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Harmonics and Interharmonics Detection Based on Synchrosqueezing Adaptive S-Transform

Author

Listed:
  • Zhaoyu Lou

    (Key Lab of Power Electronics for Energy Conservation and Motor Drive of Hebei Province, Yanshan University, Qinhuangdao 066004, China)

  • Pan Li

    (Key Lab of Power Electronics for Energy Conservation and Motor Drive of Hebei Province, Yanshan University, Qinhuangdao 066004, China)

  • Kang Ma

    (Key Lab of Power Electronics for Energy Conservation and Motor Drive of Hebei Province, Yanshan University, Qinhuangdao 066004, China)

  • Fengcheng Teng

    (Key Lab of Power Electronics for Energy Conservation and Motor Drive of Hebei Province, Yanshan University, Qinhuangdao 066004, China)

Abstract

The integration of renewable energy generation and nonlinear power electronic equipment into the grid brings about complex harmonics and interharmonics problems. The amplitude and frequency of harmonics and interharmonics should be detected by high time-frequency (T-F) resolution methods owing to their time-varying transient features. In this paper, a synchrosqueezing adaptive S-transform (SAST) method is proposed to detect the parameters of harmonics. Firstly, the time-frequency spectrum (TFS) of the harmonic signals is acquired by an adaptive S-transform (AST) algorithm. The TFS results are then subjected to synchronous compression, so as to achieve higher time-frequency representation precision. The detection results of the simulation signals show that SAST can achieve a better time-frequency resolution than the S-transform (ST) and synchrosqueezing short-time Fourier transform (SSTFT). In addition, the application of SAST to the analysis of experimental signals also suggests its superiority in the parameter detection of harmonics, especially for the time-varying interharmonics.

Suggested Citation

  • Zhaoyu Lou & Pan Li & Kang Ma & Fengcheng Teng, 2022. "Harmonics and Interharmonics Detection Based on Synchrosqueezing Adaptive S-Transform," Energies, MDPI, vol. 15(13), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4539-:d:844438
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    References listed on IDEAS

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    1. Yijin Li & Jianhua Lin & Geng Niu & Ming Wu & Xuteng Wei, 2021. "A Hilbert–Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids," Energies, MDPI, vol. 14(16), pages 1-16, August.
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    Cited by:

    1. Marcin Maciążek, 2022. "Active Power Filters and Power Quality," Energies, MDPI, vol. 15(22), pages 1-4, November.

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