| 引用本文: | 丁 锐,陈 羽,孙伶雁,程 钎,刘志栋.基于邻波电流差与随机性的交流串联电弧故障识别[J].电力系统保护与控制,2023,51(8):169-178.[点击复制] |
| DING Rui,CHEN Yu,SUN Lingyan,CHENG Qian,LIU Zhidong.Series arc fault detection in low-voltage AC power lines based on absolute difference of theneighboring waveform of the current and randomness[J].Power System Protection and Control,2023,51(8):169-178[点击复制] |
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| 摘要: |
| 低压电力线路的交流串联电弧故障易引发电气火灾，造成人身财产损失。根据故障的电流突变量幅值与电流变化量的电弧随机性特征，提出了基于电流邻波绝对差与随机性的电弧识别方法。该方法基于故障前后的电流突变量变化规律，以突变幅度作为故障启动判据。然后根据故障周期间电流变化量在不同负载种类、气隙间距下的电弧随机特征时域分布，构建了电弧故障存在性判据。最后通过一维卷积神经网络综合识别电弧故障。当故障支路负载功率占比20%时，所提方法使用未训练干路数据的平均检测准确率为90.97%，可有效检测串联电弧故障，具有较好的适应性。 |
| 关键词: 串联电弧故障检测 电流差值 随机性 波形特征 卷积网络 |
| DOI：10.19783/j.cnki.pspc.221312 |
| 投稿时间：2022-08-15修订日期：2023-01-03 |
| 基金项目:国家自然科学基金项目资助(52077221)；国网湖北省电力有限公司科技项目资助(52153220001V) |
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| Series arc fault detection in low-voltage AC power lines based on absolute difference of theneighboring waveform of the current and randomness |
| DING Rui,CHEN Yu,SUN Lingyan,CHENG Qian,LIU Zhidong |
| (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255049, China) |
| Abstract: |
| The AC series arc fault of low-voltage power lines is prone to electrical fires, causing personal and property loss. Given the sudden change of fault current amplitude and the arc randomness waveform characteristics of the current change, this paper proposes a recognition method based on the absolute difference of neighboring waves and randomness of the fault current. The method is based on the change law of the current abrupt change amount before and after the fault, and the abrupt change amplitude is used as the initiation criterion. Then an arc fault existence criterion is constructed based on the arc random characteristic time domain distribution of the current change amount during the fault cycle under different load types and air gap spacing. Finally, arc fault identification is achieved through a one-dimensional convolutional neural network (1DCNN) integrated. The average detection accuracy of the method using untrained trunk circuit data is 90.97% when the load power of the faulty branch circuit accounts for 20%. Thus this can effectively detect series arc faults with good adaptability. |
| Key words: series arc fault detection difference current randomness waveform features convolutional neural network |
