Abstract：High-speed trains acquire electrical energy through sliding contact between the pantograph and catenary. Good current-collection performance between the pantograph and the contact wire is necessary for the safe and stable operation of the train. The probability of electric arc generation increases with the development of electric locomotives towards high-speed and heavy loads. Numerous factors contribute to generating electric arcs, but these ultimately stem from the contact resistance between the slider and the contact wire. It is essential to establish an accurate prediction model for contact resistance based on analyzing its characteristics. However, the analysis and modeling of contact resistance characteristics were isolated in previous research. This paper applies the conclusions from the mechanism analysis as prior knowledge to the modeling process of contact resistance. Methods for improving the accuracy of the contact resistance prediction model are explored. First, the universal conclusions in previous research on electrical contact are summarized. (1) As the contact pressure increases, the contact resistance decreases. (2) Temperature rise significantly affects contact material properties, wear mechanisms, and wear surface morphology, which can be considered an independent variable. Considering that the temperature of the contact core of the friction pair is not easy to measure and the conductive current loss is closely correlated with temperature rise, the conductive current loss as an independent variable can be used to replace the temperature rise. Subsequently, the effects of fluctuating load, sliding speed, and contact current on contact resistance are studied through the sliding electrical contact experiment machine. The relationship between wear mechanism, electric arc discharge, and contact resistance evolution is analyzed with surface morphology. The following conclusions are drawn. (1) As the fluctuating load increases, electrical contact stability weakens, mainly impacting contact resistance. (2) As the contact current increases, the current lubrication effect strengthens, which is conducive to reducing contact resistance. (3) The increase in speed weakens the stability of contact, the contact surface deteriorates, and the contact resistance increases. Finally, the partial derivative of contact resistance with respect to contact load is taken as a non-positive number, and the square of the loss current used to replace temperature rise should be regarded as an independent model input variable, called prior knowledge (Ⅰ) and (Ⅱ), respectively. They are applied to establishing the RBF network model of contact resistance. The improved carnivorous plant optimization algorithm (ICPA) is used to optimize the hyperparameters of the RBF network to improve the accuracy of the contact resistance prediction model. Simulation results indicate that prior knowledge (Ⅰ) can confine the direction of weight adjustment of the RBF model, reduce the search space of weights, help to prevent overfitting, and enhance the convergence speed of the ICPA-RBF model. However, the accuracy of the final approximation needs to be improved. The MSE index and determination coefficient R2 index of the RBF network without prior knowledge (Ⅱ) are inferior to those with prior knowledge (Ⅱ). The RBF network, adding the square of the loss current as an input variable, considers the energy of electric arc discharge indirectly, thereby providing a more comprehensive description of the contact surface state and stronger performance robustness of the prediction model. Finally, hypothesis testing is conducted under other working conditions. The results demonstrate the effectiveness of the contact resistance prediction model and the prior knowledge (Ⅱ) in improving the generalization ability and enhancing the model's robustness.
时光, 陈翼喆, 李莹, 张国威. 基于先验知识的弓网接触电阻预测模型精度提升方法[J]. 电工技术学报, 2024, 39(14): 4535-4546.
Shi Guang, Chen Yizhe, Li Ying, Zhang Guowei. Accuracy Improvement Method of Pantograph Contact Resistance Prediction Model Based on Prior Knowledge. Transactions of China Electrotechnical Society, 2024, 39(14): 4535-4546.
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2024, Vol. 39 
