基于HVD算法的负荷暂态事件监测
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1.东北电力大学自动化工程学院, 长春 132012;2.贵阳航空电机有限公司, 贵阳550025

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Load Transient Event Monitoring Based on HVD Algorithm
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1.School of Automation Engineering Northeast Electric Power University, Changchun 132012, China;2.AVIC Guiyang Aviation Motor Co Ltd, Guiyang 550025, China

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    摘要:

    通过非侵入式负荷监测技术,可以更为详细地了解居民各个时段的用电信息,帮助其制订合理的用电计划,以达到科学用电。非侵入式负荷监测技术的重点是暂态事件的监测,本文提出希尔伯特振动分解(Hilbert vibration decomposition,HVD)算法对用电设备开启瞬间的功率、电流等电气参数变化时存在的突变点进行暂态事件的监测。HVD算法负荷监测与双边滑动窗CUSUM变点监测算法相比,不用设定阈值,所以漏检和误检的可能性大大降低。通过MATLAB/Simulink仿真软件搭建相应的电路模型,仿真分析得出HVD算法可以有效地对暂态事件进行辨识。

    Abstract:

    Through non-intrusive load monitoring technology, a more detailed understanding of the electricity consumption information of residents at various time periods can be obtained, This can help us to develop a reasonable electricity consumption plan for scientific electricity use. The focus of non-intrusive load monitoring technology is the detection of transient events. The proposed Hilbert vibration decomposition (HVD) algorithm detects the transient events in the sudden change of electrical parameters such as power and current when the electrical equipment is turned on. Compared with the double-sliding window CUSUM change point detection algorithm, the HVD algorithm load detection does not need to set a threshold, so the possibility of missed and false detection is greatly reduced. The corresponding circuit model is built by MATLAB / Simulink simulation software, and the simulation analysis shows that the HVD algorithm can effectively identify transient events.

    表 1 负荷状态序号值Table 1 Load status number value
    图1 仿真信号时域波形图Fig.1 Simulation signal time domain waveform
    图2 HVD分解后的仿真信号Fig.2 Simulation signal after HVD decomposition
    图3 HVD分解后的瞬时频率和瞬时幅值Fig.3 Instantaneous frequency and instantaneous amplitude after HVD decomposition
    图4 3种负荷电流曲线图Fig.4 Three load current curves
    图5 负荷电流幅值曲线图Fig.5 Load current amplitude curve
    图6 边界延拓后的负荷电流幅值曲线图Fig.6 Load current amplitude curve after boundary extension
    图7 热水壶的电流曲线Fig.7 Current curve of the kettle
    图8 热水壶的有功功率曲线Fig.8 Active power curve of the kettle
    图9 热水壶突变时刻定位检测Fig.9 Kettle mutation moment location detection
    图10 仿真模型Fig.10 Simulation model
    图11 6种负荷电流曲线图Fig.11 Six load current curves
    图12 负荷有功功率曲线图Fig.12 Load Active power curve
    图13 暂态事件辨识结果图Fig.13 Graph of transient event identification result
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温伟伟,武金亚,王建元.基于HVD算法的负荷暂态事件监测[J].数据采集与处理,2021,36(2):289-295

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  • 收稿日期:2020-02-21
  • 最后修改日期:2020-04-19
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  • 在线发布日期: 2021-04-15