Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/20/6540 |
| _version_ | 1797514757126750208 |
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| author | Mohammed A. Shams Hussein I. Anis Mohammed El-Shahat |
| author_facet | Mohammed A. Shams Hussein I. Anis Mohammed El-Shahat |
| author_sort | Mohammed A. Shams |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:35:49Z |
| format | Article |
| id | doaj.art-936531d6124a4288bdbbe87bc3b83d4b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T06:35:49Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-936531d6124a4288bdbbe87bc3b83d4b2023-11-22T18:04:44ZengMDPI AGEnergies1996-10732021-10-011420654010.3390/en14206540Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet TransformMohammed A. Shams0Hussein I. Anis1Mohammed El-Shahat2Electrical Power Department, Faculty of Engineering, Cairo Univrsity, Giza 12613, EgyptElectrical Power Department, Faculty of Engineering, Cairo Univrsity, Giza 12613, EgyptElectrical Power Department, Faculty of Engineering, Cairo Univrsity, Giza 12613, EgyptOnline 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.https://www.mdpi.com/1996-1073/14/20/6540partial dischargedenoisingGaussian white noisestochastic pulse interferencediscrete spectral interferencehigh voltage cables |
| spellingShingle | Mohammed A. Shams Hussein I. Anis Mohammed El-Shahat Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform Energies partial discharge denoising Gaussian white noise stochastic pulse interference discrete spectral interference high voltage cables |
| title | Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform |
| title_full | Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform |
| title_fullStr | Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform |
| title_full_unstemmed | Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform |
| title_short | Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform |
| title_sort | denoising of heavily contaminated partial discharge signals in high voltage cables using maximal overlap discrete wavelet transform |
| topic | partial discharge denoising Gaussian white noise stochastic pulse interference discrete spectral interference high voltage cables |
| url | https://www.mdpi.com/1996-1073/14/20/6540 |
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