With the widespread applications of optical voltage transformer in power system,the fault diagnosis for opti?cal voltage transformer has become an urgent problem. In this paper,the shape,time-domain,frequency-domain,and time-frequency joint characteristics of the fault mode were extracted to form a fault feature vector at first. Then,the fault feature vector was used as input to train the back propagation(BP)neural network,thus realizing the fault diagnosis for optical voltage transformer. The reliability and accuracy of this method were verified based on sampling data generated in simulation experiments using Matlab,showing that the proposed BP neural network based fault diagnosis method was reliable and accurate,and the accuracy rate of diagnosis was above 90%.%随着光学电压互感器在电网的广泛使用,光学电压互感器的故障诊断也成为迫切需要解决的问题.本文提取光学电压互感器故障模式的形状特征、时域特征、频域特征和时频联合特征构成故障特征向量,之后,将故障模式特征向量作为输入对反向传播BP(back propagation)神经网络进行训练,从而实现对光学电压互感器的故障诊断.基于Matlab仿真实验获取数据验证了方法的可靠性和准确性.验证结果表明,本文所提出的基于BP神经网络的故障诊断方法可靠、准确,诊断正确率在90%以上.
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