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Analysis of Wide-Frequency Dense Signals Based on Fast Minimization Algorithm

Author

Listed:
  • Zehui Yuan

    (The School of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Zheng Liao

    (The School of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Haiyan Tu

    (The School of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Yuxin Tu

    (The School of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Wei Li

    (Electric Power Research Institute of State Grid Hubei Electric Power Co., Ltd., Wuhan 430000, China)

Abstract

To improve the detection speed for wide-frequency dense signals (WFDSs), a fast minimization algorithm (FMA) was proposed in this study. Firstly, this study modeled the WFDSs and performed a Taylor-series expansion of the sampled model. Secondly, we simplified the sampling model based on the augmented Lagrange multiplier (ALM) method and then calculated the augmented Lagrange function of the sampling model. Finally, according to the alternating minimization strategy, the Lagrange multiplier vector and the sparse block phasor in the function were iterated individually to realize the measurement of the original signal components. The results show that the algorithm improved the analysis accuracy of the WFDS by 35% to 46% on the IEEE C37.118.1a-2014 standard for the wide-frequency noise test, harmonic modulation test, and step-change test, providing a theoretical basis for the development of the P-class phasor measurement unit (PMU).

Suggested Citation

  • Zehui Yuan & Zheng Liao & Haiyan Tu & Yuxin Tu & Wei Li, 2022. "Analysis of Wide-Frequency Dense Signals Based on Fast Minimization Algorithm," Energies, MDPI, vol. 15(15), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5618-:d:878812
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    References listed on IDEAS

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    1. Jun-Hao Chen & Kuang-Hsiung Tan & Yih-Der Lee, 2022. "Intelligent Controlled DSTATCOM for Power Quality Enhancement," Energies, MDPI, vol. 15(11), pages 1-19, May.
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