基于自适应神经网络的变压器健康诊断模型设计

Design of transformer health diagnosis model based on adaptive neural network

为解决传统变压器故障诊断方法存在的诊断精度低、实时监测效果差的问题,提出构建基于自适应神经网络的变压器健康诊断模型。通过构建变压器状态量数据的时间序列自回归模型,对变压器状态量数据进行拟合;将变压器状态量数据的时间序列进行自适应神经网络训练,获取量化结果;在此基础上,获取变压器状态量数据跟随时间变动状态特征;基于获取的状态特征量,构建变压器状态特征健康诊断模型,实现变压器状态诊断。实验结果表明:采用所设计的模型进行变压器状态诊断的精度较高,诊断故障的漏识率下降,对变压器故障能够进行及时监测。

In order to solve the problems of low diagnosis accuracy and poor real-time monitoring effect of traditional transformer fault diagnosis methods,a transformer health diagnosis model based on adaptive neural network is proposed.By constructing the time series autoregressive model of the transformer state data,the transformer state data is fitted;the time series of the transformer state data is trained by the adaptive neural network to obtain the quantitative results;on this basis,the state characteristics of the transformer state data following the time variation are obtained;based on the obtained state characteristics,the health diagnosis model of transformer state feature is constructed to realize transformer state diagnosis.The experimental results show that the accuracy of the model is high,the leakage rate of fault diagnosis is low,and the transformer fault can be monitored in time.