摘要
电力变压器油中溶解气体的色谱分析是变压器故障诊断的重要方法,通过该方法可以间接了解变压器的运行状态和内部潜在故障.人工神经网络已经成功地应用于电力变压器故障诊断,但学习样本数多和输入输出关系复杂性减慢了网络的收敛速度.为解决此问题,将用遗传算法改进的小波神经网络应用于电力变压器故障诊断,克服小波算法易于陷入局部极小、收敛速度慢等缺点.
The chromatographic analysis of the power transformer oil dissolved gas is an important method for transformer fault diagnosis by which the operating state of the transformer and the potential transformer internal fault can be grasped indirectly.Artificial neural network has been applied in the power transformer fault diagnosis successfully,but the large number of learning samples and the complicated input-output relationship will lead to a slow network convergence.To resolve the problem,this paper employ the wavelet neural network improved by using genetic algorithms in power transformer fault diagnosis,thus overcoming the shortcomings of local minima and slow convergence speed.
出处
《吉首大学学报（自然科学版）》
CAS
2013年第1期51-55,76,共6页
Journal of Jishou University(Natural Sciences Edition)
关键词
小波神经网络
遗传算法
变压器故障诊断
wavelet neural network
genetic algorithms
power transformer fault diagnosis
