基于电流模式分解的非入户式故障电弧识别
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1.国网天津市电力公司营销服务中心;2.国网天津电力公司城南供电分公司

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基于非侵入式负荷特征提取技术的故障电弧检测方法研究 (KJ20-1-30)


Non-invasive arc fault recognition based on current mode decomposition
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1.State Grid Tianjin Electric Power Company Marketing Service Center Shenzhen Power Supply Bureau;2.Chengnan Power Supply Branch,State Grid Tianjin Electric Power Company

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    摘要:

    近年来电气火灾频发,故障电弧是重要诱因之一。本文考虑实际低压用户场景特点,开展非入户式故障电弧检测与识别方法研究。首先采集用户供电入口处的负荷总电流波形数据,而后通过谐波分析得到总电流基波和各次谐波的幅值和相位信息,接着将总电流和预训练得到的电流特征参数矩阵一起构建目标函数,形成多负荷电流分解模型,最后采用智能寻优算法进行最优化求解得到各个电器设备的运行状态(包括故障状态),判别电弧故障并分析其成因。在实验室条件下针对低压用户常见电器进行了故障电弧模拟实验,结果表明所提出的基于电流模式分解的非入户式故障电弧检测方法的有效性。

    Abstract:

    In recent years, electrical fires occur frequently, among which the arc fault is the important causes of electrical fires. In this paper, considering the characteristics of the low-voltage customer scenarios, the research of non-invasive arc fault detection is carried out. First, the aggregated load current waveform data is acquired at the entrance of the customer"s power supply. Then, the amplitude and phase information of the fundamental and the second harmonic of the total current is obtained by harmonic analysis. Next, the total current and the current characteristic parameter matrix obtained from training are used together to construct the objective function and form a multi-load current decomposition model. Finally, the intelligent optimization algorithm is adopted to optimize the solution to obtain the operating state of each appliance (including the fault states), to identify the arc fault and analyze its causes. In addition, this paper carries out the simulation experiment of arc fault for common appliances of actual low-voltage users in the laboratory, and the experimental results show that the proposed non-invasive arc fault recognition method is effective.

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  • 收稿日期:2021-03-26
  • 最后修改日期:2021-05-11
  • 录用日期:2021-06-25
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