Abstract：Transient arc breakdown is recognized as an important precursor to permanent faults in power cables. Currently, diagnostic methods for such faults primarily depend on the online monitoring of arc signals. Due to the randomness and rapid changes associated with transient arcs, along with interference from noise signals, the challenges in locating and identifying these faults of this method are significant. Given that transient arcs may create carbonized breakdown channels in cable insulation, causing reflections of traveling waves, the feasibility of employing offline wave reflection methods for diagnostics has been explored. The frequency domain reflection (FDR) method, noted for its high sensitivity to slight impedance changes, is utilized in this study to diagnose channel defects induced by transient arc breakdown and to establish identification criteria. However, traditional FDR diagnostic curves are plagued with many interfering waveforms, making it challenging to determine the type of defect. To address these issues, an improved Z-transform algorithm is proposed in this paper, and transient arc breakdown channel defects are prepared, ensuring high location accuracy and sensitivity while facilitating the discrimination of defect types. First, an equivalent electrical circuit model of the power cable under distributed parameters is constructed, from which the reflection coefficient spectrum (RCS) at the cable's head end is derived. The application of the Z-transform algorithm to process the RCS and the settings of various parameters are then thoroughly analyzed. Despite these measures, the diagnostic results still contain numerous sidebands and interfering waveforms, and the polarity information of the reflection coefficients cannot be discerned. Considering the wide applications of the hyperbolic secant pulse, a rapidly attenuating and smoothly transitioning unipolar wave in fields such as signal processing, communications, and fiber optics, this pulse is employed to modulate the RCS and to refine the Z-transform. Moreover, various types of defects are set up on a 500-meter-long simulated cable for diagnostic validation. The improved algorithm is shown to maintain high location accuracy and sensitivity, providing correct criteria for defect types based on the polarity information of the reflection coefficients. Subsequently, an experimental platform for transient arc breakdown is designed and assembled using experimental transformers, voltage divider, voltage probes and current probes among other devices. The defects produced experimentally are confirmed as transient arc breakdown channels through the analysis of online waveforms and defect cross-sections. And then the improved algorithm and traditional FDR method are applied to process the reflection coefficient spectrum at the first end of the defective cable, and time-domain reflectometry (TDR) is used for comparison in diagnosing the defective cable. The diagnostic results from the improved Z-transform algorithm indicate that the position error of the breakdown channel is less than 0.5%, identifying it as an inductive defect. In contrast, TDR results are unable to ascertain the defect's location, and although the traditional FDR method achieves defect localization, it does so with a location error of approximately 6%, accompanied by numerous interfering waveforms, failing to provide criteria for defect type. In conclusion, the improved Z-transform algorithm proposed in this paper ensures a location accuracy of less than 0.5% for transient arc breakdown channels and provides criteria for their classification, deducing them to be inductive defects. This offers a potent algorithm and valid criteria for diagnosing such incipient faults in the maintenance of cable transmission and distribution lines.
何光华, 金琰, 冯尧, 齐金龙, 赵嘉豪. 基于反射频谱Z变换的电缆瞬时性电穿通道诊断[J]. 电工技术学报, 2025, 40(17): 5615-5625.
He Guanghua, Jin Yan, Feng Yao, Qi Jinlong, Zhao Jiahao. Diagnosis of Cable Transient Electric Arc Breakdown Channel Based on Z-Transform of Reflection Coefficient Spectrum. Transactions of China Electrotechnical Society, 2025, 40(17): 5615-5625.
