Effective Denoising of Multi-Source Partial Discharge Signals via an Improved Power Spectrum Segmentation Method Based on Normalized Spectral Kurtosis.

Baojia Chen, Kaiwen Li, Yipeng Guo

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

3 authors.

Baojia ChenHubei Key Laboratory of Hydroelectric Machinery Design and Maintenance, China Three Gorges University, Yichang 443002, China.

Kaiwen LiCollege of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China.ORCID 0009-0008-2996-0503

Yipeng GuoHubei Key Laboratory of Hydroelectric Machinery Design and Maintenance, China Three Gorges University, Yichang 443002, China.

Funding

he National Natural Science Foundation of China 51975324the Key Research and Development Program of Hubei Province 519753242023BAB129

6 · The paper itself

Abstract

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.

Indexed as

improved power spectrum segmentationmulti-source partial dischargepower spectral kurtosistransformer

Identifiers

PMID40573685

DOI10.3390/s25123798

PMCPMC12197294

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