高温超导磁浮多参量监测方法及智能状态识别

doi:10.19595/j.cnki.1000-6753.tces.L11047

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Abstract On January 13, 2021, the first high-temperature superconducting (HTS) pinning high-speed maglev engineering vehicle was launched in Chengdu, China, marking that HTS maglev technology has entered the stage of engineering research. The temperature rise of the HTS bulk in the superconducting levitator and the relevant operation parameters directly determine the safe and stable operation of maglev train. It is urgent to carry out the research of intelligent state recognition theory and test platform to promote the technology development for safe operation of HTS maglev. The temperature rise of the HTS bulk in the superconducting levitator directly determines the levitation performance of the HTS maglev system. However, due to the limitation of the internal structure space inside the superconducting levitator, it is difficult to put in many temperature sensors. On the other hand, although the temperature sensor can directly measure the real-time temperature inside the HTS bulk, it may damage the structure of the HTS bulk. Therefore, a new non-contact temperature rise measurement method combining the thermal-vibration characteristics of the HTS bulk and BP neural network (BPNN) is proposed in this paper, which can achieve a high temperature rise recognition rate.
Firstly, a test device for thermal-vibration characteristics of the HTS bulk was built in this paper and an alternating magnetic field (MF) was generated by Halbach permanent magnet (PM) wheel. The temperature rise and dynamic force change of the HTS bulk at 0~4000 r/min were tested experimentally. It was found that the internal temperature rise of the HTS bulk increases progressively with the frequency of the alternating MF generated by the PM wheel. At the same time, based on the temperature rise and vibration data, the vibration acceleration features are extracted by wavelet decomposition, and the temperature rise is identified by BPNN with an accuracy of over 99.9%.
Then, to further verify the effectiveness of wavelet decomposition combined with BPNN in temperature identification of the HTS bulk under the excitation of a real permanent magnet guideway (PMG), the thermal-vibration characteristics of the HTS bulk were studied in combination with the real MF provided by opposite-pole PMG used in the testing equipment of SCML-03. The thermal-vibration characteristics of the HTS bulk under the abnormal excitation of the PMG were studied by finite element simulation. The intrinsic relationship between the dynamic characteristics of the HTS bulk and the internal temperature rise under the simulated excitation of the actual MF irregularity was studied, and the recognition accuracy was also over 99.5%.
The following conclusions can be drawn from the analysis: under the condition of alternating MF provided by PM wheel, the higher the MF change frequency, the higher the internal temperature rise of the HTS bulk, leading to a greater levitation force attenuation. The wavelet energy values of the HTS bulk are different under different fluctuating frequencies of alternating MF, and are different from traditional characteristics such as levitation height. These features can also be used as the main parameters for monitoring the HTS bulk. The non-contact intelligent temperature rise detection method in this paper does not occupy the space inside the superconducting levitator, and is suitable for the dynamic operating conditions of the HTS maglev vehicle, weakening the technical difficulty of the inside of the onboard levitators.

Key words： Superconducting levitator maglev train temperature rise vibration BP neural network (BPNN) intelligent monitoring

Received: 14 July 2024

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U237

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	Zheng Jun
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Zheng Jun,Pang Peng,Yang Hao等. Multi Parameter Monitoring Method and Intelligent State Recognition for High-Temperature Superconducting Maglev[J]. Transactions of China Electrotechnical Society, 2024, 39(zk1): 1-13.

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