| 基于改进人工搜索群算法的欠压脱扣器优化 |
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| 引用本文： | 陈堂功,王梦莹,黄涛,刘超,周小婷. 基于改进人工搜索群算法的欠压脱扣器优化[J]. 电测与仪表, 2020, 57(4): 147-152 |
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| 作者姓名： | 陈堂功 王梦莹 黄涛 刘超 周小婷 |
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| 作者单位： | 省部共建电工装备可靠性与智能化国家重点实验室河北工业大学,省部共建电工装备可靠性与智能化国家重点实验室河北工业大学,省部共建电工装备可靠性与智能化国家重点实验室河北工业大学,省部共建电工装备可靠性与智能化国家重点实验室河北工业大学,省部共建电工装备可靠性与智能化国家重点实验室河北工业大学 |
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| 基金项目： | 河北省科技计划项目（16214528）；河北省自然科学基金（E2018202282） |
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| 摘 要： | 文章在对莱维飞行和人工搜索群算法(Artificial Searching Swarm Algorithm,ASSA)原理研究的基础上,为克服算法易于陷入局部解的缺陷,提出了莱维飞行人工搜索群算法(Lévy Flight Artificial Searching Swarm Algorithm,LFASSA)。为了验证改进后算法的性能,文中选取了标准测试函数进行测试,对改进前后的算法性能进行对比,证明了LFASSA算法在收敛速度和计算精度方面的优越性。文中利用有限元分析软件建立了欠压脱扣器参数化模型,利用LFASSA算法对欠压脱扣器进行性能优化。其结果表明,在满足其约束条件的前提下,降低了欠压脱扣器中Cu和Fe的使用量,且磁场分布变得更加均匀,从而满足了降低材料成本以及提高产品性能的目的。
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| 关 键 词： | 人工搜索群算法 莱维飞行 欠压脱扣器 |
| 收稿时间： | 2019-01-18 |
| 修稿时间： | 2019-01-18 |
Optimization of undervoltage release based on improved artificial searching swarm algorithm |
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| Chen Tanggong,Wang Mengying,Huang Tao,Liu Chao and Zhou Xiaoting. Optimization of undervoltage release based on improved artificial searching swarm algorithm[J]. Electrical Measurement & Instrumentation, 2020, 57(4): 147-152 |
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| Authors: | Chen Tanggong Wang Mengying Huang Tao Liu Chao Zhou Xiaoting |
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| Affiliation: | State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology |
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| Abstract: | Based on the research of the theories of the artificial searching swarm algorithm (ASSA), in order to overcome the shortcomings of the ASSA, the Lévy Flight Artificial searching swarm algorithm is proposed (LFASSA). In order to verify the performance of the improved algorithm, the standard test function is selected for testing. The performance of the algorithm before and after the improvement is compared, and the convergence speed and calculation accuracy of LFASSA are proved. In this paper, ANSYS software is used to establish the parametric model of undervoltage release. The LFASSA and ANSYS software are combined to optimize the undervoltage release. The optimization results show that under the condition of satisfying the suction requirement, the volume of copper and iron of the undervoltage release is reduced, and the magnetic field distribution is more uniform, which achieves the purpose of reducing cost and improving product performance. |
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