一种电力变压器神经网络故障诊断方法

电力变压器是电力系统的重要组成部分之一，它的安全运行对电力系统的可靠供电有很大的影响，本文对电力变压器故障诊断方法进行了研究.油中溶解气体分析法是诊断电力变压器内部故障的有效手段；最速下降算法是一种常用的神经网络局部寻优算法，在电力变压器故障诊断中收敛速度较慢，易陷入局部极小值；遗传膜算法具有并行计算的特点，用于电力变压器的故障诊断，很好地解决了神经网络在搜索过程中容易陷入局部最优的问题，具有更好的故障诊断速度.数值仿真结果验证了所给方法的有效性.

A neural network-based method for fault diagnosis of power transformer

The power transformer is an important component of the power system, its safe operation has great influence on the reliable power supply of power system.Therefore, the paper focuses on the method for fault diagnosis of power transformer.The dissolved gas analysis of power transformer is an effective means of diagnosing internal faults of power transformer.The gradient descent principle is a general local optimization algorithm of neural network, which has slow convergence speed in fault diagnosis of power transformer, and is easy to fall into local minimum.While the genetic membrane algorithm has the characteristics of parallel computing, and can be used to effectively prevent neural network from converging to the local optimal solution, and has better fault diagnosis speed.Finally numerical simulations show the effectiveness of the given method.