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A Principal Components Rearrangement Method for Feature Representation and Its Application to the Fault Diagnosis of CHMI

Author

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  • Zhuo Liu

    (Department of Electrical Automation, Shanghai Maritime University, Shanghai 201306, China)

  • Tianzhen Wang

    (Department of Electrical Automation, Shanghai Maritime University, Shanghai 201306, China)

  • Tianhao Tang

    (Department of Electrical Automation, Shanghai Maritime University, Shanghai 201306, China)

  • Yide Wang

    (Institut d’Electronique et Télécommunications de Rennes, UMR CNRS 6164, Polytech Nantes, Rue Christian Pauc, BP 50609, 44306 Nantes CEDEX 3, France)

Abstract

Cascaded H-bridge Multilevel Inverter (CHMI) is widely used in industrial applications thanks to its many advantages. However, the reliability of a CHMI is decreased with the increase of its levels. Fault diagnosis techniques play a key role in ensuring the reliability of a CHMI. The performance of a fault diagnosis method depends on the characteristics of the extracted features. In practice, some extracted features may be very similar to ensure a good diagnosis performance at some H-bridges of CHMI. The situation becomes even worse in the presence of noise. To fix these problems, in this paper, signal denoising and data preprocessing techniques are firstly developed. Then, a Principal Components Rearrangement method (PCR) is proposed to represent the different features sufficiently distinct from each other. Finally, a PCR-based fault diagnosis strategy is designed. The performance of the proposed strategy is compared with other fault diagnosis strategies, based on a 7-level CHMI hardware platform.

Suggested Citation

  • Zhuo Liu & Tianzhen Wang & Tianhao Tang & Yide Wang, 2017. "A Principal Components Rearrangement Method for Feature Representation and Its Application to the Fault Diagnosis of CHMI," Energies, MDPI, vol. 10(9), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1273-:d:109919
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    References listed on IDEAS

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    1. Hsueh-Hsien Chang & Nguyen Viet Linh, 2017. "Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems," Energies, MDPI, vol. 10(5), pages 1-20, April.
    2. Yonglong Yan & Jian Li & David Wenzhong Gao, 2014. "Condition Parameter Modeling for Anomaly Detection in Wind Turbines," Energies, MDPI, vol. 7(5), pages 1-17, May.
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    Cited by:

    1. Liang, Jinping & Zhang, Ke & Al-Durra, Ahmed & Zhou, Daming, 2020. "A novel fault diagnostic method in power converters for wind power generation system," Applied Energy, Elsevier, vol. 266(C).

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