基于NSGA-II和BP神经网络的杯状纵磁触头结构优化设计

doi:10.19595/j.cnki.1000-6753.tces.220369

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Abstract Under the DC breaking condition, the existing common power frequency contacts need to break the high-frequency reverse current, but their arc regulation performance under high-frequency current needs further study. In this paper, the cup-shaped longitudinal magnetic contact commonly used in power frequency is taken as an example. Firstly, the magnetic field simulation of the cup-shaped longitudinal magnetic contact is carried out under the current of 1 000Hz. It is found that the center of the contact gap with the peak current can generate a longitudinal magnetic field of 3.17mT/kA, and the magnetic field distribution is extremely uneven. Then, in order to improve the maximum magnetic field strength and distribution uniformity, a method based on the combination of BP neural network and genetic algorithm is proposed to optimize the contact structure, and a BP neural network model is established, which takes the contact slot width, slot length, radial slot rotation angle, contact cup chute height and chute inclination as inputs, and takes the maximum magnetic field strength and distribution unevenness of the peak current in the center of the contact gap as outputs. Finally, the structural parameters of the cup-shaped longitudinal magnetic contact are optimized by NSGA-II algorithm. The optimization results show that when the parameters L₁ is 2.9mm, L₂ is 18.0 mm, θ₁is 20.0°, H is 17.5mm and θ₂ is 26.0°, the center of the contact gap with peak current can generate a longitudinal magnetic field of 4.34mT/kA, and the unevenness is reduced from 6.89 to 3.39, which greatly improves the uniformity, thus improving the ability of the longitudinal magnetic field to regulate the vacuum arc.

Key words： Vacuum interrupter contact longitudinal magnetic field NSGA-II BP neural network

Received: 12 March 2022

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TM561

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	Ding Can
	Li Jiang
	Yuan Zhao
	Chen Lixue
	Liu Liming

Cite this article:

Ding Can,Li Jiang,Yuan Zhao等. Structural Optimization Design of Cup-Shaped Longitudinal Magnetic Contact Based on NSGA-II and BP Neural Network[J]. Transactions of China Electrotechnical Society, 2022, 37(23): 6074-6082.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.220369 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I23/6074

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