A Novel DC-Link Capacitor Condition Monitoring Method Based on VBAKF Algorithm With Locally Variable Prior Matrix

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Abstract
DC-link capacitors in rail transit are susceptible to failure under hard working conditions, and it is important to estimate the capacitance for safe operation purpose. However, current approaches struggle to obtain accurate capacitance estimations in rail applications because of noise interference and modification restriction. Therefore, an effective method is proposed for capacitance estimation of dc-link capacitors in rail transit. The variational Bayesian adaptive Kalman filtering is designed for parameter calculation, and a novel update mechanism of prior matrix is introduced in the fixed-point iteration to improve calculation accuracy and noise immunity. The capacitance can be precisely calculated by input and capacitor voltages. The proposed method was tested on several metro vehicles, and the average error was 0.74%. Its feasibility within a certain range of signal-noise ratio, voltage deviation, and resistance drift was also proved. Comparison illustrated that the proposed method has advantages in estimation accuracy and convergence stability.
Funding Information
  • National Natural Science Foundation of China (52202428)
  • Hunan Natural Science Foundation Project (2023JJ40750)
  • Youth Talent-Program Supported by China Railway Society (B26094CRSQT202407)
  • Institute Local Cooperation Major Project by Chinese Academy of Engineering (2024-DFZD-57)
  • Independent Exploration Innovation Project for Graduate Students of Central South University (2025ZZTS0762)

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