电测与仪表2012，Vol.49Issue(4)：1-4,4.
基于GA-RBF神经网络的变压器温升预测
Transformer Temperature Rising Forecasting Based on GA-RBF Neural Network
黄方良 1周玲 1任新新 1陈月峰2
作者信息
- 1. 河海大学能源与电气学院,南京 211100
- 2. 河南舞阳县供电公司,河南 漯河 462400
- 折叠
摘要
Abstract
A genetic algorithm optimization of radial basis function neural network prediction model for the prediction of transformer temperature rising is presented. Firstly, it uses the GA algorithm to optimize the RBF neural network initial value of four parameters including the number of hidden layer nodes, the output weights, the hidden layer basis function centers and width , then it uses the optimized RBF neural network to train samples, which overcomes the random parameters of the traditional neural network. Taking an S9-1000/10 low-loss power transformer as an example for the temperature rising test, the predicted values are compared with measured values and the values based on traditional BP neural network prediction. The results show that transformer temperature rise predicted values using this method is closer to measured values, and this prediction model has better accuracy and adaptability.关键词
变压器/遗传算法/RBF神经网络/温升/预测Key words
transformer, genetic algorithm, RBF neural network, temperature rise, prediction分类
信息技术与安全科学引用本文复制引用
黄方良,周玲,任新新,陈月峰..基于GA-RBF神经网络的变压器温升预测[J].电测与仪表,2012,49(4):1-4,4.
