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Application of VMD and Hilbert Transform Algorithms on Detection of the Ripple Components of the DC Signal

Author

Listed:
  • Derong Luo

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Ting Wu

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Ming Li

    (Zhejiang Institute of Metrology, Hangzhou 310018, China)

  • Benshun Yi

    (School of Electronic Information, Wuhan University, Wuhan 430072, China)

  • Haibo Zuo

    (State Grid Yangzhong County Electric Power Supply Company, Yangzhong 212200, China)

Abstract

Accurate detection of ripple components of the direct-current (DC) signals is essential for evaluating DC power quality. In this study, the combination algorithm based on variational mode decomposition (VMD) and Hilbert transform (HT) is applied to detect and analyze the characteristics of the ripple components of the DC disturbance signals. Firstly, the optimal modal number of VMD algorithms is comprehensively determined by observing the center frequencies of the mode components and the Index of Orthogonality (IO) of mode components. Through utilizing the VMD algorithm, the DC disturbance signal is accurately decomposed into a series of amplitude modulation-frequency modulation (AM-FM) functions. Then, the HT algorithm is applied to each AM-FM function to obtain the corresponding instantaneous amplitude and frequency, and the characteristics of DC disturbance signal are determined. Some case studies are implemented to analyze the ripple components of the DC disturbance signal with the VMD-HT and empirical mode decomposition (EMD) algorithm. Finally, the experiment results of Gree Photovoltaic Cabin have verified the feasibility and effectiveness of the proposed combination VMD-HT algorithm by comparison with EMD and the window interpolation fast Fourier transform (WIFFT) algorithms.

Suggested Citation

  • Derong Luo & Ting Wu & Ming Li & Benshun Yi & Haibo Zuo, 2020. "Application of VMD and Hilbert Transform Algorithms on Detection of the Ripple Components of the DC Signal," Energies, MDPI, vol. 13(4), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:935-:d:322753
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    References listed on IDEAS

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    1. Gramacki, Artur & Gramacki, Jarosław, 2017. "FFT-based fast bandwidth selector for multivariate kernel density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 27-45.
    2. Xu, Mengjia & Shang, Pengjian & Lin, Aijing, 2016. "Cross-correlation analysis of stock markets using EMD and EEMD," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 82-90.
    3. Stephen Whaite & Brandon Grainger & Alexis Kwasinski, 2015. "Power Quality in DC Power Distribution Systems and Microgrids," Energies, MDPI, vol. 8(5), pages 1-22, May.
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    Cited by:

    1. Andrea Mariscotti, 2021. "Power Quality Phenomena, Standards, and Proposed Metrics for DC Grids," Energies, MDPI, vol. 14(20), pages 1-41, October.
    2. Shuhao Liu & Kunlun Han & Hongzheng Li & Tengyue Zhang & Fengyuan Chen, 2023. "A Two-Terminal Directional Protection Method for HVDC Transmission Lines of Current Fault Component Based on Improved VMD-Hilbert Transform," Energies, MDPI, vol. 16(19), pages 1-21, October.

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