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Harmonic Detection for Power Grids Using Adaptive Variational Mode Decomposition

Author

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  • Guowei Cai

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Lixin Wang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Deyou Yang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Zhenglong Sun

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Bo Wang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

Abstract

The harmonic pollution problem in power grids has become increasingly prominent with the large-scale application of power electronic equipment, nonlinear loads, and renewable energy. This study proposes a method based on adaptive variational mode decomposition (AVMD) and Hilbert transform (HT) that is applicable to harmonic detection in power system. The AVMD method constructs and solves the constrained variational model. Then, a single-frequency harmonic component with stable features can be obtained. The proposed method can effectively avoid the recursive process in empirical mode decomposition (EMD). In this study, the variational mode decomposition algorithm is used to obtain the periodic harmonic components concurrently. Subsequently, the characteristic parameters of each harmonic component are extracted via HT. Simulation analysis and measured data verify the validity and feasibility of the proposed algorithm. Compared with the detection results obtained using the EMD algorithm, the proposed method is proven to exhibit stronger applicability to harmonic detection in power system.

Suggested Citation

  • Guowei Cai & Lixin Wang & Deyou Yang & Zhenglong Sun & Bo Wang, 2019. "Harmonic Detection for Power Grids Using Adaptive Variational Mode Decomposition," Energies, MDPI, vol. 12(2), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:2:p:232-:d:197313
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    References listed on IDEAS

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    1. Liu, W.Y. & Zhang, W.H. & Han, J.G. & Wang, G.F., 2012. "A new wind turbine fault diagnosis method based on the local mean decomposition," Renewable Energy, Elsevier, vol. 48(C), pages 411-415.
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    3. Roberto Perillo Barbosa da Silva & Rodolfo Quadros & Hamid Reza Shaker & Luiz Carlos Pereira da Silva, 2019. "Analysis of the Electrical Quantities Measured by Revenue Meters Under Different Voltage Distortions and the Influences on the Electrical Energy Billing," Energies, MDPI, vol. 12(24), pages 1-18, December.
    4. Minwu Chen & Yinyu Chen & Mingchi Wei, 2019. "Modeling and Control of a Novel Hybrid Power Quality Compensation System for 25-kV Electrified Railway," Energies, MDPI, vol. 12(17), pages 1-23, August.

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