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Donghui Li,Yuguang Zhong,Xue Zhou,Guofu Zhai 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.12
In windy weather, overhead lines swing and collide with each other, often resulting in short-circuits and arc faults. The line is partially molten and produces numerous hot metal particles, which fall and may ignite the fuels on the ground. This paper developed an experimental system to obtain hot particle dimensions at different short-circuit current levels. Based on the results of this experiment, an ignition prediction method was developed. The particle parameters from the experiments were used as a bridge that to complete the prediction from short-circuit current to ignition risk. A neural network was trained according to the experimental and calculated results, then used to predict the risk of ignition. The overhead lines in a forest were used as an example application to perform thte investigation. The results show that copper lines in a forest with needles on the ground create a high risk of ignition hazard.
Ye Xuerong,Deng Jie,Wang Yingqi,Zhai Guofu 한국자기학회 2014 Journal of Magnetics Vol.19 No.4
Optimization design and robust design are significant measures for improving the performance and reliability of electromagnetic devices (EMDs, specifically refer to relays, contactors in this paper). However, the implementation of the above-mentioned design requires substantial calculation; consequently, on the premise of guaranteeing precision, how to improve the calculation speed is a problem that needs to be solved. This paper proposes a new method for establishing an approximate model for the EMD. It builds a relationship between the input and output of the EMD with different coil voltages and air gaps, by using a user-defined interpolating function. The coefficient of the fitting function is determined based on a quantum particle swarm optimization (QPSO) method. The effectiveness of the method proposed in this paper is verified by the electromagnetic force calculation results of an electromagnetic relay with permanent magnet.
Huimin Liang,Jiaxin You,Wenying Yang,Guofu Zhai 한국자기학회 2014 Journal of Magnetics Vol.19 No.1
The equivalent model of a permanent magnet (PM) plays an important role in electromagnetic system calculation. A type of subsection model for a PM bar is established, to improve the accuracy of the traditional equivalent circuit method. The mathematical expression, and its end verification condition, are presented. Based on the analytical method and finite element method, the leakage permeance calculation of a PM bar in an open magnetic circuit is investigated. As an example, for a given certain type of PM bar, the magnetic flux of each section is validated by experiment, and by simulation. This model offers a foundation for building a high accuracy equivalent magnetic PM model in an electromagnetic system.
Donghui Li,Xue Zhou,Sanqiang Ling,Yue Jin,Guofu Zhai 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.7
Traditional method evaluates operating reliability by counting life-test results of a small number of samples, which ignores variability between individuals and cannot accurately reflect the true state. This paper proposes a model for predicting operational reliability of mechanism in a batch of micro-switches based on manufacturing parameters, in view of complex structure and diverse manufacturing parameters. A multi-body kinetic model based on manufacturing parameters and principle of action considering contact fatigue and wear was established, and the operating characteristics in the degradation process with sliding wear of joints were obtained. Finally, the linkage with revolute clearance in miniature circuit breaker (MCB) was used as numerical example application to perform investigation. Prediction results are consistent with batch experiments, which verifies accuracy of model. In addition, the optimal combination of clearances was obtained by using genetic algorithm based on predicted model, which increases locking reliability to 99.3 % after 10000 operation cycles.