|
|
|
| Frequency Domain Reflectometry Cell Average Constant False Alarm Rate Cable Defect Identification Method Considering Attenuation Compensation |
| Tang Zuoxin, Zhou Kai, Xu Yefei, Tang Zhirong, Huang Jingtao |
| School of Electrical Engineering Sichuan University Chengdu 610065 China |
|
|
|
Abstract The frequency domain reflectometry (FDR) method using sweep frequency signals is widely used in cable defect monitoring due to its high frequency components in the incident wave and its ability to quickly monitor weak defects. The broadband impedance spectroscopy (BIS) method and reflection coefficient spectrum (RCS) method, developed based on the FDR method, obtain cable characteristic parameters through scanning signal measurement, and can locate local defects in cables with high sensitivity. However, when using FDR to locate defects, spectrum leakage during Fourier transform can result in many "pseudo peaks" on the final localization spectrum. Due to the influence of these "pseudo peaks", it is difficult to accurately identify the position of defect peaks in the localization spectrum, and due to subjective human factors, it is highly likely to lead to misjudgment. At the same time, during on-site testing, the attenuation of the signal will increase with the increase of cable length, resulting in very inconspicuous cable defects during defect detection for some medium and long cables, causing significant economic losses for subsequent repairs. Therefore, the constant false alarm rate (CFAR) method can be introduced for automatic identification of cable defects. It can automatically extract the position and peak value of defects in the positioning spectrum, greatly reducing misjudgments caused by spectrum leakage and subjective human factors, and improving the fault detection rate.
Constant False Alarm Rate is a commonly used method in the field of radar target detection. During the process of detecting targets, radar is subject to interference from clutter. The constant false alarm rate detector adaptively changes the detection threshold with changes in the reference unit to achieve CFAR detection of the target. The ultimate goal is to determine whether the object detected by the radar is a target object, which is consistent with our goal of cable defect recognition. What we need is to determine whether the abnormal peak in the positioning spectrum is a defect. Therefore, the CFAR method has a foundation for application. The most basic form of constant false alarm rate is the cell average constant false alarm rate (CA-CFAR), which has a simple algorithm structure and fast calculation speed, and can be effectively applied in cable defect identification.
Firstly, in order to reduce the uncertainty of defect measurement caused by signal attenuation effect, a attenuation compensation method is adopted by approximating the attenuation factor with the test frequency and then deconvoluting it in the frequency domain. This effectively weakens the negative impact of excessive signal attenuation during cable propagation. Secondly, in order to reduce the problem of human subjective experience judgment being prone to misjudgment, the dynamic threshold of CFAR is used to extract the location and normalized amplitude information of defects in cables. The dynamic threshold dynamically selects the threshold for defect judgment based on the changes in test signal amplitude, avoiding the shortcomings of human subjective experience judgment. In order to test the effectiveness of this method, tests were conducted on 30 m, 105 m, and 500 m cables in the laboratory, as well as on 1500 m running cables. The test results showed that this method can not only accurately extract defect locations, but also effectively solve the problem of defect masking during CFAR detection.
|
|
Received: 09 June 2023
|
|
|
|
|
|
[1] 汪先进, 周凯, 谢敏, 等. 基于时间反演相位法的电力电缆局部放电定位[J]. 电网技术, 2020, 44(2): 783-790.
Wang Xianjin, Zhou Kai, Xie Min, et al.Research on partial discharge source location for power cables based on time reversal phase method[J]. Power System Technology, 2020, 44(2): 783-790.
[2] 刘凤莲, 夏荣, 李文杰, 等. 高压交联电缆缓冲层烧蚀缺陷检测方法研究[J]. 高压电器, 2022, 58(8): 259-266, 274.
Liu Fenglian, Xia Rong, Li Wenjie, et al.Research on detection method of buffer layer ablation defect in high voltage XLPE cable[J]. High Voltage Apparatus, 2022, 58(8): 259-266, 274.
