Research on fault positioning of underground power cable
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摘要: 针对传统井下电力电缆故障定位方法依赖主观参数选择和抗噪性能较差，无法满足强噪声背景下井下电力电缆故障精确定位要求的问题，提出了一种基于樽海鞘群算法(SSA)优化变分模态分解(VMD)并结合改进型Teager能量算子(NTEO)的井下电力电缆故障定位方法。针对VMD在信号分解上存在的模态混叠、过分解和欠分解问题，采用SSA以模糊熵为适应度函数对VMD模态数K和惩罚因子$ \alpha $ 2个参数进行优化，得到更能反映故障特征信息的本征模态函数；采用NTEO对本征模态函数进行首波波头标定，得到首末两端的波头到达时刻，根据双端测距法得出故障位置。采用PSCAD/EMTDC进行井下电力电缆故障仿真，模拟具有强背景噪声的井下故障信号，结果表明：① 在理想电流信号中加入9 ，12 dB噪声后，SSA−VMD的信噪比最低，皮尔逊相关系数最大，说明SSA−VMD在最大程度降噪的同时，能很好地保留信号的特征信息。② 在不同过渡电阻下，SSA−VMD−NTEO的定位精度较高。③ 在不同故障相角下，SSA−VMD−NTEO在采样点上出现不同，但定位位置没有改变，依旧保持较高的定位精度。④ 在不同故障距离下，SSA−VMD−NTEO均能保证较高的定位精度。⑤ 在井下较大噪声和10 MHz采样频率下，SSA−VMD−NTEO较小波模极大值和VMD+NTEO 2种方法的定位精度具有明显优势。Abstract: The traditional underground power cable fault positioning method relies on subjective parameter selection and noise resistance is poor. It cannot meet the accurate fault positioning requirements of underground power cable under strong noise background. In order to solve the above problems, a fault positioning method of underground power cable based on salp swarm algorithm (SSA) optimizing variational mode decomposition (VMD) combined with novel Teager energy operator (NTEO) is proposed. In response to the problem of modal aliasing, over decomposition, and under decomposition in signal decomposition of VMD, SSA is used to optimize the modal number K and penalty factor α of VMD parameters using fuzzy entropy as the fitness function. The intrinsic modal function that better reflects the fault feature information is obtained. NTEO is used to calibrate the first wave head to obtain the arrival time of the wave heads at both ends. The fault position is determined based on the dual end distance measurement method. PSCAD/EMTDC is used for underground power cable fault simulation. It simulates underground fault signals with strong background noise. The results show the following points. ① After adding 9 dB and 12 dB noise to the ideal current signal, the signal-to-noise ratio of SSA-VMD is the lowest, and the Pearson correlation coefficient is the highest. It indicates that SSA-VMD can effectively preserve the characteristic information of the signal while minimizing noise. ② Under different transition resistances, the positioning precision of SSA-VMD-NTEO is relatively high. ③ Under different fault phase angles, although SSA-VMD-NTEO may have different sampling points, the positioning position remains unchanged and still maintains high precision. ④ SSA−VMD-NTEO can ensure high positioning precision at different fault distances. ⑤ Under high underground noise and a sampling frequency of 10 MHz, SSA-VMD-NTEO has significant advantages in positioning precision compared to wavelet modulus maximum and VMD+NTEO methods.
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图 2 VMD分解下信号中心频率分布
Figure 2. Signal center frequency distribution under VMD decomposition
图 5 M侧各参数FE对比
Figure 5. Comparison of fuzzy entropy (FE) of various parameters on M-side
图 6 N侧各参数FE对比
Figure 6. Comparison of fuzzy entropy （FE） of various parameters on N-side
表 1 不同算法的信号分解结果
Table 1. Filtering results of different algorithms
算法 SNR/dB PCC 9 dB噪声 12 dB噪声 9 dB噪声 12 dB噪声 小波硬阈值 9.82 12.77 0.973 0.965 小波软阈值 9.43 12.76 0.986 0.976 VMD 9.33 12.76 0.990 0.989 SSA−VMD 9.15 12.19 0.996 0.998 
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表 2 不同过渡电阻下故障定位结果
Table 2. Fault positioning results under different transition resistance
过渡电阻/Ω 波头采样点 定位位置/m 定位误差/m M侧 N侧 0.1 5 032 5 022 599.13 0.87 10 5 032 5 022 599.13 0.87 1 000 5 032 5 022 599.13 0.87
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表 3 不同故障相角下故障定位结果
