Equipotential Entry Path Optimization for Ultra-High Voltage Cathead Towers Based on PSO-AFSA

WU Tian; TANG Shengyi; QIU Zhonghua; PEN Yong; ZHU Xiang; PU Ziheng; FANG Chunhua

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Equipotential Entry Path Optimization for Ultra-High Voltage Cathead Towers Based on PSO-AFSA

更新时间：2025-08-12

- Equipotential Entry Path Optimization for Ultra-High Voltage Cathead Towers Based on PSO-AFSA
- Moren Journal (2025)
- 作者机构：
  
  1.湖北省输电线路工程技术研究中心（三峡大学）,湖北省宜昌市443002
  2.三峡大学电气与新能源学院,湖北省宜昌市443002
  3.国网四川省电力公司超高压分公司,四川省成都市610041
  4.中国电力科学研究院有限公司,湖北省武汉市430074
- 作者简介：
- 基金信息：
  
  The Science and Technology Projects of State Grid Corporation of China(5500-202455164A-1-1-ZN);National Natural Science Foundation of China(51807110)
- DOI：
  CLC：
- Received：19 February 2025，
  
  Revised：23 April 2025，
  
  Accepted：25 April 2025，
- 稿件说明：
移动端阅览
WU Tian, TANG Shengyi, QIU Zhonghua, et al. Equipotential Entry Path Optimization for Ultra-High Voltage Cathead Towers Based on PSO-AFSA[J/OL]. Moren journal, 2025.
DOI：

WU Tian, TANG Shengyi, QIU Zhonghua, et al. Equipotential Entry Path Optimization for Ultra-High Voltage Cathead Towers Based on PSO-AFSA[J/OL]. Moren journal, 2025. DOI：

摘要

带电作业作为确保特高压线路运行可靠性的重要技术方法，选择安全高效的等电位带电作业进入路径至关重要。鉴于当前等电位进入路径优化评估标准单一、没有考虑电场分布和放电特性的问题，本文提出了一种基于分段评估函数的混合粒子群—人工鱼群优化算法(PSO-AFSA)路径优化模型。首先，建立了1000kV猫头塔输电线路的有限元仿真模型，并通过与现场试验数据对比验证了模型的准确性；随后依据常规路径场强分布规律并结合组合空气间隙放电规律，以E40%作为畸变电场划分点，构建了基于电场畸变、路径与场强的分段评估函数；最后，通过分析作业人员从不同相位进入等电位时，以不同姿势于地电位与等电位处体表场强的极值及其分布规律，以及边相和中相几种不同路径进入过程中，场强分布在径向距离下的分布规律，结合全局寻优能力强的粒子群优化(PSO)和逃离局部最优能力强的人工鱼群算法(AFSA)，实现了不同位置下等电位进入路径的优化。与传统路径优化方法进行了对比，结果表明，所提出的路径优化方法在相同评估模型下避免了对低场强区域的无效位移，使评估函数值平均降低了32.865%，验证了其在等电位路径优化方面的可行性。

Abstract

As a crucial technical approach for ensuring the operational reliability of ultra-high voltage (UHV) transmission lines

live working necessitates the selection of safe and efficient equipotential entry paths. Addressing the limitations of current optimization criteria for equipotential access paths—particularly their singular evaluation standard and negligence of electric field distribution and discharge characteristics—this paper proposes a hybrid Particle Swarm Optimization-Artificial Fish Swarm Algorithm (PSO-AFSA) path optimization model incorporating a segmented evaluation function. Initially

a finite element simulation model of 1000kV cat-head tower transmission lines was established

with its accuracy validated through comparative analysis with on-site experimental data. Subsequently

based on conventional path electric field distribution patterns and combined air gap discharge characteristics

a segmented evaluation function was developed using E40% as the threshold for electric field distortion demarcation

integrating field distortion severity

path parameters

and field intensity metrics. Ultimately

through systematic analysis of field intensity extremes and distribution patterns at both ground potential and equipotential states during different postural transitions of operators across various phase entries

as well as radial distance-dependent field distribution characteristics during outer-phase and middle-phase access processes

an optimized equipotential entry path solution was achieved by synergizing the global optimization capability of PSO with the local optimum-escaping strength of AFSA. Comparative studies with conventional path optimization methods demonstrate that the proposed approach reduces unnecessary displacements in low-field-intensity regions under identical evaluation models

achieving an average 32.865% reduction in evaluation function values—thereby conclusively verifying its technical feasibility for equipotential path optimization.

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references

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