Enhanced Noise Suppression in Partial Discharge Signals via SVD and VMD with Wavelet Thresholding
Partial discharge evaluation is a principal method for assessing insulation conditions in power transformers. Traditional singular value decomposition (SVD) approaches, however, face issues like high residual noise and loss of signal details in white noise suppression. This article introduces an adv...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/5676986 |
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| author | Hailong Wang Yongliang Yao Guangdong Zhang Jidong Pan Longlong Gao Hai Jin Chuang Wang |
| author_facet | Hailong Wang Yongliang Yao Guangdong Zhang Jidong Pan Longlong Gao Hai Jin Chuang Wang |
| author_sort | Hailong Wang |
| collection | DOAJ |
| description | Partial discharge evaluation is a principal method for assessing insulation conditions in power transformers. Traditional singular value decomposition (SVD) approaches, however, face issues like high residual noise and loss of signal details in white noise suppression. This article introduces an advanced denoising algorithm integrating SVD, variational mode decomposition (VMD), and wavelet thresholding to effectively address mixed noise in on-site power transformer assessments. The algorithm initially employs SVD to suppress mixed noise, specifically targeting narrowband interference by decomposing the noisy signal and nullifying the corresponding singular values. Post-SVD, the signal is further processed through VMD, with its modal components refined via wavelet thresholding. The final reconstruction of these denoised components effectively eliminates white noise. Applied to an input signal with a signal-to-noise ratio of -27.593 dB, the proposed method achieves a postdenoising ratio of 13.654 dB. Comparative analysis indicates its superiority over existing algorithms in mitigating white noise and narrowband interference and more accurately restoring the partial discharge signal. |
| format | Article |
| id | doaj-art-70fb8006b5f2428ebdd481dac1889690 |
| institution | OA Journals |
| issn | 1687-5605 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-70fb8006b5f2428ebdd481dac18896902025-08-20T02:06:47ZengWileyModelling and Simulation in Engineering1687-56052024-01-01202410.1155/2024/5676986Enhanced Noise Suppression in Partial Discharge Signals via SVD and VMD with Wavelet ThresholdingHailong Wang0Yongliang Yao1Guangdong Zhang2Jidong Pan3Longlong Gao4Hai Jin5Chuang Wang6Electric Power Research Institute of State Grid Gansu Electric Power CompanyState Grid Gansu Electric Power CompanyElectric Power Research Institute of State Grid Gansu Electric Power CompanyCollege of Electrical Engineering and Information EngineeringCollege of Electrical Engineering and Information EngineeringCollege of Electrical Engineering and Information EngineeringSchool of Electrical EngineeringPartial discharge evaluation is a principal method for assessing insulation conditions in power transformers. Traditional singular value decomposition (SVD) approaches, however, face issues like high residual noise and loss of signal details in white noise suppression. This article introduces an advanced denoising algorithm integrating SVD, variational mode decomposition (VMD), and wavelet thresholding to effectively address mixed noise in on-site power transformer assessments. The algorithm initially employs SVD to suppress mixed noise, specifically targeting narrowband interference by decomposing the noisy signal and nullifying the corresponding singular values. Post-SVD, the signal is further processed through VMD, with its modal components refined via wavelet thresholding. The final reconstruction of these denoised components effectively eliminates white noise. Applied to an input signal with a signal-to-noise ratio of -27.593 dB, the proposed method achieves a postdenoising ratio of 13.654 dB. Comparative analysis indicates its superiority over existing algorithms in mitigating white noise and narrowband interference and more accurately restoring the partial discharge signal.http://dx.doi.org/10.1155/2024/5676986 |
| spellingShingle | Hailong Wang Yongliang Yao Guangdong Zhang Jidong Pan Longlong Gao Hai Jin Chuang Wang Enhanced Noise Suppression in Partial Discharge Signals via SVD and VMD with Wavelet Thresholding Modelling and Simulation in Engineering |
| title | Enhanced Noise Suppression in Partial Discharge Signals via SVD and VMD with Wavelet Thresholding |
| title_full | Enhanced Noise Suppression in Partial Discharge Signals via SVD and VMD with Wavelet Thresholding |
| title_fullStr | Enhanced Noise Suppression in Partial Discharge Signals via SVD and VMD with Wavelet Thresholding |
| title_full_unstemmed | Enhanced Noise Suppression in Partial Discharge Signals via SVD and VMD with Wavelet Thresholding |
| title_short | Enhanced Noise Suppression in Partial Discharge Signals via SVD and VMD with Wavelet Thresholding |
| title_sort | enhanced noise suppression in partial discharge signals via svd and vmd with wavelet thresholding |
| url | http://dx.doi.org/10.1155/2024/5676986 |
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