[3] 赵书静, 詹博博, 龚梁涛, 等. 基于调频连续波相位敏感特性的电缆局部缺陷检测方法[J]. 电工技术学报, 2023, 38(11): 3009-3021.
Zhao Shujing, Zhan Bobo, Gong Liangtao, et al.Research on cable local defect detection method based on phase-sensitive characteristics of frequency modulated continuous wave[J]. Transactions of China Electrotechnical Society, 2023, 38(11): 3009-3021.
[4] 高晓欣. 电力电缆故障分析与探测[D]. 济南: 山东大学, 2012.
Gao Xiaoxin.Power cables fault analyse and detect[D]. Jinan: Shandong University, 2012.
[5] 李泽, 王辉, 钱勇, 等. 基于加速鲁棒特征的含噪局部放电模式识别[J]. 电工技术学报, 2022, 37(3): 775-785.
Li Ze, Wang Hui, Qian Yong, et al.Pattern recognition of partial discharge in the presence of noise based on speeded up robust features[J]. Transactions of China Electrotechnical Society, 2022, 37(3): 775-785.
[6] 周凯, 赵世林, 何珉, 等. 考虑短距离电缆中行波特性的振荡波局部放电定位方法[J]. 电网技术, 2017, 41(6): 2047-2054.
Zhou Kai, Zhao Shilin, He Min, et al.An oscillating wave test method based on traveling wave characteristics of partial discharges for defect location in short cables[J]. Power System Technology, 2017, 41(6): 2047-2054.
[7] 谢敏, 周凯, 赵世林, 等. 考虑相速度频变特性的改进互相关算法局部放电定位[J]. 电网技术, 2018, 42(5): 1661-1667.
Xie Min, Zhou Kai, Zhao Shilin, et al.Research on partial discharge location using modified cross-correlation method considering frequency characteristic of phase velocity[J]. Power System Technology, 2018, 42(5): 1661-1667.
[8] 李保生. 基于时域脉冲反射原理的电线电缆精确测长技术研究[D]. 西安: 西安电子科技大学, 2010.
Li Baosheng.Study on wire-cable length precision measurement technology base on TDPR[D]. Xi’an: Xi’an University of Science and Technology, 2010.
[9] Song E, Shin Y J, Stone P E, et al.Detection and location of multiple wiring faults via time-frequency-domain reflectometry[J]. IEEE Transactions on Electromagnetic Compatibility, 2009, 51(1): 131-138.
[10] Ohki Y, Yamada T, Hirai N.Diagnosis of cable aging by broadband impedance spectroscopy[C]//2011 Annual Report Conference on Electrical Insulation and Dielectric Phenomena, Cancun, Mexico, 2012: 24-27.
[11] 饶显杰, 周凯, 黄永禄, 等. 频域反射法中阻抗变化类型判断技术[J]. 电工技术学报, 2021, 36(16): 3457-3466.
Rao Xianjie, Zhou Kai, Huang Yonglu, et al.Type judgement technology of impedance variation in frequency domain reflection method[J]. Transactions of China Electrotechnical Society, 2021, 36(16): 3457-3466.
[12] Zhou Zhiqiang, Zhang Dandan, He Junjia, et al.Local degradation diagnosis for cable insulation based on broadband impedance spectroscopy[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2015, 22(4): 2097-2107.
[13] 饶显杰, 徐忠林, 刘翔宇, 等. 基于反射系数与核函数构建的新型电缆缺陷诊断方法[J]. 电工技术学报, 2024, 39(7): 2184-2192.
Rao Xianjie, Xu Zhonglin, Liu Xiangyu, et al.A new cable defect diagnosis method based on reflection coefficient and kernel function construction[J]. Transactions of China Electrotechnical Society, 2024, 39(7): 2184-2192.
[14] 陈禹. FMCW激光雷达测距测速信号处理算法研究[D]. 哈尔滨: 哈尔滨工业大学, 2021.
Chen Yu.Research on FMCW lidar ranging and velocity signal processing algorithm[D]. Harbin: Harbin Institute of Technology, 2021.
