High-speed trains rely on pantograph-catenary systems(PCSs)to collect electrical energy from power systems.However,the dynamic interaction between the pantograph and the catenary system may become mismatched once ice ...High-speed trains rely on pantograph-catenary systems(PCSs)to collect electrical energy from power systems.However,the dynamic interaction between the pantograph and the catenary system may become mismatched once ice accumulates on the overhead conductors.More frequent arcing may occur within the PCS during train operation,posing an unpredictable threat to operational safety.Therefore,it is crucial to evaluate the ability of overhead contact system(OCS)to withstand ice-covered variability during line desgin.A new strategy is proposed to evaluate the adaptive performance of an OCS under various icing conditions.First,a dynamic model considering icing conditions is constructed to simulate the interaction within the PCS.Five different OCS structures with various icing thicknesses are studied.The parameters of the contact force within the PCS and proportion of high-possibility arcing moments are obtained.The dependence of the contact force on the icing thickness and pantograph displacement has been illustrated in the form of cloud maps.Finally,the OCS sensitivity coefficient is calculated,and ice-covered environmental adaptability assessments for the five different OCS structures are compared.展开更多
High-strength copper contact wire is of great significance to the electrified railway power supply system,which constantly provides electric power to the trains during operation.However,contact wire is subject to pres...High-strength copper contact wire is of great significance to the electrified railway power supply system,which constantly provides electric power to the trains during operation.However,contact wire is subject to pressure,vibration,and natural forces such as wind,rain,ice,etc.which inevitably result in mechanical fatigue over time.This mechanical fatigue can lead to a decrease in the mechanical strength of the contact wire,and ulti-mately lead to problems such as wire detachment,fracture,or breakage,posing a serious safety hazard to the electrified railway system.Herein,the authors propose a strategy using nanosecond pulsed laser induced breakdown spectroscopy(LIBS)combined with machine learning technique to realise a fast evaluation on the fatigue level of copper contact line.Three different fatigue levels of copper samples have been made related with the requirement of operational conditions,and a total of 898 LIBS spectra were collected.Twenty-four combinations of spectral pre-processing,feature extraction,and optimisa-tion algorithms were used to compare the recognition results with the accuracy,recall rate,and time cost taken into accounted.Results have shown that the standard normal variable transform–principal component analysis–genetic algorithm improve support vector machine(SNV-PCA-GASVM)model have presented a most satisfactory per-formance than the others.The cross-validation accuracy of the SNV-PCA-GASVM model was 92.97%while the dimensionality of input variables was reduced by 99.62%.This work is useful for the safety operation of power supply system in high speed railway,and technique development concerning the fast evaluation on materials fatigue in other industrial fields.展开更多
基金China State Railway Group Co.,Ltd.(L2022G006)Chengdu Guojia Electrical Engineering Co.,Ltd.(NEEC-2022-A04)Natural Science Foundation of Sichuan Province(2022NSFSC1863).
文摘High-speed trains rely on pantograph-catenary systems(PCSs)to collect electrical energy from power systems.However,the dynamic interaction between the pantograph and the catenary system may become mismatched once ice accumulates on the overhead conductors.More frequent arcing may occur within the PCS during train operation,posing an unpredictable threat to operational safety.Therefore,it is crucial to evaluate the ability of overhead contact system(OCS)to withstand ice-covered variability during line desgin.A new strategy is proposed to evaluate the adaptive performance of an OCS under various icing conditions.First,a dynamic model considering icing conditions is constructed to simulate the interaction within the PCS.Five different OCS structures with various icing thicknesses are studied.The parameters of the contact force within the PCS and proportion of high-possibility arcing moments are obtained.The dependence of the contact force on the icing thickness and pantograph displacement has been illustrated in the form of cloud maps.Finally,the OCS sensitivity coefficient is calculated,and ice-covered environmental adaptability assessments for the five different OCS structures are compared.
基金National Natural Science Foundation of China,Grant/Award Number:52322704Science and Technology Research and Development Project of China National Railway Group Corporation Limited,Grant/Award Number:L2022G006。
文摘High-strength copper contact wire is of great significance to the electrified railway power supply system,which constantly provides electric power to the trains during operation.However,contact wire is subject to pressure,vibration,and natural forces such as wind,rain,ice,etc.which inevitably result in mechanical fatigue over time.This mechanical fatigue can lead to a decrease in the mechanical strength of the contact wire,and ulti-mately lead to problems such as wire detachment,fracture,or breakage,posing a serious safety hazard to the electrified railway system.Herein,the authors propose a strategy using nanosecond pulsed laser induced breakdown spectroscopy(LIBS)combined with machine learning technique to realise a fast evaluation on the fatigue level of copper contact line.Three different fatigue levels of copper samples have been made related with the requirement of operational conditions,and a total of 898 LIBS spectra were collected.Twenty-four combinations of spectral pre-processing,feature extraction,and optimisa-tion algorithms were used to compare the recognition results with the accuracy,recall rate,and time cost taken into accounted.Results have shown that the standard normal variable transform–principal component analysis–genetic algorithm improve support vector machine(SNV-PCA-GASVM)model have presented a most satisfactory per-formance than the others.The cross-validation accuracy of the SNV-PCA-GASVM model was 92.97%while the dimensionality of input variables was reduced by 99.62%.This work is useful for the safety operation of power supply system in high speed railway,and technique development concerning the fast evaluation on materials fatigue in other industrial fields.
