Abstract

This paper presents a life prediction method based on the parameters of the actual operation history data collected by the existing converter power unit sensors. Firstly, the characteristics of junction temperature curves of forced air-cooled radiator and power unit are extracted, and the deep learning neural network architecture is constructed based on the characteristics. Then the thermoelectric coupling model of power unit based on thermal resistance calculation theory is established, and the cumulative loss is obtained from the measured data. The deep learning network is trained and the model prediction is verified. Finally, the power unit loss distribution under different setting temperature thresholds and the correlation analysis with radiator parameters are obtained, which provides a feasible scheme for parameter setting and life prediction.

Highlights

  • This paper presents a life prediction method based on the parameters of the actual operation history data collected by the existing converter power unit sensors

  • For IGBT, the aging failure of bonding wire can be evaluated by saturated conduction pressure drop (Vୡୣୱୟ୲), and the fatigue aging of weld layer fatigue is positively correlated with the junction-case thermal resistance (T୨ିୡ)[2]

  • The two methods are material fatigue caused by thermal stress, and the temperature curve of power units is essentially determined by the heating process caused by transmission power loss and heat dissipation process cooling caused by system operation

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Summary

Structure of energy-fed power supply system

Regenerative braking is widely used in urban rail transit vehicles. According to IEC and correlative regulations, the allowable voltage fluctuation range of DC1500V overhead catenary is 1000 ~ 1800V. The existing power supply system uses diode rectifier power supply, when the regenerative braking energy cannot be fully absorbed by adjacent trains, the grid voltage will rise beyond the limit, leading to the failure of regenerative braking and loss. The core of the medium voltage inverter feedback device is the three-phase voltage source inverter, which is connected in parallel with the diode rectifier in the traction power supply system. When the train is braking, the excess energy on the DC side is fed back to the medium voltage AC grid [1]. When the train brake and feedback the braking energy to the system, the converter works in the inverter condition to transmit the energy to the medium voltage grid

Influence factors of converter lifetime
Forced air-cooling and characteristics of historical data
Data set and deep neural network establishment
Establishment of thermoelectric coupling model
Training verification and error analysis
Findings
Conclusion
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