基于聚类特征及seq2seq深度CNN的家电负荷识别方法研究  

Research on household appliance load identification method based on clustering features and seq2seq depth CNN

作　　者：汪繁荣[1,2] 向堃 吴铁洲 Wang Fanrong;Xiang Kun;Wu Tiezhou(Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System,Hubei University of Technology,Wuhan 430068,China;Wuxi Fengfan Weiye Technology Co.,Ltd.,Wuxi 214171,Jiangsu,China)

机构地区：[1]湖北工业大学太阳能高效利用及储能运行控制湖北省重点实验室,武汉430068 [2]无锡风繁伟业科技有限公司,江苏无锡214171

出　　处：《电测与仪表》2023年第10期79-86,共8页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(51677058)。

摘　　要：非侵入式负荷分解技术能够挖掘用户内部信息获取各用电设备负荷信息,使智能电网更加贴近日常生活,为泛在电力物联网感知层建立提供有效数据。为解决传统非侵入式负荷分解方法输入数据复杂,考虑因素较多,采样硬件要求高以及辨识准确率较低等问题,文章首先利用改进迭代K均值聚类提取用电设备运行状态建立负荷特征集,之后将特征集输入构造的序列到序列的一维深层卷积神经网络模型以及序列到序列的单、双向长短时记忆网络等模型中进行负荷分解挖掘各设备运行状态。最后通过REFITPowerData数据集进行验证,一维深层卷积神经网络模型虽然耗时较大但负荷识别准确率达到93%以上,表明基于特征数据集及序列到序列的一维深层卷积神经网络非侵入式负荷分解方法与其他深度学习模型方法、人工神经网络方法相比表现出更显著的信息提取能力以及辨识能力。Internal information of users can be mined by non-intrusive load decomposition(NILD)to obtain load information of various electrical equipments,which enables the smart grid to have a closer connection with daily life and provides effective data for the establishment of perception layer of ubiquitous power Internet of things(UPIoT).However,there exist some problems regarding traditional NILD,for example,the input data is complicated and a lot of factors need to be considered.Besides,sampling hardware is highly demanding and identification accuracy is relatively low.In order to solve these problems,the operating state of electrical equipment is extracted by using improved iterative K-means clustering to establish characteristic data set firstly.And then,constructed by inputting data set,sequence-to-sequence one-dimensional deep convolutional neural network(1-D-DCNN)and sequence-to-sequence LSTM and Bi-LSTM network model are decomposed to mine user information.Finally,verified by the REFITPowerData,the identification accuracy of 1-D-DCNN is over 93% though it is quite time-consuming.Compared with other deep learning model and artificial neural network methods,NILD based on characteristic data set and sequence-to-sequence 1-D-DCNN show more significant information extraction and identification capabilities.

关 键 词：非侵入式负荷分解 深度学习 卷积神经网络 序列到序列 特征提取 数据挖掘 

分 类 号：TM714[电气工程—电力系统及自动化]