基于小波AlexNet网络的配电网故障区段定位方法

Fault segment location method for distribution network based on wavelet AlexNet network

文中提出一种基于深度网络迁移学习的配电网故障区段定位方法。利用小波包变换(WPT)分解配电网各区段的电量信号,将各节点小波包系数按照低频到高频的顺序重新排列获得时频矩阵,通过颜色编码将时频矩阵转成具有图像性质的像素矩阵,像素矩阵囊括了当前系统的工作状况信息,利用迁移学习AlexNet网络,调整网络结构使其适应于配电网故障区段辨识,通过微调的AlexNet网络自主挖掘像素矩阵的故障特征作为预测变量,利用门控循环单元(GRU)、学习向量量化(LVQ)、朴素贝叶斯分类器(NBC)、极限学习机(ELM)、支持向量机(SVM)等模式识别算法进行故障特征分类,从而实现配电网故障区段定位。针对多分支的线缆混合线路进行实验分析,比较5种模式识别算法的分类效果,得到GRU算法准确率可以达到99.92%,证明了该方法不受故障时刻、故障类型和过渡电阻等因素的影响,可满足配电网对故障区段定位准确度和可靠性的需求。

A novel fault segment location method based on deep network with transfer learning for the distribution network is proposed.Firstly,the wavelet packet transform(WPT)is adopted to decompose electric signals in each section.The wavelet packet coefficients of each node are rearranged from low frequency to high frequency to obtain the time-frequency matrix.The time-frequency matrix can be converted into the pixel matrix with the property of the image by the color-coding.The pixel matrix can contain the working conditions of the current system.Then,transfer learning is performed on the AlexNet model,and the network structure is adjusted to adapt to fault segment identification of distribution network.The fine-tune AlexNet network can autonomously extract the pixel matrix features as predictive variables.Finally,the pattern recognition algorithms of gated recurrent unit(GRU),LVQ,NBC,ELM,and SVM are used to classify the fault features,and the fault area location for the distribution network is completed.Experimental analysis is carried out for the overhead/cable hybrid line with multi-branches.The classifying effects of the five pattern recognition algorithms are compared.The accuracy rate of the GRU algorithm is 99.92%.The testing results show that the proposed method is not affected by fault time,fault type,grounding resistance,and other factors,which can meet the fault location accuracy and reliability requirement of the distribution network.