| 刘艳丽,郭凤仪,朱连勇,游江龙,吴仁基,张西瑞,刘丽智,王培龙.矿用电连接器串联型故障电弧诊断方法研究[J].电子测量与仪器学报,2017,31(8):1257-1264 |
| 矿用电连接器串联型故障电弧诊断方法研究 |
| Study of diagnostic method on series fault arc of mining electric connector |
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| DOI：10.13382/j.jemi.2017.08.014 |
| 中文关键词: 矿用电连接器 串联型故障电弧 特征向量 随机森林 故障诊断 |
| 英文关键词:mining electric connector series arc fault eigenvector random forests fault diagnosis |
| 基金项目:国家自然科学基金(51674136)、辽宁工程技术大学市场调研基金（20160067T）、国家大学生创新创业计划训练项目（201610147000028）资助 |
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| Author | Institution |
| Liu Yanli | 1. Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China; 2. Faculty of Security, Liaoning Technical University, Huludao 125105, China |
| Guo Fengyi | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Zhu Lianyong | State Grid Huludao Electric Power Supply Company, Huludao 125099, China |
| You Jianglong | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Wu Renji | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Zhang Xirui | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Liu Lizhi | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Wang Peilong | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
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| 摘要点击次数: 3162 |
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| 中文摘要: |
| 为提高煤矿井下供电系统的可靠性，在不同电压、电流、功率因数、环境相对湿度条件下，开展了因机械振动引发的串联型故障电弧模拟实验。分析了不同实验参数对故障电弧的影响；提取串联型故障电弧相邻五周期电流信号中的过零点数、归一化后的方差、协方差构成特征向量；建立了基于随机森林分类算法的串联型故障电弧诊断模型，以正常运行及故障电弧电流信号的特征向量构成训练样本和测试样本作为随机森林模型的输入，对样本进行分类，进而诊断是否发生串联型故障电弧。结果表明，该方法能够有效地实现矿用电连接器串联型故障电弧的诊断。 |
| 英文摘要: |
| In order to improve the reliability of the coal mine power supply system, the simulation experiment in different voltage, current, power factor and environmental relative humidity conditions is carried out. The influence of different experimental parameters on the arc fault is analyzed, eigenvector constituted by the number of passing zero, normalized variance and covariance of current signals of adjacent five cycles on series arc fault are extracted, and a diagnosis model of series arc fault is established based on random forest classification algorithm. The training samples and test samples constituted by eigenvector of the normal operation and fault arc current signals are served as the input of random forest model, which are sorted to further diagnose whether the series arc fault is occurred. The results show that the method can effectively realize the diagnosis of series arc fault on mining electric connector. |
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