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

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

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This work is supported by National Natural Science Foundation of China (No. 51777068).

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    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.

    表 3 各方法功率分解准确度对比Table 3 Comparison of power decomposition accuracy for each method
    表 4 Table 4
    表 9 Table 9
    表 5 Table 5
    表 7 Table 7
    图1 染色体编码方案Fig.1 Coding scheme for chromosome
    图2 考虑分时段状态行为的NILM方法流程图Fig.2 Flow chart of NILM method considering time-phased state behavior
    图3 所提方法求解时间Fig.3 Solution time of proposed method
    图 典型负荷有功功率时间分布图Fig. Time distribution chart of active power of typical load
    图 负荷典型状态行为时间段智能寻优流程图Fig. Intelligent optimization flow chart of load typical state behavior time period
    图 场景1各负荷状态聚类结果Fig. The clustering results of load states in scene 1
    图 场景1各负荷全天状态行为概率分布图Fig. Probability distribution of all-day state behavior of each load in scene 1
    图 场景1各负荷典型行为时间段智能寻优结果Fig. The results of typical behavioral time period intelligent optimization of each load in scene 1
    图 场景1各负荷分时段状态行为分布图Fig. Time-phased state behavior distribution of each load in scene 1
    图 场景1本文方法权重因子自适应选优结果Fig. Adaptive selection result of weighting factors of the method in scene 1
    图 场景1本文方法分解结果Fig. Decomposition results of the method in scene 1
    表 2 各方法状态识别准确率对比Table 2 Comparison of state recognition accuracy for each method
    表 1 3种负荷设备组合场景对比Table 1 Scenarios comparisons of three load equipment combinations
    表 6 Table 6
    表 8 Table 8
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CUI Liangjie,SUN Yi,LIU Yaoxian,et al.Non-intrusive Load Disaggregation Method Considering Time-phased State Behavior[J].Automation of Electric Power Systems,2020,44(5):215-222.DOI:10.7500/AEPS20190225006

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  • Received:February 25,2019
  • Revised:July 09,2019
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  • Online: March 08,2020
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