Abstract
With the sustained and rapid growth of the national economy, the demand for urban rail transit is increasing, and the requirements for the operation reliability of the traction power supply system are becoming more and more. Therefore, the importance of intelligent operation and maintenance is increasingly prominent. This paper proposes an intelligent cloud computing system for the energy-fed traction power supply system of Hohhot metro line 1. On the basis of the original system, according to the requirements of cloud computing, the network structure scheme of the system is presented, and through the analysis of the types and transmission mode of monitoring data needed in system, to determine the system data acquisition scheme.
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References
Mao y (2015) Design and implementation of massive power data analysis system based on cloud computing. Beijing Jiaotong University (in Chinese)
Chen j (2018) Key technologies for traction power supply of new generation intelligent urban rail transit. Electr Age 7:72 (in Chinese)
Zhang Y (2014) Design and application of rail transit power monitoring system. East China University of Science and Technology (in Chinese)
Luo m (2015) Fault prediction and health management platform design and implementation of high-speed railway traction power supply equipment. Southwest Jiaotong University (in Chinese)
Liu k (2017) Design and research of 110Â kV smart substation for JIMO power grid. Qingdao University (in Chinese)
Wang x (2016) Research and implementation of Ethernet transmission system based on FPGA. Beijing Institute of Technology (in Chinese)
Peng g, Du y (2008) Application discussion of Ethernet technology in train communication network. Railw Veh 46(12):25–28 (in Chinese)
Compare M, Bellani L, Zio E (2017) Availability model of a PHM-equipped component. IEEE Trans Reliab PP(99):1–15
Liu D, Zhou J, Liao H et al (2015) A health indicator extraction and optimization framework for lithium-ion battery degradation modeling and prognostics. IEEE Trans Syst Man Cybern Syst 45(6):915–928
Acknowledgements
This work is supported by the National Key Research and Development Program (2017YFB1200802-01).
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Liang, Y., Chen, R., Chen, J., Qiu, R., Liu, Z. (2020). Cloud Computing System of Rail Transit Traction System and Data Collection. In: Liu, B., Jia, L., Qin, Y., Liu, Z., Diao, L., An, M. (eds) Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019. EITRT 2019. Lecture Notes in Electrical Engineering, vol 640. Springer, Singapore. https://doi.org/10.1007/978-981-15-2914-6_36
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DOI: https://doi.org/10.1007/978-981-15-2914-6_36
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