Table 3. Fault positioning results under different fault phase angles
故障相角/（°） 波头采样点 定位位置/m 定位误差/m M侧 N侧 0 5 033 5 023 599.13 0.87 30 5 032 5 022 599.13 0.87 60 5 029 5 019 599.13 0.87 90 5 030 5 020 599.13 0.87
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表 4 不同故障距离下故障定位结果
Table 4. Fault positioning results under different fault distances
故障距离/m 波头采样点 定位位置/m 定位误差/m M侧 N侧 100 5 011 5 051 103.57 3.57 200 5 098 5 128 202.61 2.61 300 5 042 5 062 301.74 1.74 400 5 061 5 071 400.87 0.87 500 5 059 5 059 500.00 0 600 5 032 5 022 599.13 0.87 700 5 067 5 047 698.26 1.74 800 5 143 5 113 797.39 2.61 900 5 125 5 185 896.43 3.57
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表 5 不同方法的故障定位结果
Table 5. Fault positioning results of different methods
故障位置/m 方法 定位位置/m 定位误差/m 400 小波模极大值 419.96 19.96 VMD+NTEO 409.04 9.04 SSA−VMD−NTEO 400.87 0.87 800 小波模极大值 837.04 37.04 VMD+NTEO 817.21 17.21 SSA−VMD−NTEO 797.39 2.61
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[1] 赵利. 融合小电流接地的矿山电网接地选线设计[J]. 煤炭工程,2019,51(9):48-51.ZHAO Li. Fault line selection of coalmine distribution network integrated small current neutral grounding system[J]. Coal Engineering,2019,51(9):48-51. [2] 周鲁天. 基于LSTM的矿山电网行波波头辨识[D]. 徐州:中国矿业大学,2020.ZHOU Lutian. Traveling wave front identification in mine power grid based on LSTM[D]. Xuzhou: China University of Mining and technology, 2020. [3] 罗超,耿蒲龙,曲兵妮,等. 基于小波包的矿井供电系统单相接地故障选线方法[J]. 工矿自动化,2018,44(2):68-74.LUO Chao,GENG Pulong,QU Bingni,et al. A line selection method for single phase ground fault in coal mine power supply system based on wavelet packet[J]. Industry and Mine Automation,2018,44(2):68-74. [4] 王国法,赵国瑞,任怀伟. 智慧煤矿与智能化开采关键核心技术分析[J]. 煤炭学报,2019,44(1):34-41.WANG Guofa,ZHAO Guorui,REN Huaiwei. Analysis on key technologies of intelligent coal mine and intelligent mining[J]. Journal of China Coal Society,2019,44(1):34-41. [5] 王升花. 煤矿供电系统电能质量综合评价[J]. 工矿自动化,2017,43(2):86-89.WANG Shenghua. Comprehensive evaluation of power quality of coal mine power supply system[J]. Industry and Mine Automation,2017,43(2):86-89. [6] 王国法,李世军,张金虎,等. 筑牢煤炭产业安全奠定能源安全基石[J]. 中国煤炭,2022,48(7):1-9.WANG Guofa,LI Shijun,ZHANG Jinhu,et al. Ensuring the safety of coal industry to lay the cornerstone of energy security[J]. China Coal,2022,48(7):1-9. [7] 王炜,王全金,尹力,等. 基于零模行波波速量化的高压输电线路双端故障定位方法[J]. 电力自动化设备,2022,42(12):165-170.WANG Wei,WANG Quanjin,YIN Li,et al. Two-terminal fault location method for high-voltage transmission line based on zero-mode traveling wave velocity quantization[J]. Electric Power Automation Equipment,2022,42(12):165-170. [8] 周鲁天,梁睿,彭楠,等. 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):177-188.ZHOU Lutian,LIANG Rui,PENG Nan,et al. Transient traveling wave front identification and fault location in mine power grid based on ARIMA[J]. Electric Power Automation Equipment,2020,40(6):177-188. [9] 郭秀才,刘冰冰,王力立. 基于小波包和CS−BP神经网络的矿用电力电缆故障诊断[J]. 计算机应用与软件,2021,38(9):105-110.GUO Xiucai,LIU Bingbing,WANG Lili. Fault diagnosis of mining power cable based on wavelet packet an CS-BP neural network[J]. Computer Applications and Software,2021,38(9):105-110. [10] 赵建文,孟旭辉. 数字孪生在煤矿电网中的应用研究[J]. 工矿自动化,2023,49(2):38-46.ZHAO Jianwen,MENG Xuhui. Research on the application of digital twin in coal mine power grid[J]. Journal of Mine Automation,2023,49(2):38-46. [11] 詹惠瑜,刘科研,盛万兴,等. 有源配电网故障诊断与定位方法综述及展望[J]. 高电压技术,2023,49(2):660-671.ZHAN Huiyu,LIU Keyan,SHENG Wanxing,et al. Review and prospects of fault diagnosis and location method in active distribution network[J]. High Voltage Engineering,2023,49(2):660-671. [12] 徐岩,胡紫琪,董浩然. 等. 基于灰色综合关联度的柔性直流配电网故障定位[J]. 