Abstract
As a major consideration during the traction system design procedure, the traction motor temperature rise is also deemed as an important physical parameter for evaluating performance of the traction motor over the course of long-term service. Due to the operating conditions, ambient temperature and other factors, it is difficult to accurately assess the traction temperature rise during the designing process of traction system. On a basis of extensive data analysis on traction motor temperature rise tests, this paper first adopts the Heuristic method of Sequential Forward Selection to determine main factors that cause the traction motor’s temperature rises in various operating conditions. Then the fuzzy neural network calculation models of traction motor temperature rise is established under different working conditions. Training these fuzzy neural networks with sample data from route test to obtain the traction motor temperature rise calculation model under full operating conditions and the whole climate environment. Taking actual parameters of a certain type EMU (Electric Multiple Units) of the Beijing—Shanghai line as the object, this paper compares the temperature variation of the traction motor obtained from the simulation calculation with the experimental data in a way to justify the correctness and validity of the selected method.
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References
Maximini M, Koglin HJ (2004) Determination of the absolute rotor temperature of squirrel cage induction machines using measurable variables. IEEE Trans Energy Convers 19(1):34–39
Kral C, Habetler TG, Harley RG (2004) Rotor temperature estimation of squirrel-cage induction motors by means of a combined scheme of parameter estimation and a thermal equivalent mode. IEEE Trans Ind Appl 40(4):1049–1056
Calculation and analysis of 3D temperature fields of medium size high voltage asynchronous motor based on coupled field. Electric Machines Control 15(1):73–78. (Ch)
Xie Y, Liweili L (2008) Calculation and analysis of temperature field for induction motors with broken bars fault. Trans China Electrotechnical Soc 23(10):33–399. (Ch)
Yang M, Zhang P (2013) Dynamic thermal characteristic and its discrete algorithm of stator windings of the asynchronous motor. Proc CSEE 33(24):121–126. (Ch)
Huang Z, Wei X, Liu Z (2012) Fault diagnosis of railway track circuits using fuzzy neural network. J China Railway Soc 34(11):54–59. (Ch)
Dong H, Liu Y, Li X, Yan J (2013) Study on high-speed train atp based on fuzzy neural network predictive control. J China Railway Soc 35(8):58–62. (Ch)
Dai W, Lou H, Yang A (2009) An overview of neural network predictive control for nonlinear systems. Control Theory Appl 26(5):521–530. (Ch)
Yunqiu T (2016) Electromechanics, 5th edn. China Machine Press, Beijing
Cai Z, Xu G (2004) Artificial intelligence and its application, 3rd edn. Tsinghua University Press, Beijing
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Liang, J., Liu, S., Zhong, C., Yu, J. (2018). Calculation Analysis on Traction Motor Temperature Rise of EMU Vehicles Based on Fuzzy Neural Network. In: Jia, L., Qin, Y., Suo, J., Feng, J., Diao, L., An, M. (eds) Proceedings of the 3rd International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2017. EITRT 2017. Lecture Notes in Electrical Engineering, vol 482. Springer, Singapore. https://doi.org/10.1007/978-981-10-7986-3_29
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DOI: https://doi.org/10.1007/978-981-10-7986-3_29
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