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A Supraharmonic Detection Method Based on EMD-wavelet Thresholding and Adaptive Grouping Aggregation
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Yuandong MING, Jianfeng LIU, Yifan FAN, Yining SONG
Journal of Power Supply | 2026, 24(7) : 222 - 231
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Journal of Power Supply | 2026, 24(7): 222-231
• Power Electronic Dominated Power Systems •
A Supraharmonic Detection Method Based on EMD-wavelet Thresholding and Adaptive Grouping Aggregation
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Yuandong MING, Jianfeng LIU, Yifan FAN, Yining SONG
Affiliations
  • School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Published: 2026-07-30 doi: 10.13234/j.issn.2095-2805.2026.7.222
Outline
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During the detection of supraharmonics, noise affects the results, and existing harmonic grouping methods were not entirely suitable for their analysis. Consequently, a supraharmonic detection method based on empirical mode decomposition (EMD)-wavelet thresholding and adaptive grouping aggregation was proposed. Initially, high-frequency intrinsic mode function (IMF) components containing significant noise from the EMD method were denoised using wavelet thresholding. The denoised IMF components were then combined with the original low-frequency IMF components to form the final denoised signal. Subsequently, an appropriate grouping bandwidth was determined based on the switching frequency of the supraharmonic sources, and the supraharmonics within the denoised signal spectrum were grouped and aggregated outward from the characteristic frequency center. Finally, simulation and experiments validated the aforementioned method, indicating the feasibility of the EMD-wavelet thresholding and adaptive grouping aggregation approach for supraharmonic detection.

Supraharmonics  /  noise  /  grouping aggregation  /  switching frequency  /  grouping bandwidth  /  harmonic analysis
明远东, 刘建锋, 范一凡, 宋伊宁. 基于EMD-小波阈值与自适应分组聚合的超高次谐波检测方法. 电源学报, 2026 , 24 (7) : 222 -231 . DOI: 10.13234/j.issn.2095-2805.2026.7.222
Yuandong MING, Jianfeng LIU, Yifan FAN, Yining SONG. A Supraharmonic Detection Method Based on EMD-wavelet Thresholding and Adaptive Grouping Aggregation[J]. Journal of Power Supply, 2026 , 24 (7) : 222 -231 . DOI: 10.13234/j.issn.2095-2805.2026.7.222
Funding
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  • National Natural Science Foundation of China(51777119)
Appendix
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Year 2026 volume 24 Issue 7
28
5
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Article Info
doi: 10.13234/j.issn.2095-2805.2026.7.222
  • Receive Date：2024-07-25
  • Online Date：2026-10-29
  • Published：2026-07-30
Article Data
Affiliations
History
  • Received：2024-07-25
  • Revised：2024-08-26
  • Accepted：2024-11-15
Funding
National Natural Science Foundation of China(51777119)
Affiliations
    School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
References
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https://castjournals.cast.org.cn/joweb/dyxb/EN/10.13234/j.issn.2095-2805.2026.7.222
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