摘要
随着电力物联网的迅速发展,为进一步提高配电网故障定位的精准度,提出了一种基于边缘计算和小波神经网络的配电网故障定位方法。首先对终端进行重新划分,将其分为具有计算功能的汇集终端和故障信息收集的采集终端,然后对故障电流信号进行小波变换和小波包频带分解得到故障特征向量,最后通过神经网络训练,输出诊断结果。仿真算例验证了所提方法能够减少故障诊断时间,提高故障定位精准度。
With the rapid development of the power internet of things, in order to further improve the accuracy of fault location in the distribution network, a method of distribution network fault location based on the edge computing and wavelet neural network is proposed.Firstly, the terminal is redivided into a collection terminal with calculation function and a collection terminal for fault information collection.Then, the fault current signal is subjected to wavelet transform and wavelet packet frequency band decomposition to obtain the fault feature vector.Finally, through the neural network training, the output diagnostic result the is obtained.The simulation results show that this method can reduce the time of fault diagnosis and improve the accuracy of fault location.
作者
许欣
张颖
XU Xin;ZHANG Ying(College of Electrical&Information Engineering,Changsha University of Science&Technology,Changsha 410114,China)
出处
《电器与能效管理技术》
2022年第8期33-38,共6页
Electrical & Energy Management Technology
关键词
配电网
故障定位
边缘计算
小波神经网络
distribution network
fault location
edge computing
wavelet neural network
