高电压技术2012，Vol.38Issue(1)：16-21,6.
广义回归神经网络在变压器绕组热点温度预测中的应用
Application of Generalized Regression Neural Network to Transformer Winding Hot Spot Temperature Forecasting
摘要
Abstract
The winding hot-spot temperature of transformer is the key factor which affects the insulating performance,so it is necessary to forecast the transformer winding hot spot temperature so as to improve the operational reliability of transformer.However,the inner temperature of transformer is affected by many factors,and the heat transfer,fluid mechanics,electromagnetism and other interdisciplinary increase the complexity of calculation.Generalized regression neural network（GRNN） has strong nonlinear mapping capability,flexible network structure,high fault tolerance and robustness,etc,and adopting GRNN to predict the hot spot of transformer winding will overcome the shortcomings of back propagation neural network（BPNN）,such as the local minimum and low convergence speed and so on.Comparison of the results of GRNN and the measured data show that the results of GRNN are in accordance with the measured values.关键词
变压器/热点温度/BP神经网络/绕组/GRNN神经网络/预测Key words
transformer/hot spot temperature/back propagation neural network（BPNN）/winding/generalized regression neural network（GRNN）/forecast分类
信息技术与安全科学引用本文复制引用
陈伟根,奚红娟,苏小平,刘文..广义回归神经网络在变压器绕组热点温度预测中的应用[J].高电压技术,2012,38(1):16-21,6.基金项目
国家重点基础研究发展计划（973计划） （）
国家创新研究群体基金 （）
