Multi-objective optimization of large-load insulating pull rod end based on improved MOMVO algorithm

doi:10.3785/j.issn.1006-754X.2025.05.110

Chinese Journal of Engineering Design

2025, Vol. 32

Issue (5): 696-707 DOI: 10.3785/j.issn.1006-754X.2025.05.110

Optimization Design

Multi-objective optimization of large-load insulating pull rod end based on improved MOMVO algorithm

Tian WU^1,²(

),Binfan WU^1,²(

),Zhonghua QIU³,Yong PENG⁴,Xiang ZHU⁴

^1.College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
^2.Hubei Transmission Line Engineering Technology Research Center, Yichang 443002, China
^3.Extra High Voltage Company, State Grid Sichuan Electric Power Company, Chengdu 610041, China
^4.China Electric Power Research Institute Limited, Wuhan 430074, China

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Abstract

Aiming at the problems of excessive weight and inconvenient use of large-load insulating pull rods for ultra-high voltage, a multi-objective optimization method for the end of insulating pull rods is proposed to reduce weight and enhance insulation performance and mechanical properties. Firstly, a finite element simulation model of the insulating pull rod was established, and the electric field distribution and mechanical characteristics of its end were analyzed. Then, based on the optimal Latin hypercube sampling experimental design method and the radial basis function neural network, the surrogate models for the mass, maximum stress, maximum deformation and maximum electric field intensity of the insulating pull rod end were constructed. On this basis, the MOMVO (multi-objective multi-verse optimization) algorithm was utilized to conduct multi-objective optimization design. During the optimization process, the multi-objective optimization performance of the MOMVO algorithm was improved by combining the Sine-Tent-Cosine chaotic mapping strategy, the sine cosine algorithm, and the adaptive parameter update strategy. Finally, the feasibility of the multi-objective optimization design method was verified through simulation and tests. The results indicated that the optimization performance of the improved MOMVO algorithm was superior to that of the traditional NSGA-II (non-dominated sorting genetic algorithm-II) and MOEA/D (multi-objective evolutionary algorithm based on decomposition). Compared with before optimization, the maximum stress, maximum deformation and maximum electric field intensity of the optimized insulating pull rod end decreased by 17.03%, 6.85% and 5.58%, respectively, while the mass decreased by 10.66%. The research results provide reference for the comprehensive optimization design of insulating tools and equipment.

Key words： large-load insulating pull rod finite element simulation surrogate model multi-objective optimization multi-objective multi-verse optimization algorithm

Received: 06 March 2025 Published: 31 October 2025

CLC:

TM 84

Corresponding Authors: Binfan WU E-mail: wutian_08@163.com;1044435906@qq.com

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Cite this article:

Tian WU,Binfan WU,Zhonghua QIU,Yong PENG,Xiang ZHU. Multi-objective optimization of large-load insulating pull rod end based on improved MOMVO algorithm. Chinese Journal of Engineering Design, 2025, 32(5): 696-707.

URL:

https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2025.05.110 OR https://www.zjujournals.com/gcsjxb/Y2025/V32/I5/696

基于改进MOMVO算法的大荷载绝缘拉棒端头多目标优化

针对特高压大荷载绝缘拉棒笨重、使用不便的问题，提出了一种绝缘拉棒端头多目标优化方法，以减小质量和提升绝缘性能及机械性能。首先，建立绝缘拉棒的有限元仿真模型，分析了其端头的电场分布和机械特性。然后，基于最优拉丁超立方抽样试验设计方法和径向基神经网络，构建了绝缘拉棒端头质量、最大应力、最大形变量和最大电场强度的代理模型。在此基础上，采用MOMVO（multi-objective multi-verse optimization，多目标多元宇宙优化）算法开展了多目标优化设计。在优化过程中，结合Sine-Tent-Cosine混沌映射策略、正余弦算法以及自适应参数更新策略，以提高MOMVO算法的多目标优化性能。最后，通过仿真和试验来检验多目标优化方法的可行性。结果表明：改进MOMVO算法的优化性能优于传统的NSGA-II（non-dominated sorting genetic algorithm-Ⅱ，二代非支配排序遗传算法）和MOEA/D（multi-objective evolutionary algorithm based on decomposition，基于分解的多目标进化算法）等。相较于优化前，优化后绝缘拉棒端头的最大应力、最大形变量和最大电场强度分别下降了17.03%、6.85%和5.58%，质量减小了10.66%。研究结果为绝缘工器具的综合优化设计提供了参考。

关键词： 大荷载绝缘拉棒, 有限元仿真, 代理模型, 多目标优化, 多目标多元宇宙优化算法

Fig.1 Three-dimensional model of insulating pull rod (the first section)

Table 1 Relevant parameters of materials for each component of insulating pull rod

Fig.2 Cloud map of stress distribution of key components in insulating pull rod

Fig.3 Cloud map of deformation distribution of key components in insulating pull rod

Fig.4 Cloud map of electric field distribution on the surface of insulating pull rod end

Fig.5 Axial electric field distribution of insulating pull rod

Fig.6 Distribution diagram of Sine-Tent-Cosine chaotic mapping sequence values

Fig.7 Flow of improved MOMVO algorithm

Fig.8 Test results under the ZDT1 benchmark function

Fig.9 Test results under the DTLZ2 benchmark function

Fig.10 Key optimization position of insulating pull rod end

Table 2 Value range of optimization variables for insulating pull rod end

Table 3 Experimental design schemes and results for optimization of insulating pull rod end

Fig.11 Fitting curves of each optimization target of insulating pull rod end

Fig.12 Pareto frontier of multi-objective optimization for insulating pull rod end

Fig.13 Weighted sum value of feasible solutions of multi-objective optimization for insulating pull rod end

Table 4 Dimensional parameters of optimized insulating pull rod end

Table 5 Performance parameters and quality of optimized insulating pull rod end

Fig.14 Simulation results of optimized insulating pull rod end

Fig.15 Performance test layout for insulating pull rod


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