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基于非侵入式用电数据分解的自适应特征库构建与负荷辨识
作者:
作者单位:

华北电力大学电气与电子工程学院,北京市 102206

作者简介:

武昕(1986—),女,通信作者,博士,副教授,硕士生导师,主要研究方向:智能用电及信息处理、电力系统通信与信号处理技术。E-mail:wuxin07@ncepu.edu.cn
焦点(1996—),男,硕士研究生,主要研究方向:智能电力信息处理与非侵入负载监测。E-mail:a1321907679@163.com
高宇辰(1996—),男,硕士研究生,主要研究方向:电力信息处理技术与智能算法。E-mail:gyc199603@163.com

通讯作者:

基金项目:

北京市自然科学基金资助项目(3172034)。


Construction of Adaptive Feature Library and Load Identification Based on Decomposition of Non-intrusive Power Consumption Data
Author:
Affiliation:

School of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, China

Fund Project:

This work is supported by Natural Science Foundation of Beijing Municipality (No. 3172034).

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    摘要:

    非侵入负荷监测是实现需求侧测量与能效优化的有效途径。文中提出了一种高频采集模式下的非侵入式负荷在线监测方法,使整个监测过程自动化、实时化。首先,根据负荷电流的可加性原理建立了负荷分离模型,得到独立负荷波形;并结合负荷的操作特性,无需预实验获取先验数据。然后,通过贝叶斯分类模型实现负荷种类判断,从而在运行过程中为每个独立用户构建动态的负荷特征库。最后,基于库中数据,通过构建寻优模型实现负荷辨识,从而持续、实时获取负荷用电状态,并通过实际采集的用电数据验证了方法的有效性。该研究可自适应地为独立用户构建负荷特征库,改善了提前建库不具有普适性的问题,同时,基于特征库的快速寻优保证了辨识的有效性与准确性。

    Abstract:

    Non-intrusive load monitoring (NILM) is an effective way to realize measurement on demand side and optimization of energy efficiency. This paper explores an online NILM method in high-frequency acquisition mode, which ensures the entire process automative and real-time. Firstly, a load-decomposition model is established based on the additivity of load current to obtain the independent load waveform. Moreover, combined with the operation characteristics of load, priori data are obtained without pre-experiment. Then, the load types are judged by Bayesian classification model to construct dynamic feature library of load for every independent user during operation process. Finally, load identification is realized by constructing optimization model to continuously get the state of power consumption of load in real time. The power consumption data measured in actual scenario is used to verify the effectiveness of the method. The method can adaptively construct the dynamic feature library of load for independent users, which improves the weak universality caused by establishing the database in advance. The fast optimization based on feature library ensures the effectiveness and accuracy of identification.

    表 2 Table 2
    表 5 Table 5
    表 4 Table 4
    表 3 Table 3
    图1 非侵入式负荷辨识实现结构Fig.1 Implementation structure of non-intrusive load identification
    图2 分离的负荷电流与模板电流对比图Fig.2 Comparison of separated load current and template current
    图3 负荷的标签概率Fig.3 Labeling probability of load
    图4 第1天至第3天辨识负荷与实际用电量结果对比图Fig.4 Comparison of identified load electricity consumption and actual electricity consumption from the first day to the third day
    图5 算法性能对比Fig.5 Performance comparison of algorithms
    表 1 Table 1
    图 算法流程图Fig. The flow chart of the algorithm in this paper
    图 负荷聚类结果Fig. Load-clustering results
    图 各独立电器贴标签结果Fig. Labeling results of each independent electric appliance
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引用本文

武昕,焦点,高宇辰.基于非侵入式用电数据分解的自适应特征库构建与负荷辨识[J].电力系统自动化,2020,44(4):101-109. DOI:10.7500/AEPS20190612009.
WU Xin,JIAO Dian,GAO Yuchen.Construction of Adaptive Feature Library and Load Identification Based on Decomposition of Non-intrusive Power Consumption Data[J].Automation of Electric Power Systems,2020,44(4):101-109. DOI:10.7500/AEPS20190612009.

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  • 收稿日期:2019-06-12
  • 最后修改日期:2019-09-02
  • 录用日期:2019-09-02
  • 在线发布日期: 2020-02-25
  • 出版日期: