A Capacitance Estimation Method for DC-Link Capacitors in Rail Transit Based on Maximum Likelihood and Variable Convergence Factor

Film capacitor is widely used as dc-link capacitor in rail transit converters. Its capacitance decreases under hard working conditions and the capacitance estimation is necessary for condition monitoring. Currently, there are several challenges in rail transit applications: strong noise, low sampling frequency, and modification. In this article, a capacitance estimation method based on maximum likelihood and variable convergence factor is proposed. The input and capacitor voltages are used for modeling. The initial values of model parameters and signal noise are evaluated by recursive least square (RLS). Then, the maximum likelihood (ML) with Newton–Raphson iteration is used for parameter calculation, and a variable convergence factor is designed to solve the numerical divergence problem. The capacitance is accurately estimated and the challenges are well addressed. The proposed method was verified on the vehicles of Shenzhen Metro Group, and the error was within 2%. Performance analysis showed that the proposed method had certain immunity in the case of early signal faults and resistance offsets. The comparison results proved the advantages of the proposed method in accuracy and convergence speed.