Effective Denoising of Multi-Source Partial Discharge Signals via an Improved Power Spectrum Segmentation Method Based on Normalized Spectral Kurtosis
In the field of partial discharge (PD) analysis, traditional methods typically employ single-source PD signal-processing techniques. However, these approaches exhibit significant limitations when applied to transformers with relatively complex structures. To overcome these limitations and achieve pr...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/12/3798 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850164559205105664 |
|---|---|
| author | Baojia Chen Kaiwen Li Yipeng Guo |
| author_facet | Baojia Chen Kaiwen Li Yipeng Guo |
| author_sort | Baojia Chen |
| collection | DOAJ |
| description | In the field of partial discharge (PD) analysis, traditional methods typically employ single-source PD signal-processing techniques. However, these approaches exhibit significant limitations when applied to transformers with relatively complex structures. To overcome these limitations and achieve precise characterization of composite PD signatures, this study proposes an improved power spectrum segmentation method (IPSK) based on spectral kurtosis. Firstly, normalized power spectral kurtosis is used to select the appropriate parameters. Then, through the improved power spectrum segmentation method, the segmentation frequency band with the least noise is obtained. Finally, the instantaneous signal components with physical significance are obtained by reconstructing each frequency band through inverse fast Fourier transform. By analyzing the simulated signals and measured data of partial discharge, the proposed method is compared with EWT, AEFD, VMD, and CEEMDAN. The results show that IPSK has a good suppression effect on noise interference. |
| format | Article |
| id | doaj-art-ab2cf39d448d4d2b832c9eade596eefb |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ab2cf39d448d4d2b832c9eade596eefb2025-08-20T02:21:58ZengMDPI AGSensors1424-82202025-06-012512379810.3390/s25123798Effective Denoising of Multi-Source Partial Discharge Signals via an Improved Power Spectrum Segmentation Method Based on Normalized Spectral KurtosisBaojia Chen0Kaiwen Li1Yipeng Guo2Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance, China Three Gorges University, Yichang 443002, ChinaCollege of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, ChinaHubei Key Laboratory of Hydroelectric Machinery Design and Maintenance, China Three Gorges University, Yichang 443002, ChinaIn the field of partial discharge (PD) analysis, traditional methods typically employ single-source PD signal-processing techniques. However, these approaches exhibit significant limitations when applied to transformers with relatively complex structures. To overcome these limitations and achieve precise characterization of composite PD signatures, this study proposes an improved power spectrum segmentation method (IPSK) based on spectral kurtosis. Firstly, normalized power spectral kurtosis is used to select the appropriate parameters. Then, through the improved power spectrum segmentation method, the segmentation frequency band with the least noise is obtained. Finally, the instantaneous signal components with physical significance are obtained by reconstructing each frequency band through inverse fast Fourier transform. By analyzing the simulated signals and measured data of partial discharge, the proposed method is compared with EWT, AEFD, VMD, and CEEMDAN. The results show that IPSK has a good suppression effect on noise interference.https://www.mdpi.com/1424-8220/25/12/3798transformermulti-source partial dischargeimproved power spectrum segmentationpower spectral kurtosis |
| spellingShingle | Baojia Chen Kaiwen Li Yipeng Guo Effective Denoising of Multi-Source Partial Discharge Signals via an Improved Power Spectrum Segmentation Method Based on Normalized Spectral Kurtosis Sensors transformer multi-source partial discharge improved power spectrum segmentation power spectral kurtosis |
| title | Effective Denoising of Multi-Source Partial Discharge Signals via an Improved Power Spectrum Segmentation Method Based on Normalized Spectral Kurtosis |
| title_full | Effective Denoising of Multi-Source Partial Discharge Signals via an Improved Power Spectrum Segmentation Method Based on Normalized Spectral Kurtosis |
| title_fullStr | Effective Denoising of Multi-Source Partial Discharge Signals via an Improved Power Spectrum Segmentation Method Based on Normalized Spectral Kurtosis |
| title_full_unstemmed | Effective Denoising of Multi-Source Partial Discharge Signals via an Improved Power Spectrum Segmentation Method Based on Normalized Spectral Kurtosis |
| title_short | Effective Denoising of Multi-Source Partial Discharge Signals via an Improved Power Spectrum Segmentation Method Based on Normalized Spectral Kurtosis |
| title_sort | effective denoising of multi source partial discharge signals via an improved power spectrum segmentation method based on normalized spectral kurtosis |
| topic | transformer multi-source partial discharge improved power spectrum segmentation power spectral kurtosis |
| url | https://www.mdpi.com/1424-8220/25/12/3798 |
| work_keys_str_mv | AT baojiachen effectivedenoisingofmultisourcepartialdischargesignalsviaanimprovedpowerspectrumsegmentationmethodbasedonnormalizedspectralkurtosis AT kaiwenli effectivedenoisingofmultisourcepartialdischargesignalsviaanimprovedpowerspectrumsegmentationmethodbasedonnormalizedspectralkurtosis AT yipengguo effectivedenoisingofmultisourcepartialdischargesignalsviaanimprovedpowerspectrumsegmentationmethodbasedonnormalizedspectralkurtosis |