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Fault Diagnosis of Capacitance Aging in DC Link Capacitors of Voltage Source Inverters Using Evidence Reasoning Rule

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  • Linhao Liao
  • Haibo Gao
  • Yelan He
  • Xiaobin Xu
  • Zhiguo Lin
  • Yajie Chen
  • Fubing You

Abstract

Capacitance aging of DC link capacitors in voltage source inverters (VSIs) is a common fault which can lead to instability of the DC voltage. In such a failure state, although the VSI can still work, its performance gradually deteriorates, resulting in a shorter service life of the equipment. Here, an online monitoring and fault diagnosis method for capacitance aging based on evidence reasoning (ER) rule is presented. Features from the DC link voltage data with different levels of capacitance aging are extracted, and data features are generated as pieces of diagnostic evidence, which are then combined according to the ER rule. Finally, capacitance aging fault levels were estimated using the combined results. This method has better diagnostic performance compared to a backpropagation (BP) neural network approach and can be used to flexibly define the relative weighting of each evidence parameter depending on the application. This approach can therefore be widely used for fault diagnosis of an array of different devices.

Suggested Citation

  • Linhao Liao & Haibo Gao & Yelan He & Xiaobin Xu & Zhiguo Lin & Yajie Chen & Fubing You, 2020. "Fault Diagnosis of Capacitance Aging in DC Link Capacitors of Voltage Source Inverters Using Evidence Reasoning Rule," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, March.
  • Handle: RePEc:hin:jnlmpe:5724019
    DOI: 10.1155/2020/5724019
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    Cited by:

    1. David Gonzalez-Jimenez & Jon del-Olmo & Javier Poza & Fernando Garramiola & Izaskun Sarasola, 2021. "Machine Learning-Based Fault Detection and Diagnosis of Faulty Power Connections of Induction Machines," Energies, MDPI, vol. 14(16), pages 1-21, August.

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