高压电器Issue(12)：19-24,6.DOI:10.13296/j.1001-1609.hva.2015.12.004
基于RBF-GA算法的特高压线路复合绝缘子均压环优化
Optimization Design for Grading Ring of Composite Insulators in UHV Transmission Lines Based on RBF-GA
摘要
Abstract
UHV composite insulator surface potential is very unevenly distributed, and there is an intense local electric field strength on the surface of the insulator sheath near high voltage insulator end fitting , accelerating the aging of insulators. The method of installing the grading ring to resolve the issue is commonly adopted in engineering. The effect of electric field distributing depends on the structural parameters of grading ring. In order to conclude optimized structural parameters, it is of great necessity to study the optimization of the tube radius, the ring radius and the projection from high voltage end fitting. In this paper, an improved genetic algorithm (GA) has been adopted because the traditional genetic algorithm is time consuming in grading ring optimization. Radial base function-GA(RBF-GA) is an effective method which combines the approximate ability of any continuous function with arbitrary precision of RBF network and the ability of global optimizing of GA. This paper calculates the distribution of electric field by using finite element method and uses RBF network to approximate the maximum electric field strength on the surface of the insulator sheath near high voltage insulator end fitting. Finally , this paper optimizes the structural parameters by using GA based on the fitting value. The optimization speed of grading ring structural parameters based on RBF-GA method significantly is accelerated with good optimization results , which proves the effectiveness of the method.关键词
均压环/复合绝缘子/遗传算法/RBF神经网络/优化算法/有限元法Key words
grading ring/composite insulator/genetic algorithm(GA)/RBF network/optimization algorithm/finite element method引用本文复制引用
张宇娇,徐彬昭,徐天勇..基于RBF-GA算法的特高压线路复合绝缘子均压环优化[J].高压电器,2015,(12):19-24,6.基金项目
国家自然科学基金项目(51207081)；湖北省自然科学基金项目(2014CFB668)。Project Supported by National Natural Science Foundation of China (51207081), Natural Science Foundation of Hubei Province(2014CFB668) （51207081）
