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On the Application of Joint-Domain Dictionary Mapping for Multiple Power Disturbance Assessment

Author

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  • Delong Cai

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronics Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Kaicheng Li

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronics Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Shunfan He

    (School of Computer Science, South-Central University for Nationalities, Wuhan 430074, China)

  • Yuanzheng Li

    (School of Automation, Ministry of Education Key Laboratory of Image Processing and Intelligence Control, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yi Luo

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronics Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

This paper proposes a joint-domain dictionary mapping method to obtain high assessment accuracy of multiple power disturbances. Firstly, in order to achieve resolutions in both the time and frequency domains, a joint-domain dictionary is proposed which consists of a discrete Hartley base and an identity matrix. Due to the low correlation between the discrete Hartley base and the identity matrix, the joint-domain dictionary mapping can separately capture the approximations of the sinusoidal components and transients. Since the mapping coefficients contain the physical quantities, the eigenvalues of each component can be effectively estimated. A quantified eigenvalue classifier was designed for identifying power disturbances using the estimated eigenvalues. The proposed method was compared with several advanced methods through simulated power disturbances under different noise conditions, and actual data from the Institute of Electrical and Electronics Engineers Power and Energy Society database. The results reveal that the joint-domain dictionary mapping technique shows good performance on parameter estimation and recognition precision, even dealing with complicated multiple power disturbances.

Suggested Citation

  • Delong Cai & Kaicheng Li & Shunfan He & Yuanzheng Li & Yi Luo, 2018. "On the Application of Joint-Domain Dictionary Mapping for Multiple Power Disturbance Assessment," Energies, MDPI, vol. 11(2), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:347-:d:130016
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    References listed on IDEAS

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    1. Nantian Huang & Hua Peng & Guowei Cai & Jikai Chen, 2016. "Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm," Energies, MDPI, vol. 9(11), pages 1-21, November.
    2. Huihui Wang & Ping Wang & Tao Liu, 2017. "Power Quality Disturbance Classification Using the S-Transform and Probabilistic Neural Network," Energies, MDPI, vol. 10(1), pages 1-19, January.
    3. Misael Lopez-Ramirez & Luis Ledesma-Carrillo & Eduardo Cabal-Yepez & Carlos Rodriguez-Donate & Homero Miranda-Vidales & Arturo Garcia-Perez, 2016. "EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments," Energies, MDPI, vol. 9(7), pages 1-15, July.
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    Cited by:

    1. Alexandre Serrano-Fontova & Pablo Casals Torrens & Ricard Bosch, 2019. "Power Quality Disturbances Assessment during Unintentional Islanding Scenarios. A Contribution to Voltage Sag Studies," Energies, MDPI, vol. 12(16), pages 1-21, August.

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