| Pantograph-catenary current collection quality is a significant factor that restricts and affects the power supply quality and operation speed of high-speed train.The standard deviation of contact force(CFSD)between pantograph and catenary is an important index to evaluate the current collection quality.Aiming at optimizing the current collection quality of pantograph and catenary,combined with pantograph-catenary system modeling,experimental design method and optimization algorithm,the parameters of pantograph and catenary of high-speed railway were optimized.Main work of this thesis can be summarized as follow:(1)Based on the finite element method,2D pantograph-catenary models which coupled simple and elastic chain suspension catenary with three mass pantograph was established in this thesis.The established pantograph catenary simulation model was verified by EN 50318 standard and the results of actual line.(2)Combining the central composite test design method,BP neural network and genetic algorithm,a kind of pantograph-catenary current collection quality optimization approach based on BP neural network and genetic algorithm was proposed.The optimization results show that this approach can achieve ideal results in the optimization of pantograph and catenary of high-speed railway compared with the traditional response surface method.(3)Based on the established optimization approach,the parameters of pantograph and catenary of Beijing-Tianjin line and Beijing-Shanghai line was optimized.The BP neural network model was constructed with the input of Beijing Tianjin line pantograph(SSS400 +),the leading and trailing pantograph of Beijing Shanghai line pantograph(DSA380),the initial shape of Beijing Tianjin line simple chain suspension catenary,the distance from the droppers to the positioning point,the initial length of droppers and the tension of the contact wire,messenger wire and stitch wire of Beijing Shanghai line elastic chain suspension catenary,as well as the output of the CFSD of pantograph and catenary.By searching the extreme value through genetic algorithm,the optimal value of each optimization parameter was obtained.The results showed that the CFSD of pantograph and catenary was reduced by30.05%,29.30%,20.86%,37.68%,27.23%,8.65% and 9.47% respectively.The optimization results were verified by the maximum lifting amount of the positioning point,the maximum lifting vertical displacement of the pantograph catenary contact point,the standard deviation of the contact force under different operating speeds and the tension of droppers.The results showed that the optimization results are effective except for the optimization of the tension of contact wire,messenger wire and stitch wire.(4)The evaluation method based on neural network weight matrix and Garson equation was used to quantify the relative importance of the input variables of the neural network model.The results show that the standard deviation of contact force of SSS400+ pantograph is sensitive to the changes of various parameters.For DSA380 pantograph,the quality of upper frame has a greater impact on the results.The results of catenary show that: The influence weight of catenary parameters on the CFSD of pantograph and simple chain suspension catenary: the distance between the second dropper and the positioning point > the distance between the first dropper and the positioning point > the contact wire pre-sag;The influence weight of catenary parameters on CFSD pantograph and elastic chain suspension catenary: the distance between the second dropper and the positioning point > the distance between the first dropper and the positioning point > the distance between the third dropper and the positioning point;The initial length of the third dropper > the initial length of the second dropper > the initial length of the first dropper;Contact wire tension > stitch wire tension > messenger wire tension. |
