| 引用本文: | 周鲁天,梁睿,彭楠,许传义,滕松,陈轩.基于ARIMA的矿山电网故障暂态行波波头辨识及故障测距[J].电力自动化设备,2020,40(6): |
| ZHOU Lutian,LIANG Rui,PENG Nan,XU Chuanyi,TENG Song,CHEN Xuan.Transient traveling wave front identification and fault location in mine power grid based on ARIMA[J].Electric Power Automation Equipment,2020,40(6): |
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| 基于ARIMA的矿山电网故障暂态行波波头辨识及故障测距 |
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周鲁天1,2, 梁睿1,2, 彭楠1,2, 许传义1,2, 滕松3, 陈轩4
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1.江苏省煤矿电气与自动化工程实验室，江苏 徐州 221116;2.中国矿业大学 电气与动力工程学院，江苏 徐州 221116;3.国网江苏省电力公司 徐州供电公司，江苏 徐州 221000;4.国网江苏省电力公司检修分公司，江苏 南京 210000
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| 摘要: |
| 由于矿山电网含有大量的整流设备及非线性负载，运行时含有稳定的高次谐波分量和高频噪声，同时矿山电网多为短距离线路，故障后产生的暂态信号与原有高次谐波混叠严重，给行波故障测距带来了极大的困难。通过分析矿山电网故障行波的时域特征，提出基于整合移动平均自回归模型(ARIMA)对行波波头到达前的高频周期信号进行预测，并结合波头到达时刻的真实波形得到波形残差，同时对残差进行平稳性校验，通过行波波头到达时刻前后残差平稳性的不同确定准确的波头到达时刻，进而实现行波故障测距。利用低压电缆网络仿真实现矿山电网故障，仿真结果表明：与小波变换与经验模态分解相比，所提方法能够准确辨识行波波头，且不易受故障状况和噪声的影响，能有效提升行波可行性及精度，尤其适用于含有整流设备及非线性负载矿山电网故障测距。 |
| 关键词: 故障暂态信号 整合自回归移动平均模型 波头辨识 故障测距 矿山电网 |
| DOI：10.16081/j.epae.202005031 |
| 分类号:TM77 |
| 基金项目:国家重点研发计划项目(2017YFC0804400)；江苏省“六大人才高峰”项目(XNY-046)；国家电网公司科技项目(J2018078) |
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| Transient traveling wave front identification and fault location in mine power grid based on ARIMA |
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ZHOU Lutian1,2, LIANG Rui1,2, PENG Nan1,2, XU Chuanyi1,2, TENG Song3, CHEN Xuan4
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1.Jiangsu Province Laboratory of Electrical and Automation Engineering for Coal Mining, Xuzhou 221116, China;2.School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China;3.Xuzhou Electric Power Supply Company, State Grid Jiangsu Electric Power Co.,Ltd.,Xuzhou 221000, China;4.Maintenance Branch of State Grid Jiangsu Electric Power Company, Nanjing 210000, China
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| Abstract: |
| Because of a large number of rectifier equipment and non-linear loads accessed, the mine power grid contains stable high-order harmonic components and high frequency noise in operation. At the same time, the mine power grid has a large number of short-distance lines, so the transient signal generated by fault is seriously overlapped with the original high-order harmonic, which brings great difficulties to traveling wave fault location. By analyzing the time domain characteristics of fault traveling wave in mine power grid, it is proposed to predict the high frequency periodic signal before the arrival of the traveling wave front based on ARIMA(AutoRegressive Integrated Moving Average model),combined with the real waveform of the wave front arrival time the waveform residual can be obtained, and the stability of the residual error is checked. The exact arrival time of the traveling wave front is determined through the difference of residual stationarity before and after the arrival time of wave front, then the fault location is realized. The fault of the mine power grid is simulated by low voltage cable network. The simulative results show that compared with the wavelet transform and empirical mode decomposition, the proposed method can accurately identify the traveling wave front even with the influence of fault condition and noise. It can effectively improve the accuracy and reliability of the traveling wave fault location and is especially suitable for the mine power grid with rectifier equipment and non-linear loads. |
| Key words: transient fault signal ARIMA wave front identification fault location mine power grid |
