基于先验统计模型的非侵入负荷辨识算法  

Resident non-invasive load identification algorithm based on prior statistical model

作　　者：赵成 宋彦辛 周赣[2] 冯燕钧 郭帅 李季巍 ZHAO Cheng;SONG Yanxin;ZHOU Gan;FENG Yanjun;GUO Shuai;LI Jiwei(State Grid Beijing Electric Power Company Electric Power Research Institute,Beijing 100080,China;School of Electrical Engineering,Southeast University,Nanjing 211189,China)

机构地区：[1]国网北京市电力公司电力科学研究院,北京100080 [2]东南大学电气工程学院,江苏南京211189

出　　处：《电力工程技术》2024年第1期165-173,211,共10页Electric Power Engineering Technology

基　　金：国网北京市电力公司科技项目“低压台区用户非介入式负荷辨识技术研究及负荷辨识关键装置研发应用”(SGBJDK00JLJS2250128)资助。

摘　　要：针对传统非侵入负荷辨识技术中电热细分能力不足的问题,文中提出了一种基于先验知识与统计学习模型的居民非侵入式负荷辨识算法。文中对洗衣机辅热、电水壶、电饭锅、电热水器等设备进行了电热细分研究,通过设备运行关联算法实现了辅热设备的细分,并在用户有限反馈信息和专家标注的基础上,实现了非辅热设备分类的模型训练。实验结果表明,文中所提技术框架在事件检测负荷辨识算法的基础上实现了电热设备的细分,且在运行状态分解的F1分数指标中取得了0.9以上的优异效果。In this paper,a non-intrusive load identification algorithm for residents based on prior knowledge and statistical learning model is proposed to solve the problem of insufficient electric heating subdivision capability in traditional identification technology.In this paper,the electric heating subdivision research is carried out for the auxiliary heating equipment of washing machine,electric kettle,electric rice cooker,electric water heater.The subdivision of auxiliary heating equipment is realized through the equipment operation association algorithm,and the model training of non-auxiliary heating equipment classification is realized based on the limited feedback information of users and expert annotation.The experimental results show that the technical framework proposed in this paper realizes the subdivision of electric heating equipment on the basis of the event detection load identification algorithm and F1 socre above 0.9 is achieved in the decomposition of operation state.

关 键 词：非侵入负荷监测(NILM) 事件检测 电热细分 统计分析 高斯混合聚类(GMM) 支持向量机(SVM) 

分 类 号：TM714[电气工程—电力系统及自动化]