虚拟家庭负荷生成方法的研究  

Generation Method of Virtual Household Load

作　　者：吴蓓莉 辛洁晴[1] 韩跃峻 WU Beili;XIN Jieqing;HAN Yuejun(Shanghai Jiaotong University,Shanghai 200240,China;State Grid Shanghai Shibei Electricity Supply Company,Shanghai 200072,China)

机构地区：[1]上海交通大学,上海200240 [2]国网上海市北供电公司,上海200072

出　　处：《智慧电力》2021年第3期67-73,共7页Smart Power

基　　金：国家自然科学基金资助项目(51777121)。

摘　　要：针对非侵入式家庭负荷监测所需家庭用电设备实时运行数据难以获取的问题,分析不同家庭家电配置差异及使用习惯特性,提出虚拟家庭负荷生成方法。首先,采用多项式负荷模型生成家电负荷波形。其次,融合不同代际结构家庭家电配置和家电运行习惯特点,基于蒙特卡洛仿真生成计及家庭成员年龄、数量、身份用电关联的家庭用电时间分布。最后,虚拟生成4类典型的家庭日用电负荷曲线。研究结果表明,该方法虚拟生成的家庭用电负荷波形与实测波形的功率平均相对误差小,可生成最接近实际情况的虚拟家庭负荷波形,另外家庭用户关联性用电生成了大范围覆盖的低采样率实验数据,可用于后续非侵入式负荷辨识的检验,是目前所有方法中的最优方法。For non-intrusive household load monitoring,it is difficult to obtain actual information about the running status of household appliances.The paper analyzes the household appliance configuration differences of families and user’s behavior,and proposes a method for generating virtual household load data.Firstly,the load waveform of the household appliances is generated based on polynomial load model.Secondly,by integrating the configuration and operating habits of the household appliances of families with different generation structures,the time distribution of the household power with Monte Carlo simulation is obtained considering the information to family members about age,number and identity.Finally,four typical household daily electricity load profiles are simulated.The calculation results show that the deviation value between the household appliance load waveform generated by the proposed method and the measured waveform is the smallest,which can generate the virtual household load waveform close to actual conditions.Considering the low sampling rate experimental data with a wide range of coverage generated by household user-related electricity,the virtual household load data can be used for the subsequent test of non-intrusive load identification,which is the best method in the non-intrusive household load monitoring.

关 键 词：高速电力线载波 非侵入式负荷监测 多项式负荷模型 蒙特卡洛仿真 用电行为 

分 类 号：TM764[电气工程—电力系统及自动化]