基于SDP信息融合的用电特征分析及负荷识别方法研究  

Research on the Electricity Consumption Characteristic Analysis and Load Identification Method Based on SDP Information Fusion

作　　者：李乐 刘智源 王学军 董云飞 张雅纯 李羽轩 朱霄珣[2] LI Le;LIU Zhiyuan;WANG Xuejun;DONG Yunfei;ZHANG Yachun;LI Yuxuan;ZHU Xiaoxun(State Grid Beijing Daxing Power Supply Company,Beijing 102600,China;North China Electric Power University,Baoding 071003,Hebei,China)

机构地区：[1]国网北京大兴供电公司,北京102600 [2]华北电力大学,河北保定071003

出　　处：《电网与清洁能源》2024年第8期56-63,73,共9页Power System and Clean Energy

基　　金：河北省自然科学基金项目(E2019502080);国网北京市电力公司科技项目(520212220007)。

摘　　要：针对多标签负荷识别信息缺失的问题,提出了基于对称点图案分解法(symmetrized dot pattern,SDP)信息融合的用电负荷特征分析及智能识别方法。针对模态混叠和残余辅助噪声问题,使用互补集合经验模态分解(complementary ensemble mode decomposition,CEEMD)分解提取电流的周期信号,提高了信号分解的鲁棒性并减小了重构误差;针对特征提取的信息缺失问题提出了基于SDP的负荷融合特性分析方法,提高了特征信息的完备性。在此基础上,提出SDP-YOLOv5的负荷识别方法,建立了SDP-YOLOv5的负荷智能识别模型。通过实验研究显示,该方法的负荷识别精度达到了98%,保证了非侵入式负荷监测水平。To tackle the missing of multi-label load identification information,a power load characteristic analysis and intelligent identification method based on SDP information fusion is proposed in this paper.For the problems of modal aliasing and residual auxiliary noise,CEEMD decomposition is used to extract the periodic signal of the current,which improves the robustness of signal decomposition and reduces the reconstruction error.A load fusion characteristic analysis method based on SDP is proposed to address the issue of missing information in feature extraction,which improves the completeness of feature information.On this basis,the load identification method of SDP-YOLOv5 is proposed,and the load intelligent identification model of SDP-YOLOv5 is established.The experimental studies show that the load recognition accuracy of the proposed method reaches 98%,which ensures the non-invasive load monitoring level.

关 键 词：负荷识别 对称点图案分解法 互补集合经验模态分解 YOLOv5 融合特征 

分 类 号：TM721[电气工程—电力系统及自动化]