太阳能学报,2023,44(4):324-331.XU Yan,HU Ziqi,DONG Haoran,et al. Fault location based on comprehensive grey relational degree for flexible DC distribution network[J]. Acta Energiae Solaris Sinica,2023,44(4):324-331. [13] 赵敏,尚鹏辉. 井下配电网电缆故障在线双端行波测距方法[J]. 工矿自动化,2016,42(11):50-55.ZHAO Min,SHANG Penghui. Online cable fault ranging method by double-end traveling wave for underground distribution network[J]. Industry and Mine Automation,2016,42(11):50-55. [14] 毕胜,耿蒲龙,张建花,等. 基于CEEMD与自相关阈值去噪的单相接地故障选线方法研究[J]. 煤炭工程,2022,54(7):153-158.BI Sheng,GENG Pulong,ZHANG Jianhua,et al. Line selection method for single phase ground fault based on CEEMD and autocorrelation threshold denoising[J]. Coal Engineering,2022,54(7):153-158. [15] 吴赛. 基于VMD−GST−TEO的煤矿井下输电线路故障定位[D]. 阜新:辽宁工程技术大学,2020.WU Sai. Fault location of underground transmission line based on VMD-GST-TEO in coal mine [D]. Fuxin:Liaoning Technical University,2020. [16] 杜政奇,王敬华,张新慧. 基于参数优化VMD和能量相似度的配电网故障区段定位方法[J]. 电子测量技术,2022,45(8):95-101.DU Zhengqi,WANG Jinghua,ZHANG Xinhui. Fault section location in distribution network based on parameter optimization VMD and energy similarity[J]. Electronic Measurement Technology,2022,45(8):95-101. [17] 荣相. 矿用变频器性能测试系统设计[J]. 工矿自动化,2021,47(5):9-15.RONG Xiang. Design of mine inverter performance test system[J]. Idustry and Mine Automation,2021,47(5):9-15. [18] 张伟,李军霞,陈维望. 基于蝙蝠算法优化VMD参数的滚动轴承复合故障分离方法[J]. 振动与冲击,2022,41(20):133-141.ZHANG Wei,LI Junxia,CHEN Weiwang. A compound fault feature separation method of rolling bearings based on VMD optimized by the bat algorithm[J]. Journal of Vibration and Shock,2022,41(20):133-141. [19] 罗亦泳,姚宜斌,黄城,等. 基于改进VMD的变形特征提取与分析[J]. 武汉大学学报(信息科学版),2020,45(4):612-619.LUO Yiyong,YAO Yibin,HUANG Cheng,et al. Deformation feature extraction and analysis based on improved VMD[J]. Geomatics and Information Science of Wuhan University,2020,45(4):612-619. [20] 任学平,李攀,王朝阁,等. 基于改进VMD与包络导数能量算子的滚动轴承早期故障诊断[J]. 振动与冲击,2018,37(15):6-13.REN Xueping,LI Pan,WANG Chaoge,et al. Rolling bearing early fault diagnosis based on improved VMD and envelope derivative operator[J]. Journal of Vibration and Shock,2018,37(15):6-13. [21] 李一鸣. 基于小波包多尺度模糊熵和加权KL散度的煤岩智能识别[J]. 工矿自动化,2023,49(4):92-98.LI Yiming. Intelligent recognition of coal and rock based on wavelet packet multi-scale fuzzy entropy and weighted KL divergence[J]. Journal of Mine Automation,2023,49(4):92-98. [22] SEYEDALI M,AMIR H G,SEYEDEH Z M,et al. Salp swarm algorithm:A bio-inspired optimizer for engineering design problems[J]. Advances in Engineering Software,2017,114(6):163-191. [23] 吴传龙,陈伟,刘晓文,等. 基于特征融合的提升机逆变器故障诊断[J]. 工矿自动化,2021,47(5):46-51.WU Chuanlong,CHEN Wei,LIU Xiaowen,et al. Feature fusion based fault diagnosis of hoist inverter[J]. Industry and Mine Automation,2021,47(5):46-51. [24] 范新桥,朱永利,卢伟甫. 基于EMD−TEO的输电线路行波故障定位[J]. 电力系统保护与控制,2012,40(9):8-12,17.FAN Xinqiao,ZHU Yongli,LU Weifu. Traveling wave based fault location for transmission lines based on EMD-TEO[J]. Power System Protection and Control,2012,40(9):8-12,17. [25] 骆玮,王恒,王磊,等. 基于设备信息交互的小电流接地故障定位[J]. 电力系统保护与控制,2019,47(4):73-82.LUO Wei,WANG Heng,WANG Lei,et al. Faulted line location method for distribution systems based on the equipment's information exchange[J]. Power System Protection and Control,2019,47(4):73-82. [26] 郭威, 史运涛. 基于空间域图像生成和混合卷积神经网络的配电网故障选线方法[J/OL]. 电网技术: 1-14.[2023-08-04]. http://kns.cnki.net/kcms/detail/11.2410.tm.20230310.1705.003.html.GUO Wei, SHI Yuntao. Fault line selection for distribution network based on spatial domain image generation and hybrid convolutional neural network[J/OL]. Power System Technology: 1-14[2023-08-04]. http://kns.cnki.net/kcms/detail/11.2410.tm.20230310.1705.003.html. -
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