一种智能电表电路暂态故障实时识别方法

郭志华; 薛晓慧; 厉娜; 孟祥甫; 于洋洋; 刘庚

doi:10.19304/J.ISSN1000-7180.2022.0413

一种智能电表电路暂态故障实时识别方法

doi: 10.19304/J.ISSN1000-7180.2022.0413

1.
国网青海省电力公司, 青海西宁 810008
2.
国网思极紫光（青岛）微电子科技有限公司, 北京 100081

详细信息

作者简介:
郭志华：男,（1972-）,学士,高级工程师.主要研究领域为电力计量管理、计量新技术. E-mail：su16038995@163.com

薛晓慧：女,（1971-）,学士,高级工程师.主要从事电力计量、采集管理、营销信息化建设

厉娜：女,（1980-）,硕士,高级工程师.主要研究领域为电力计量管理、计量新技术

孟祥甫：男,（1980-）,硕士,高级工程师.主要研究领域为电力计量管理、计量新技术、信息安全

于洋洋：男,（1986-）,学士,工程师.主要研究方向为电力计量管理

刘庚：男,（1981-）,学士,高级工程师.主要研究领域为集成电路、微电子技术

中图分类号: TN702
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出版历程
- 收稿日期: 2022-07-08
- 修回日期: 2022-09-15

A real-time identification method for transient fault of intelligent meter circuit

1.
State Grid Qinghai Electric Power, Xining 810008, Qinghai, China
2.
Smart Shine Microelectronics Technology Co.,Ltd., Beijing 100081, China

摘要

摘要:
基于故障信号频率的识别模型由于受到周围磁场等的影响,精度较低,为提高智能电表电路暂态故障信号实时识别的准确性,从故障信号处理的方向出发,基于时域粗检测方法,研究了一种智能电表电路暂态故障实时识别方法. 对智能电表电路故障信号采样,使故障信号按照指定的路线传输,就要增加通道的接收能力,利用小波分析方法,对复杂的电信号进行样本预处理,经过时域检测后的信号被统一进行重新分布,以较为简单的方式进行标记；基于小波熵测度的故障信号融合模型,采用信息熵方法融合分离相似故障信号,确定所有智能电表故障电路信号暂态特征；构建基于小波熵测度的故障信号融合模型,在信号谱上完成信号的筛选与分解,实现智能电表电路暂态故障信号实时识别. 实验结果表明,针对智能电表电路接地短路、两相接地短路、相间短路、三相接地短路四种不同类型的暂态故障,通过该方法识别出的故障电压曲线值与实际的值相差小于0.3 V,具有较高的识别准确性,具备一定的实际应用意义.
- 时域粗检测 /
- 智能电表 /
- 电路暂态故障 /
- 识别 /
- 传播 /
- 截取
Abstract:
The identification model based on fault signal frequency has low accuracy due to the influence of surrounding magnetic field. In order to improve the accuracy of real-time identification of transient fault signal of smart meter circuit, from the direction of fault signal processing, a real-time identification method of transient fault of smart meter circuit based on time domain rough detection method is studied. To sample the fault signal of the smart meter circuit and make the fault signal transmit according to the specified route, it is necessary to increase the receiving capacity of the channel. The complex electrical signal is preprocessed by using the wavelet analysis method. The signal after time-domain detection is uniformly redistributed and marked in a relatively simple way; The fault signal fusion model based on wavelet entropy measure adopts the information entropy method to fuse and separate similar fault signals, and determines the transient characteristics of fault circuit signals of all smart meters; A fault signal fusion model based on wavelet entropy measure is constructed to complete the signal screening and decomposition on the signal spectrum, and realize the real-time identification of the transient fault signal of the smart meter circuit. The experimental results show that the difference between the fault voltage curve value identified by this method and the actual value is less than 0.3 V, which has high identification accuracy and certain practical application significance for the four different types of transient faults of the smart meter circuit, i.e. grounding short circuit, two-phase grounding short circuit, phase to phase short circuit and three-phase grounding short circuit.
- rough detection in time domain /
- smart meter /
- circuit transient fault /
- identification /
- propagation /
- interception

HTML全文

图 1 基于小波熵测度的故障信号融合模型

Figure 1. Failure signal fusion model based on the wavelet entropy measure

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图 2 输电线路分析模型

Figure 2. Analysis model of transmission lines

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图 3 智能电表电路暂态故障信号实时识别算法

Figure 3. Real-time identification algorithm of transient fault signal of smart meter circuits

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图 4 实验测试系统结构

Figure 4. Experimental test system structure

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图 5 D1故障电压分析

Figure 5. D1 fault voltage analysis

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图 6 D2故障电压分析

Figure 6. D2 fault voltage analysis

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图 7 D3故障电压分析

Figure 7. D3 fault voltage analysis

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图 8 D4故障电压分析

Figure 8. D4 fault voltage analysis

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表 1 故障类型

Table 1. Fault Type

编号故障模式故障发生时间

D1 接地短路 5:00
D2 两相接地短路 7:00
D3 相间短路 11:00
D4 三相接地短路 16:00

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