基于RMT-CNN的电网短路故障定位研究  

Locating Short-Circuit Fault of Power Grid Based on RMT-CNN

作　　者：刘义艳[1] 郝婷楠 张伟 LIU Yiyan;HAO Tingnan;ZHANG Wei(School of Energy and Electrical Engineering,Chang’an University,Xi’an,Shaanxi 710018,China;Shenzhen Woer Heat-Shrinkable Material Co.,Ltd.,Shenzhen,Guangdong 518000,China)

机构地区：[1]长安大学能源与电气工程学院,陕西西安710018 [2]深圳市沃尔核材股份有限公司,广东深圳518000

出　　处：《北京理工大学学报》2024年第4期403-412,共10页Transactions of Beijing Institute of Technology

基　　金：陕西省重点研发计划资助项目(2021GY-098);国家重点研发计划资助项目(2021YFB2601300)。

摘　　要：随着我国智能电网的快速发展,电网监测数据呈现多元化、高速化、海量化的趋势.为了充分挖掘电力大数据的潜在价值,实现电网内异常区域的自动识别与定位,本文研究了基于随机矩阵理论(random matrix theory,RMT)和卷积神经网络(convolutional neural networks,CNN)的电网异常事件定位方法.首先根据电网内部联系将电网划分为若干子系统,分区构建监测矩阵;然后采用RMT作为数据挖掘的特征提取方法,提取分区矩阵特征向量作为输入,根据电网监测数据和异常识别需求的特点搭建CNN模型;最后基于分区矩阵特征向量构建数据集,训练获得有效的异常事件自动定位CNN模型.以IEEE39节点电网模型三相短路故障为例,分析表明通过RMT提取特征向量的预处理方法能有效降低数据维度,提高CNN模型的故障定位准确率,分区RMT-CNN模型能有效定位电网内异常事件的发生地点,定位精度可达97.96%,精确率可达98.65%.With the rapid development of smart grid in China,the monitoring data of power grid presents the trend of diversification,high speed and quantification.In order to fully exploit the potential value of power big data,and realize the automatic recognition and location of abnormal regions in power grids,a method of locating abnormal events was studied based on random matrix theory(RMT)and convolutional neural networks(CNN)in this paper.Firstly,the power grid was divided into several subsystems according to the network relationship,and a monitoring matrix was constructed for every sub-area.Then,taking RMT as the feature extraction method,the extracted feature vector of partition matrix was used as input for the monitoring system.And a CNN model was built according to the characteristics of grid monitoring data and abnormal recognition requirements.Finally,a dataset was constructed based on partition matrix feature vector to obtain an effective CNN model for automatic location of abnormal events.Taking the three-phase short-circuit fault of IEEE39-node power grid model as an example,the analysis results show that the preprocessing method of extracting feature vector by RMT can effectively reduce the data dimension and improve the fault location accuracy of CNN model.The partitioned RMTCNN model can effectively locate the location of abnormal events in the grid with a location accuracy of 97.96%and precision of 98.65%.

关 键 词：电网 随机矩阵理论 卷积神经网络 异常区域 故障定位 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]