电工技术学报2018，Vol.33Issue(11)：2545-2553,9.DOI:10.19595/j.cnki.1000-6753.tces.170477
基于RBF神经网络的干式空心电抗器涡流损耗计算
Eddy Current Loss Calculation of Dry-Type Air-Core Reactor Based on Radial Basis Function Neural Network
摘要
Abstract
Based on numerical simulations,the structural parameters of dry-type air-core reactor were analyzed for the effect on eddy current losses. A unified model in engineering practice was then proposed to consider the fit scheme of winding cross section, the shape of conductor cross section, the airway width, and the number of layers per package. In order to improve the computational accuracy of reactor eddy current losses, a radial basis function (RBF) neural network model was established, in which the exponential function was determined as the activation function according to the relationship between the input and output variables. Moreover, an improved particle swarm algorithm for optimizing network parameters was presented. Numerical results indicate that the proposed model exhibits the highest precision and best computational performance. As a result, this model applies especially to the optimum design of dry-type air-core reactors.关键词
交流电阻/干式空心电抗器/径向基函数/神经网络/粒子群算法Key words
AC resistance/dry-type air-core reactor/radial basis function/neural network/particle swarm algorithm分类
信息技术与安全科学引用本文复制引用
陈锋,王嘉玮,吴梦晗,马西奎..基于RBF神经网络的干式空心电抗器涡流损耗计算[J].电工技术学报,2018,33(11):2545-2553,9.基金项目
国家自然科学基金资助项目(51407139). （51407139）
