基于遗传算法进化小波神经网络的电力变压器故障诊断

Fault Diagnosis of Power Transformers Based on Genetic Algorithm Evolving Wavelet Neural Network

在电力变压器故障诊断方法中,小波神经网络常用的反向传播算法存在着易陷入局部极小点和对初值要求较高的缺点,往往给故障诊断带来困难。文中提出了一种基于遗传算法进化小波神经网络的变压器故障诊断方法,用实数编码的遗传算法来代替人解决小波神经网络结构的选择和参数的设定。在整个学习过程中,网络的复杂度、收敛性和泛化能力得到了较好的综合。大量实例表明,该方法能有效地对电力变压器单故障和多故障样本进行分类,提高了诊断准确率。

In the field of fault diagnosis of power transformers, the main disadvantage of the back propagation algorithm of wavelet neural network commonly used lies in the optimization procedure getting easily stacked into the minimal value locally and strict requirement on the initial value, which would make fault diagnosis difficult to some extent. This paper presents a fault diagnostic method based on the genetic algorithm evolving wavelet neural network. The selection of network structure and parameters is carried out by use of the real value encoding genetic algorithm instead of artificial setting. Throughout the process, compromise is satisfactorily reached among the network complexity, the convergence and the generalization ability. The results of numerical examples show that the algorithm proposed has good classifying capability of both single-fault and multiple-fault samples as well as high accuracy of fault diagnosis.