考虑分时段状态行为的非侵入式负荷分解方法  

Non-intrusive Load Disaggregation Method Considering Time-phased State Behavior

作　　者：崔亮节 孙毅[1] 刘耀先 文亚凤[1] CUI Liangjie;SUN Yi;LIU Yaoxian;WEN Yafeng(School of Electrical and Electronic Engineering,North China Electric Power University,Beijing 102206,China)

机构地区：[1]华北电力大学电气与电子工程学院,北京市102206

出　　处：《电力系统自动化》2020年第5期215-222,共8页Automation of Electric Power Systems

基　　金：国家自然科学基金资助项目(5177068)。

摘　　要：负荷监测是智能用电的一个重要环节,针对现有低频非侵入式负荷分解方法需要较多先验信息,且对功率相近或小功率负荷的辨识精度较低的问题,提出了一种考虑分时段状态行为的非侵入式负荷分解方法。首先,对负荷设备的功率数据进行聚类分析,构建负荷状态模板。提出一种不需要指定时间段个数的负荷典型行为时间段智能寻优方法,分时段提取负荷状态行为规律,构建负荷行为模板。然后,在传统功率特征的基础上,综合考虑概率和时间2个维度,将分时段状态概率因子(TSPF)作为负荷新特征引入目标函数,通过多特征遗传优化迭代实现负荷分解。最后,在公开数据集上验证了所提方法的有效性和准确性。Load monitoring is an important part of intelligent electricity consumption. A non-intrusive load disaggregation method considering time-phased state behavior is proposed to solve the problem that existing low frequency non-intrusive load disaggregation methods require more priori information and have lower accuracy for load with similar or lower power. Firstly,power data of the load device is clustered to construct a load state template. An intelligent optimization method for the typical behavior time period that does not require a specified number of time periods is proposed. Load state behavior law is extracted by time-phase to construct a load behavior template. Then, on the basis of the traditional power characteristics, considering the two dimensions of probability and time, the time-phased state probability factor(TSPF) is introduced into the objective function as a new load characteristic, and the load disaggregation is realized by multi-feature genetic optimization iteration. Finally, the validity and accuracy of the method are verified on the public data set.

关 键 词：非侵入式负荷分解 负荷典型行为时间段 分时段状态概率因子 多特征遗传优化 

分 类 号：TM714[电气工程—电力系统及自动化]