Transistor arc Model of DC Contactor and Its Application in Remaining Electric Life Prediction

With the wide application of DC contactors in power systems, their remaining life prediction has become an important issue to ensure the safe operation of system. This paper presents a residual life prediction method for DC contactors based on the transistor arc model and xgboost algorithm. Firstly, the transistor arc model is used to model the voltage and current signals of the DC contactor, and the key features related to the arc characteristics are extracted. Then, the xgboost model is used to carry out in-depth training and learning on these features to achieve accurate prediction of the remaining life of DC contactors. The experimental results demonstrate that the proposed method can effectively capture key information in the arc process, offering significant advantages in prediction accuracy and robustness compared to traditional methods. This study presents a novel approach and method for health management and life prediction of DC contactors.