Analysis and Research on Influencing Factors of Pantograph-catenary Sliding Electrical Contact Failure

doi:10.11985/2023.02.029

Abstract

Abstract:

In order to study the influence degree of influencing elements on pantograph-catenary sliding electrical contact failure, the self-made sliding electrical contact experimental machine is used to measure the experimental data. Firstly, the correlation between each factor and current stability coefficient is analyzed by using the Spearman’s rank correlation coefficient. Secondly, the significance of the influencing elements of pantograph-catenary sliding electrical contact failure is ranked by the use of RF, LightGBM and XGBoost algorithms. Finally, the grid search and cross validation strategies are used to optimize the parameters of the XGBoost model. In order to understand the influence of influencing factors on the prediction results of GC-XGBoost model, SHAP attribution theory is added to explain and analyze the model. The results show that the order of importance of influencing factors of pantograph-catenary sliding electrical contact failure is fluctuating pressure amplitude, contact current, sliding speed and fluctuation frequency from high to low. Compared with other models, the evaluation index values of XGBoost model are the best, and the fitting degree of the optimized GC-XGBoost model is improved by 5.72%, and the error accuracy is the lowest. SHAP model reveals the influence law of influencing factors on sliding electrical contact failure from the whole and local aspects, and helps researchers better solve the contact failure problem of pantograph-catenary system of electrified railway.

Key words: Pantograph-catenary sliding electrical contact, current stability coefficient, Spearman’s rank correlation coefficient, grid search, cross validation

CLC Number:

TM501

HUI Lichuan, CHEN Xuelian, MENG Sibo. Analysis and Research on Influencing Factors of Pantograph-catenary Sliding Electrical Contact Failure[J]. Journal of Electrical Engineering, 2023, 18(2): 277-286.

Figures/Tables 17

模型	MAE	MAPE	RMSE	$R 2$
SVR	0.064 0	0.135 4	0.004 1	0.758 1
RF	0.048 5	0.070 9	0.002 1	0.858 7
LightGBM	0.099 5	0.142 5	0.009 9	0.420 4
XGBoost	0.026 5	0.063 5	0.000 7	0.888 2

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参数名	默认值	最优值
colsample_bytree	1	0.82
subsample	1	0.53
n_estimators	100	95
learning_rate	0.1	0.06
max_depth	3	4