Optimisation of current collection quality of high-speed pantograph-catenary system using the combination of artificial neural network and genetic algorithm

References

• Lee JH, Park TW, Oh HK, et al. Analysis of dynamic interaction between catenary and pantograph with experimental verification and performance evaluation in new high-speed line. Veh Syst Dyn. 2015;53(8):1117–1134.
   Web of Science ®Google Scholar
• Prado M, Pozo SP, Perez-Blanca A, et al. Analysis of contact forces between the pantograph and the overhead conductor rail using a validated finite element model. Eng Struct. 2020;225(1):111265.
   Google Scholar
• Nåvik P, Rønnquist A, Stichel S. The use of dynamic response to evaluate and improve the optimization of existing soft railway catenary systems for higher speeds. Proc Inst Mech Eng F J Rail Rapid Transit. 2016;230(4):1388–1396.
   Web of Science ®Google Scholar
• EN 50367. (2012). Railway applications-current collection systems-technical criteria for the interaction between pantograph and overhead line. European Committee for Electrotechnical Standardization.
   Google Scholar
• Pombo J, Ambrósio J. Influence of pantograph suspension characteristics on the contact quality with the catenary for high speed trains. Comput Struct. 2012;110-111:32–42.
   Web of Science ®Google Scholar
• Park TJ, Han CS, Jang JH. Dynamic sensitivity analysis for the pantograph of a high-speed rail vehicle. J Sound Vib. 2003;266(2):235–260.
   Web of Science ®Google Scholar
• Wu M, Liu Y, Xu X. Sensitivity analysis and optimization parameters of high speed pantograph-catenary system. Chin J Theor Appl Mech. 2021;53(1):75–83.
   Google Scholar
• Lee JH, Kim YG, Paik JS, et al. Performance evaluation and design optimization using differential evolutionary algorithm of the pantograph for the high-speed train. J Mech Sci Technol. 2012;26(10):3253–3260.
   Web of Science ®Google Scholar
• Ambrósio J, Pombo J, Pereira M. Optimization of high-speed railway pantographs for improving pantograph-catenary contact. Theor App Mech Lett. 2013;3(1):013006.
   Google Scholar
• Zhang J, Liu W, Zhang Z. Sensitivity analysis and research on optimisation methods of design parameters of high-speed railway catenary. IET Electr Syst. 2019;9(3):150–156.
   Web of Science ®Google Scholar
• Cho YH, Lee K, Park Y, et al. Influence of contact wire pre-sag on the dynamics of pantograph–railway catenary. Int J Mech Sci. 2010;52(11):1471–1490.
   Web of Science ®Google Scholar
• Zhang W, Mei G, Zeng J. A study of pantograph/catenary system dynamics with influence of presag and irregularity of contact wire. Veh Syst Dyn. 2002;37(sup1):593–604.
   Google Scholar
• Gregori S, Tur M, Nadal E, et al. An approach to geometric optimisation of railway catenaries. Veh Syst Dyn. 2018;56(8):1162–1186.
   Web of Science ®Google Scholar
• Gregori S, Gil J, Tur M, et al. Analysis of the overlap section in a high-speed railway catenary by means of numerical simulations. Eng Struct. 2020;221:110963–14.
   Web of Science ®Google Scholar
• Gregori S, Tur M, Nadal E, et al. Fast simulation of the pantograph–catenary dynamic interaction. Finite Elem Anal Des. 2017;129:1–13.
   Web of Science ®Google Scholar
• Huang HX, Li JC, Xiao CL. A proposed iteration optimization approach integrating backpropagation neural network with genetic algorithm. Expert Syst Appl. 2015;42(1):146–155.
   Web of Science ®Google Scholar
• Zhang G, Zhang Z, Guo J, et al. Modeling and optimization of medium-speed WEDM process parameters for machining SKD11. Mater Manuf Processes. 2013;28(10):1124–1132.
   Web of Science ®Google Scholar
• Esmaeili R, Dashtbayazi MR. Modeling and optimization for microstructural properties of Al/SiC nanocomposite by artificial neural network and genetic algorithm. Expert Syst Appl. 2014;41(13):5817–5831.
   Web of Science ®Google Scholar
• EN 50318. 2018 Railway applications - Current collection systems - Validation of simulation of the dynamic interaction between pantograph and overhead contact line.
   Google Scholar
• Bruni S, Ambrosio J, Carnicero A, … Zhang W. The results of the pantograph–catenary interaction benchmark. Veh Syst Dyn. 2015;53(3):412–435.
   Web of Science ®Google Scholar
• Cho YH. Numerical simulation of the dynamic responses of railway overhead contact lines to a moving pantograph,: considering a nonlinear dropper. J Sound Vib. 2008;315(3):433–454.
   Web of Science ®Google Scholar
• Zhou N, Li R, Zhang W. Modeling and simulation of catenary based on negative sag method. J Traffic Transp Eng. 2009;9(4):28–32.
   Google Scholar
• UIC799. (2002). Characterristics of a.c. overhead contact systems for high-speed lines worked at speeds of over 200km/h.
   Google Scholar
• Rahimi MH, Shayganmanesh M, Noorossana R, et al. Modelling and optimization of laser engraving qualitative characteristics of Al-SiC composite using response surface methodology and artificial neural networks. Opt Laser Technol. 2019;112:65–76.
   Web of Science ®Google Scholar
• Zhao H, Wang Y, Song J, et al. The pollutant concentration prediction model of NNP-BPNN based on the INI algorithm,: AW method and neighbor-PCA. J Ambient Intell Humaniz Comput. 2019;10(8):3059–3065.
   Web of Science ®Google Scholar
• Tan M, He G, Li X, et al. Prediction of the effects of preparation conditions on pervaporation performances of polydimethylsiloxane (PDMS)/ceramic composite membranes by backpropagation neural network and genetic algorithm. Sep Purif Technol. 2012;89:142–146.
   Web of Science ®Google Scholar
• Singh B, Misra JP. Surface finish analysis of wire electric discharge machined specimens by RSM and ANN modeling. Measurement ( Mahwah N J). 2019;137:225–237.
   Google Scholar
• Park D, Cha J, Kim M, et al. Multi-objective optimization and comparison of surrogate models for separation performances of cyclone separator based on CFD,: RSM, GMDH-neural network, back propagation-ANN and genetic algorithm. Eng Appl Comput Fluid Mech. 2020;14(1):180–201.
   Google Scholar
• Zhao H, Xiao X, Qi G, et al. Analysis of fatigue life of catenary dropper for high-speed railway. Engineering Journal of Wuhan University. 2019;52(4):351–357.
   Google Scholar
• Maleki E, Unal O, Kashyzadeh KR. Fatigue behavior prediction and analysis of shot peened mild carbon steels. Int J Fatigue. 2018;116:48–67.
   Web of Science ®Google Scholar
• Garson GD. Interpreting neural-network connection weights. AI Expert. 1991;6:47–51.
   Google Scholar
• Olden JD, Joy MK, Death RG. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol Modell. 2004;178(3–4):389–397.
   Web of Science ®Google Scholar