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Quasi-Periodic Harmonic Feature Extraction of Power Signals via Improved Scaling-Basis Chirplet Transform.

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Title: Quasi-Periodic Harmonic Feature Extraction of Power Signals via Improved Scaling-Basis Chirplet Transform.
Authors: Hu, Yihao1 (AUTHOR), Yu, Jiexiao1 (AUTHOR)
Source: Energies (19961073). May2026, Vol. 19 Issue 9, p2122. 28p.
Subject Terms: *Time-frequency analysis, *Signal reconstruction, *Automatic tracking
Abstract: The increasing randomness of source–load fluctuations and the rapid proliferation of power-electronic devices have introduced wide dynamic ranges, fast time-varying behaviors, and strong stochastic characteristics into power signals, leading to severe measurement deviations in existing metering equipment. Conventional modeling and feature analysis methods based on fixed-frequency steady-state assumptions are inadequate for characterizing such non-stationary behaviors, making the underlying causes of metering deviations difficult to identify. To address this issue, we propose a modeling and dynamic time–frequency feature extraction method for complex non-stationary power signals. First, the operating characteristics of power equipment are analyzed to identify the fundamental non-stationary features of power signals, based on which a quasi-periodic harmonic signal model is established. Then, the scaling-basis chirplet transform is employed to intuitively represent the time–frequency structure, while a ridge detection algorithm is incorporated to quantitatively characterize the time–frequency trajectories and instantaneous amplitude features. Finally, to cope with the limited availability of power signal measurements, a non-stationary component reconstruction method based on cross-correntropy is developed. Experimental results from multiple datasets, including field-measured signals, demonstrate that the proposed method enables effective dynamic monitoring and reconstruction of non-stationary components, offering significant advantages in both time–frequency analysis capabilities and reconstruction accuracy. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 193716018
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: Quasi-Periodic Harmonic Feature Extraction of Power Signals via Improved Scaling-Basis Chirplet Transform.
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  Data: <searchLink fieldCode="AR" term="%22Hu%2C+Yihao%22">Hu, Yihao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Jiexiao%22">Yu, Jiexiao</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 9, p2122. 28p.
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  Data: *<searchLink fieldCode="DE" term="%22Time-frequency+analysis%22">Time-frequency analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Signal+reconstruction%22">Signal reconstruction</searchLink><br />*<searchLink fieldCode="DE" term="%22Automatic+tracking%22">Automatic tracking</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The increasing randomness of source–load fluctuations and the rapid proliferation of power-electronic devices have introduced wide dynamic ranges, fast time-varying behaviors, and strong stochastic characteristics into power signals, leading to severe measurement deviations in existing metering equipment. Conventional modeling and feature analysis methods based on fixed-frequency steady-state assumptions are inadequate for characterizing such non-stationary behaviors, making the underlying causes of metering deviations difficult to identify. To address this issue, we propose a modeling and dynamic time–frequency feature extraction method for complex non-stationary power signals. First, the operating characteristics of power equipment are analyzed to identify the fundamental non-stationary features of power signals, based on which a quasi-periodic harmonic signal model is established. Then, the scaling-basis chirplet transform is employed to intuitively represent the time–frequency structure, while a ridge detection algorithm is incorporated to quantitatively characterize the time–frequency trajectories and instantaneous amplitude features. Finally, to cope with the limited availability of power signal measurements, a non-stationary component reconstruction method based on cross-correntropy is developed. Experimental results from multiple datasets, including field-measured signals, demonstrate that the proposed method enables effective dynamic monitoring and reconstruction of non-stationary components, offering significant advantages in both time–frequency analysis capabilities and reconstruction accuracy. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193716018
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19092122
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 28
        StartPage: 2122
    Subjects:
      – SubjectFull: Time-frequency analysis
        Type: general
      – SubjectFull: Signal reconstruction
        Type: general
      – SubjectFull: Automatic tracking
        Type: general
    Titles:
      – TitleFull: Quasi-Periodic Harmonic Feature Extraction of Power Signals via Improved Scaling-Basis Chirplet Transform.
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      – PersonEntity:
          Name:
            NameFull: Hu, Yihao
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          Name:
            NameFull: Yu, Jiexiao
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          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
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              Value: 19
            – Type: issue
              Value: 9
          Titles:
            – TitleFull: Energies (19961073)
              Type: main
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