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考虑分时段状态行为的非侵入式负荷分解方法
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华北电力大学

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国家自然科学基金


Non-intrusive Load Decomposition Method Considering Time-phase Behavior
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North China Electric Power University

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    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。在传统功率特征的基础上,综合考虑概率和时间两个维度,将分时段状态概率因子(time-phased state probability factor,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方法的有效性和准确性。

    Abstract:

    Load monitoring is an important part of intelligent electricity consumption. A non-intrusive load decomposition method considering time-phase behavior is proposed considering the problem that existing low frequency non-intrusive load decomposition methods require more priori information and have lower accuracy for load with similar or low 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. 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 decomposition is realized by multi-feature genetic optimization iteration. Finally, the validity and accuracy of the method are verified on the public data set.

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崔亮节,孙毅,刘耀先,等.考虑分时段状态行为的非侵入式负荷分解方法[J].电力系统自动化. DOI:10.7500/AEPS20190225006.

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历史
  • 收稿日期:2019-02-25
  • 最后修改日期:2020-01-14
  • 录用日期:2019-07-22
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