基于先验知识的弓网接触电阻预测模型精度提升方法研究

doi:10.19595/j.cnki.1000-6753.tces.231065

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Abstract

High-speed trains acquire electrical energy through sliding contact between the pantograph and catenary, and good current-collection performance between the pantograph and the contact wire is a necessary condition for the safe and stable operation of the train. With the development of electric locomotives towards high-speed and heavy-load, the probability of electric arc generation increases. Numerous factors contribute to the generation of electric arcs, but these ultimately stem from the contact resistance between the slider and the contact wire. Therefore, it is very important to establish an accurate prediction model for contact resistance based on the analysis of its characteristics. However, in previous research, the analysis and modeling of contact resistance characteristics were isolated from each other. This paper attempts to effectively combine the two, applying the conclusions obtained from the mechanism analysis as prior knowledge to the modeling process of contact resistance, and exploring methods to further improve the accuracy of the contact resistance prediction model.
First, the universal conclusions in previous research on electrical contact were summarized: (1) As the contact pressure increases, the contact resistance decreases. (2) Temperature rising significantly affects contact material properties, wear mechanisms, and wear surface morphology, and can be considered as an independent variable. Considering the temperature of the contact core of the friction pair is not easy to measure and the conductive current loss has a close correlation with temperature rising, the conductive current loss as an independent variable can be used to replace the temperature rising. Subsequently, through the sliding electrical contact experiment machine, the effects of fluctuating load, sliding speed, and contact current on contact resistance were studied, and the relationship between wear mechanism, electric arc discharge, and contact resistance evolution law was analyzed in combination with surface morphology. The following conclusions were drawn: (1) As the fluctuating load increases, the stability of electrical contact weakens, and it mainly has a negative effect on contact resistance. (2) As the contact current increases, the current lubrication effect strengthens, which is conducive to the reduction of contact resistance. (3) The increase of speed weakens the stability of contact, the contact surface deteriorates, and the contact resistance increases. Finally, based on the aforementioned two conclusions, 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 which is used to replace the temperature rising should be considered as an independent model input variable. They are called prior knowledge (I) and (II) respectively and applied to process of establishing the RBF network model of contact resistance. The improved Carnivorous Plant Optimization Algorithm (ICPA) is used to optimize the hyperparameters of 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, but it does not improve the final approximation accuracy. The MSE index and determination coefficient R² index of the RBF network without prior knowledge (II) are inferior to those of the RBF network with prior knowledge (II). The RBF network which adds the square of the loss current as an input variable considers the energy of electric arc discharge indirectly, thus providing a more comprehensive description of the contact surface state and stronger performance robustness of the prediction model. Finally, in order to further verify the generalization ability and effectiveness of the model, hypothesis testing is conducted under other working conditions. The results not only demonstrate the effectiveness of the contact resistance prediction model but also reflect the effectiveness of prior knowledge (II) in improving the generalization ability and enhancing the performance robustness of the model.

Key words： Contact resistance prior knowledge RBF neural networks carnivorous plant algorithm hypothesis testing

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TM501

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	Shi Guang
	Chen Yizhe
	Li Ying
	Zhang Guowei

Cite this article:

Shi Guang,Chen Yizhe,Li Ying等. Research on Accuracy Improvement Method of Pantograph Contact Resistance Prediction Model Based on Prior Knowledge[J]. Transactions of China Electrotechnical Society, 0, (): 9035-35.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.231065 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/9035

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