The Denoising Method for Transformer Partial Discharge Based on the Whale VMD Algorithm Combined with Adaptive Filtering and Wavelet Thresholding
Partial discharge (PD) is the primary factor causing insulation degradation in transformers. However, the collected signals of partial discharge are often contaminated with significant noise. This makes it difficult to extract the PD signal and hinders subsequent signal analysis and processing. This...
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| Format: | Article |
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/19/8085 |
| _version_ | 1797575129220251648 |
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| author | Zhongdong Wu Zhuo Zhang Li Zheng Tianfeng Yan Chunyang Tang |
| author_facet | Zhongdong Wu Zhuo Zhang Li Zheng Tianfeng Yan Chunyang Tang |
| author_sort | Zhongdong Wu |
| collection | DOAJ |
| description | Partial discharge (PD) is the primary factor causing insulation degradation in transformers. However, the collected signals of partial discharge are often contaminated with significant noise. This makes it difficult to extract the PD signal and hinders subsequent signal analysis and processing. This paper proposes a denoising method for transformer partial discharge based on the Whale VMD algorithm combined with adaptive filtering and wavelet thresholding (WVNW). First, the WOA is used to optimize the important parameters of the VMD. The selected mode components from the VMD decomposition are then subjected to preliminary denoising based on the kurtosis criterion. The reconstructed signal is further denoised using the Adaptive Filter (NLMS) algorithm to remove narrowband interference noise. Finally, the residual white noise is eliminated using the Wavelet Thresholding algorithm. In simulation experiments and practical measurements, the proposed method is compared quantitatively with previous methods, VMD-WT, and EMD-WT, based on metrics such as SNR, RMSE, NCC, and NRR. The results indicate that the WVNW method effectively suppresses noise interference and restores the original PD signal waveform with high waveform similarity while preserving a significant amount of local discharge signal features. |
| first_indexed | 2024-03-10T21:35:22Z |
| format | Article |
| id | doaj.art-113ca886673949f890abb5c2772bf944 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T21:35:22Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-113ca886673949f890abb5c2772bf9442023-11-19T15:02:31ZengMDPI AGSensors1424-82202023-09-012319808510.3390/s23198085The Denoising Method for Transformer Partial Discharge Based on the Whale VMD Algorithm Combined with Adaptive Filtering and Wavelet ThresholdingZhongdong Wu0Zhuo Zhang1Li Zheng2Tianfeng Yan3Chunyang Tang4School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaSchool of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaSchool of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaSchool of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaSchool of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaPartial discharge (PD) is the primary factor causing insulation degradation in transformers. However, the collected signals of partial discharge are often contaminated with significant noise. This makes it difficult to extract the PD signal and hinders subsequent signal analysis and processing. This paper proposes a denoising method for transformer partial discharge based on the Whale VMD algorithm combined with adaptive filtering and wavelet thresholding (WVNW). First, the WOA is used to optimize the important parameters of the VMD. The selected mode components from the VMD decomposition are then subjected to preliminary denoising based on the kurtosis criterion. The reconstructed signal is further denoised using the Adaptive Filter (NLMS) algorithm to remove narrowband interference noise. Finally, the residual white noise is eliminated using the Wavelet Thresholding algorithm. In simulation experiments and practical measurements, the proposed method is compared quantitatively with previous methods, VMD-WT, and EMD-WT, based on metrics such as SNR, RMSE, NCC, and NRR. The results indicate that the WVNW method effectively suppresses noise interference and restores the original PD signal waveform with high waveform similarity while preserving a significant amount of local discharge signal features.https://www.mdpi.com/1424-8220/23/19/8085partial dischargeVMDWOAtransformeradaptive filteringdenoising |
| spellingShingle | Zhongdong Wu Zhuo Zhang Li Zheng Tianfeng Yan Chunyang Tang The Denoising Method for Transformer Partial Discharge Based on the Whale VMD Algorithm Combined with Adaptive Filtering and Wavelet Thresholding Sensors partial discharge VMD WOA transformer adaptive filtering denoising |
| title | The Denoising Method for Transformer Partial Discharge Based on the Whale VMD Algorithm Combined with Adaptive Filtering and Wavelet Thresholding |
| title_full | The Denoising Method for Transformer Partial Discharge Based on the Whale VMD Algorithm Combined with Adaptive Filtering and Wavelet Thresholding |
| title_fullStr | The Denoising Method for Transformer Partial Discharge Based on the Whale VMD Algorithm Combined with Adaptive Filtering and Wavelet Thresholding |
| title_full_unstemmed | The Denoising Method for Transformer Partial Discharge Based on the Whale VMD Algorithm Combined with Adaptive Filtering and Wavelet Thresholding |
| title_short | The Denoising Method for Transformer Partial Discharge Based on the Whale VMD Algorithm Combined with Adaptive Filtering and Wavelet Thresholding |
| title_sort | denoising method for transformer partial discharge based on the whale vmd algorithm combined with adaptive filtering and wavelet thresholding |
| topic | partial discharge VMD WOA transformer adaptive filtering denoising |
| url | https://www.mdpi.com/1424-8220/23/19/8085 |
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