磁链闭环控制下接触器的优化设计方法

doi:10.19595/j.cnki.1000-6753.tces.230377

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Abstract

The traditional contactor’s optimization design process requires static calculation, prototype production, and repeated testing, which consumes much time and is high cost, demanding substantial human and material resources. The finite element dynamic simulation method has become the main way to optimize the contactor design but it is hindered by prolonged calculation time. The optimization design method is mainly based on the orthogonal experimental method, which can only generate the local optimal solutions at the experimental level. Intelligent optimization design algorithms can achieve global optimal solutions but require a lot of iterative calculations. Therefore, this paper proposes an improved optimization algorithm for the dynamic process of contactors to reduce the electromagnetic and mechanical inertia of contactors and increase the magnetic density. The response speed of the mechanism and the utilization rate of magnetic materials are improved, reducing contact bounces.
Firstly, considering the external leakage flux and magnetic field’s distribution characteristics in contactors, an improved magnetic circuit model is constructed based on the three-dimensional finite element dynamic simulation to calculate parameters. Then, an improved NSGA-Ⅱ algorithm is introduced for optimization design. The number of Pareto fronts is used to judge the evolution process of the population, and the adaptive genetic differential hybrid evolution strategy is realized, improving global convergence and convergence speed. Finally, combined with a constant flux closed-loop control strategy and multi-objective optimization design, the speed response and magnetic material utilization are improved by reducing electromagnetic and mechanical inertia while increasing magnetic density to mitigate contact bounces.
Simulations and experiments are conducted on the improved magnetic circuit model. The results show that the simulation and experimental waveforms are almost consistent, with errors of less than 3% within the allowable range. Combined with the improved magnetic circuit model and the improved NSGA-Ⅱ algorithm, the contactor design is optimized by the flux linkage closed-loop. The optimization results show that the prototype's holding power is reduced to 0.72 W, the average bounce time is reduced to 0.7 ms, the average number of bounce times is reduced to 2, the number of coil turns is reduced to 1 304, and the magnetic density is increased from 0.54 T to 0.73 T, improving the utilization rate of magnetic materials and reducing the power consumption.
The following conclusions can be obtained: (1) An improved magnetic circuit model, considering the external magnetic leakage and magnetic field distributions in the dynamic process of the contactors, maintains computational efficiency and accuracy across structural parameters and control strategy variations, suitable for optimizing electromagnetic mechanisms with diverse parametric forms. (2) The improved NSGA-Ⅱ algorithm judges the population evolution using the number of individuals in the Pareto frontier and selects evolution strategies and probability settings according to different stages of population evolution, thereby effectively reducing the calculation number of fitness functions and enhancing the global convergence. (3) Under constant flux closed-loop control, the electromagnetic and mechanical inertia, contact bounces, and material utilization rate are taken as the main optimization indicators. The designed prototype has advantages such as fast response, short contact bounce time, high material utilization rate, and low holding power.

Key words： Contactor optimized design magnetic circuit method non dominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ)

Received: 31 March 2023 Published: 07 June 2024

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TM572

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	Tang Longfei
	Yao Linrui
	Yang Wenwei

Cite this article:

Tang Longfei,Yao Linrui,Yang Wenwei. Optimal Design Method of Contactor Under Closed-Loop Control of Flux Linkage[J]. Transactions of China Electrotechnical Society, 2024, 39(10): 3206-3217.

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