摘要
针对BP神经网络在识别变压器故障时容易陷入局部最优、诊断精度低、收敛速度慢等缺点,提出一种自适应差分进化算法与BP神经网络相结合的变压器故障诊断方法。该方法采用差分进化算法优化BP神经网络初始权值和阈值,将优化结果赋值BP神经网络进行网络训练,最终得到用于变压器故障诊断的最佳网络模型。实验结果表明,该组合算法比传统BP神经网络具有更高的诊断精度和更快的收敛速度,是一种更适合变压器故障诊断的高效方法。
Aiming at the shortcomings of BP neural network,such as easily falling into local optimum,low diagnostic accuracy and slow convergence speed,a transformer fault diagnosis method based on adaptive differential evolution algorithm and BP neural network is proposed in this paper.This method uses differential evolution algorithm to optimize the initial weights and thresholds of BP neural network,and assigns the optimized results to BP neural network for network training.Finally,the optimal network model for transformer fault diagnosis is obtained.The experimental results show that the combined algorithm has higher diagnostic accuracy and faster convergence speed than the traditional BP neural network,which is a more efficient method for transformer fault diagnosis.
作者
孔德钱
张新燕
童涛
高亮
张家军
古超帆
Kong Deqian;Zhang Xinyan;Tong Tao;Gao Liang;Zhang Jiajun;Gu Chaofian(School of Electrical Engineering,Xinjiang University,Urumqi 830047,China)
出处
《电测与仪表》
北大核心
2020年第5期57-61,共5页
Electrical Measurement & Instrumentation
基金
国家自然科学基金资助项目(51667018)。
关键词
BP神经网络
变压器故障
差分进化算法
网络模型
BP neural network
transformer fault
differential evolution algorithm
network model
