on the appropriate threshold and threshold function. Hard thresholding and soft thresholding are two commonly used thresholding functions, and the reconstructed signal after hard thresholding has disadvantages such as discontinuity, oscillation and distortion phenomenon; soft thresholding function [13], although continuous, often appears deviation will lead to the reconstructed signal with high frequency part information loss, edge blurring and other problems. Due to these defects, the traditional threshold function needs to be improved to construct a new threshold function. ...
INTRODUCTION: In the online English learning platform, noise interference makes people unable to hear the content of English teaching clearly, which leads to a great reduction in the efficiency of English learning. In order to improve the voice quality of online English learning platform, the speech enhancement method of the online English learning platform based on deep neural network is studied.OBJECTIVES: This paper proposes a deep neural network-based speech enhancement method for online English learning platform in order to obtain more desirable results in the application of speech quality optimization.METHODS: The optimized VMD (Variable Modal Decomposition) algorithm is combined with the Moth-flame optimization algorithm to find the optimal solution to obtain the optimal value of the decomposition mode number and the penalty factor of the variational modal decomposition algorithm, and then the optimized variational modal decomposition algorithm is used to filter the noise information in the speech signal; Through the network speech enhancement method based on deep neural network learning, the denoised speech signal is taken as the enhancement target to achieve speech enhancement.RESULTS: The research results show that the method not only has significant denoising ability for speech signal, but also after this method is used, PESQ value of speech quality perception evaluation of speech signal is greater than 4.0dB, the spectral features are prominent, and the speech quality is improved.CONCLUSION: Through experiments from three perspectives: speech signal denoising, speech quality enhancement and speech spectrum information, the usability of the method in this paper is confirmed.
... The hardware method example is using the bridge PD registration circuit [1]. Software methods are using neural networks [17,18], wavelet transform [19]. Besides, the cases of multiple defect presence are also studied [20,21]. ...
