基于传递熵和小波神经网络的电子式电压互感器误差预测

Error prediction of electronic voltage transformer based on transferentropy and wavelet neural network

电子式电压互感器目前的主要问题是长期运行后的准确度退化问题。现行的方法有定期离线校验和在线校验,前者不利于及时发现互感器的误差变化,后者需要标准器并网运行,无法大规模应用。基于这一现象,文中提出了基于传递熵和小波神经网络的电子式电压互感器误差预测方法。先根据传递熵分别选取比差和角差的主要影响因素,然后将筛选所得的因素作为输入量,建立小波神经网络的误差预测模型,并对其仿真测试。实验表明,对比差的预测误差低于5%,对角差的预测误差低于10%,文章方法能够实现较长时间的互感器状态监测。

At present,the main problem of electronic voltage transformer is the degradation of accuracy after long-term operation.The current methods include regular off-line calibration and online calibration.The former finds the change of the transformer error in time,while the latter needs the standard to be grid-connected for a long time,which cannot be applied on a large scale.Thus,an error prediction method of electronic voltage transformer based on transfer entropy and wavelet neural network is proposed in this paper.Firstly,the main influencing factors of the ratio difference and the angle difference are selected by transfer entropy.Then,the factors are used as the input,and the error prediction model of the wavelet neural network is established and test.The simulation shows that the prediction error of the ratio difference is less than 5%,and the prediction error of the angle difference is less than 10%.The proposed method can realize the condition monitoring of the transformer for a long time.