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Mixed Over-Voltage Decomposition Using Atomic Decompositions Based on a Damped Sinusoids Atom Dictionary

Author

Listed:
  • Qing Yang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Jing Wang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Wenxia Sima

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Lin Chen

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Tao Yuan

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

Abstract

The main purpose of this paper is to establish a signal decomposition system aiming at mixed over-voltages in power systems. In an electric power system, over-voltage presents a great threat for the system safety. Analysis and identification of over-voltages is helpful to improve the stability and safety of power systems. Through statistical analysis of a collection of field over-voltage records, it was found that a kind of complicated signals created by mixing of multiple different over-voltages is difficult to identify correctly with current classification algorithms. In order to improve the classification and identification accuracy of over-voltages, a mixed over-voltage decomposition system based on the atomic decomposition and a damped sinusoid atom dictionary has been established. This decomposition system is optimized by using particle swarm optimization and the fast Fourier transform. Aiming at possible fault decomposition results during decomposition of the over-voltage signal, a double-atom decomposition algorithm is proposed in this paper. By taking three typical mixed over-voltages as examples, the validity of the algorithm is demonstrated.

Suggested Citation

  • Qing Yang & Jing Wang & Wenxia Sima & Lin Chen & Tao Yuan, 2011. "Mixed Over-Voltage Decomposition Using Atomic Decompositions Based on a Damped Sinusoids Atom Dictionary," Energies, MDPI, vol. 4(9), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:4:y:2011:i:9:p:1410-1427:d:14046
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    References listed on IDEAS

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    1. Jing Wang & Qing Yang & Wenxia Sima & Tao Yuan & Markus Zahn, 2011. "A Smart Online Over-Voltage Monitoring and Identification System," Energies, MDPI, vol. 4(4), pages 1-17, April.
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    Cited by:

    1. Karol Nowak & Jerzy Janiszewski & Grzegorz Dombek, 2019. "Thyristor Arc Eliminator for Protection of Low Voltage Electrical Equipment," Energies, MDPI, vol. 12(14), pages 1-15, July.
    2. Beata Palczynska, 2017. "Identification of Non-Stationary Magnetic Field Sources Using the Matching Pursuit Method," Energies, MDPI, vol. 10(5), pages 1-13, May.
    3. Kaihua Jiang & Lin Du & Huan Chen & Feng Yang & Yubo Wang, 2019. "Non-Contact Measurement and Polarity Discrimination-Based Identification Method for Direct Lightning Strokes," Energies, MDPI, vol. 12(2), pages 1-17, January.

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