[15] 王蓓. 基于杂波图的恒虚警处理技术研究[D]. 西安: 西安电子科技大学, 2018.
Wang Bei.Research on constant false alarm processing technology based on clutter map[D]. Xi’an: Xi’an University of Science and Technology, 2018.
[16] Yan He, Chen Chao, Jin Guodong, et al.Implementation of a modified faster R-CNN for target detection technology of coastal defense radar[J]. Remote Sensing, 2021, 13(9): 1703.
[17] Paul C R.Analysis of Multiconductor Transmission Lines[M]. Wiley-IEEE Press, 2007.
[18] 谢志远, 孙艳, 郭以贺. 基于传输线理论的中压电力线信道建模和分析[J]. 电力科学与工程, 2010, 26(7): 5-8.
Xie Zhiyuan, Sun Yan, Guo Yihe.Modeling and analysis of medium voltage power line channel base on transmission line theory[J]. Electric Power Science and Engineering, 2010, 26(7): 5-8.
[19] 操雅婷, 周凯, 孟鹏飞, 等. 基于正交匹配-伪魏格纳分布的电缆缺陷定位[J]. 电工技术学报, 2023, 38(16): 4489-4498.
Cao Yating, Zhou Kai, Meng Pengfei, et al.Cable defect location based on orthogonal matching pursuit and pseudo wigner-ville distribution[J]. Transactions of China Electrotechnical Society, 2023, 38(16): 4489-4498.
[20] 旷建军, 阮新波, 任小永. 集肤和邻近效应对平面磁性元件绕组损耗影响的分析[J]. 中国电机工程学报, 2006, 26(5): 170-175.
Kuang Jianjun, Ruan Xinbo, Ren Xiaoyong.Analysis of skin and proximity effects on winding losses in planar magnetic components[J]. Proceedings of the CSEE, 2006, 26(5): 170-175.
[21] 宋建辉. 基于时域反射原理的电缆测长若干关键技术研究[D]. 哈尔滨: 哈尔滨工业大学, 2010.
Song Jianhui.Some key techniques of cable length measurement based on time domain reflectometry[D]. Harbin: Harbin Institute of Technology, 2010.
[22] 李蓉, 周凯, 万航, 等. 基于频域反射法的10 kV配电电缆中间接头受潮定位[J]. 电网技术, 2021, 45(2): 825-832.
Li Rong, Zhou Kai, Wan Hang, et al.Moisture location of 10 kV cable joints in medium voltage distribution grid based on frequency domain reflection[J]. Power System Technology, 2021, 45(2): 825-832.
[23] 高云鹏, 滕召胜, 曾博, 等. 基于Kaiser窗频谱校正的介质损耗因数测量[J]. 电工技术学报, 2009, 24(5): 203-208.
Gao Yunpeng, Teng Zhaosheng, Zeng Bo, et al.Dielectric loss factor measurement based on Kaiser window spectral correction[J]. Transactions of China Electrotechnical Society, 2009, 24(5): 203-208.
[24] 邹成晓, 张海霞, 程玉堃. 雷达恒虚警率检测算法综述[J]. 雷达与对抗, 2021, 41(2): 29-35.
Zou Chengxiao, Zhang Haixia, Cheng Yukun.Radar CFAR detection algorithms review[J]. Radar & ECM, 2021, 41(2): 29-35.
[25] Barkat M.Signal Detection and Estimation[M]. 2nd ed. Boston: Artech House, 2005.
[26] Mahafza B R.Radar Systems Analysis and Design Using Matlab[M]. 2nd ed. Boca Raton, FL: Chapman & Hall/CRC, 2005.
[27] 刘宇. 毫米波LFMCW雷达对地探测杂波抑制及恒虚警技术研究[D]. 南京: 南京理工大学, 2018.
[28] 郝迎春. 雷达CFAR检测算法及门限判决系数的优化研究[D]. 成都: 电子科技大学, 2012. |
|
|
|
2024, Vol. 39 
