A Capacitance Estimation Method for DC-Link Capacitors in Railways Based on Precharging Model and Low Sampling Frequency

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Abstract
DC-link capacitor is the important device and the vulnerable electronic part of the train converter. The capacitance decline of the DC-link capacitor is accelerated because of the influence of voltage, current, charge-discharge frequency, temperature and other factors. This will lead to the circuit performance degradation and even capacitor failures. Therefore, the capacitance estimation of DC-link capacitors is necessary and of great significance to the safe operation of the train. However, the voltage sensors used in the train converter have large noise and low sampling frequency, which makes it difficult to obtain accurate capacitance estimations with existing methods. In this paper, an effective capacitance estimation method is proposed to solve these problems. The pre-charging process is analyzed and an estimation model is built to weaken the impact of the sensor noise. Based on the model, a three-stage iteration algorithm is used for capacitance estimation, which can effectively search the optimal solution at a low sampling frequency and further reduce the noise influence. The proposed method was verified on a metro vehicle and compared with other algorithms. The experimental results show that the aforementioned problems can be well solved by the proposed method and the estimation error is within 2%.
Funding Information
  • National Natural Science Foundation of China (52202428)

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