[1] 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. [2] 王昊月, 王晓威, 孙茂伦, 等. XLPE电缆绝缘热老化的高压频域介电谱诊断方法[J]. 电工技术学报, 2022, 37(17): 4497-4507. Wang Haoyue, Wang Xiaowei, Sun Maolun, et al.High voltage frequency domain dielectric spectroscopy diagnosis method for thermal aging of XPLE cables[J]. Transactions of China Electrotechnical Society, 2022, 37(17): 4497-4507. [3] 罗潘, 任志刚, 徐阳, 等. 退役高压交联聚乙烯电缆绝缘老化状态分析[J]. 电工技术学报, 2013, 28(10): 41-46. Luo Pan, Ren Zhigang, Xu Yang, et al.Aging condition analysis of high voltage XLPE cables out of service[J]. Transactions of China Electrotechnical Society, 2013, 28(10): 41-46. [4] 吴春芳, 陈云, 黄韬, 等. 电力电缆状态感知与检测技术研究综述[J]. 高压电器, 2024, 60(10): 86-103. Wu Chunfang, Chen Yun, Huang Tao, et al.Overview of condition sensing and detection technology for power cables[J]. High Voltage Apparatus, 2024, 60(10): 86-103. [5] 任广振, 王云鹤, 曹俊平, 等. 电力电缆初期绝缘故障检测方法综述[J]. 重庆大学学报, 2023, 46(11): 1-12. Ren Guangzhen, Wang Yunhe, Cao Junping, et al.Review of incipient insulation fault detection methods for power cables[J]. Journal of Chongqing University, 2023, 46(11): 1-12. [6] 李露露, 李永培, 周新月, 等. 10kV交联聚乙烯电缆内部多形态间歇性电弧故障建模[J]. 电工技术学报, 2022, 37(23): 6104-6115. Li Lulu, Li Yongpei, Zhou Xinyue, et al.Modeling of internal multiform intermittent arc fault for 10kV XLPE cable[J]. Transactions of China Electro-technical Society, 2022, 37(23): 6104-6115. [7] Rowland S, Wang Miao.Fault development in wet, low voltage, oil-impregnated paper insulated cables[J]. IEEE Transactions on Dielectrics and Electrical Insu-lation, 2008, 15(2): 484-491. [8] 瞿科, 张文海, 肖先勇, 等. 基于波形衰减因子的电缆接头绝缘早期故障状态监测[J]. 高电压技术, 2022, 48(1): 318-327. Qu Ke, Zhang Wenhai, Xiao Xianyong, et al.Condition monitoring for incipient faults of cable joint insulation based on waveform attenuation factor[J]. High Voltage Engineering, 2022, 48(1): 318-327. [9] Izadi M, Mohsenian-Rad H.Synchronous waveform measurements to locate transient events and incipient faults in power distribution networks[J]. IEEE Transactions on Smart Grid, 2021, 12(5): 4295-4307. [10] Kojovic L A, Williams C W.Sub-cycle detection of incipient cable splice faults to prevent cable damage[C]//2000 Power Engineering Society Summer Meeting, Seattle, WA, USA, 2000: 1175-1180. [11] 区伟潮, 葛阳, 朱延廷, 等. 基于双端原理的中压电缆潜伏性故障定位方法[J]. 山东大学学报 (工学版), 2022, 52(5): 84-91. Ou Weichao, Ge Yang, Zhu Yanting, et al.Method for locating latent faults of medium voltage cables based on double-ended principle[J]. Journal of Shandong University (Engineering Science), 2022, 52(5): 84-91. [12] CIGRE TF D1.02.05. Practical aspects of the dete-ction and location of PD in power cables[R]. Paris: CIGRE, 2006. [13] 叶源, 胡晓. 计及双界面的电缆绝缘水树缺陷时域反射解析模型[J]. 电工技术学报, 2024, 39(1): 55-64. Ye Yuan, Hu Xiao.A dual interface analytical model for time-domain reflectometry of water tree defects in power cables[J]. Transactions of China Electro-technical Society, 2024, 39(1): 55-64. [14] 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. [15] 梁栋, 徐丙垠, 谢伟, 等. 基于小波脉冲相关的电缆故障时域反射测距方法[J]. 中国电机工程学报, 2020, 40(24): 8050-8057, 8241. Liang Dong, Xu Bingyin, Xie Wei, et al.A time-domain-reflectometry cable fault location method using wavelet pulses and cross-correlation[J]. Pro-ceedings of the CSEE, 2020, 40(24): 8050-8057, 8241. [16] Toman G J, Fantoni P F.Cable aging assessment and condition monitoring using line resonance analysis (LIRA)[C]//16th International Conference on Nuclear Engineering, Orlando, Florida, USA, 2009: 177-186. [17] Hirai N, Yamada T, Ohki Y.Comparison of broad-band impedance spectroscopy and time domain reflectometry for locating cable degradation[C]//2012 IEEE International Conference on Condition Monitoring and Diagnosis, Bali, Indonesia, 2012: 229-232. [18] 唐作鑫, 周凯, 徐叶飞, 等. 考虑衰减补偿的频域反射单元平均恒虚警率电缆缺陷识别方法[J]. 电工技术学报, 2024, 39(16): 5194-5205. Tang Zuoxin, Zhou Kai, Xu Yefei, et al.Frequency domain reflectometry cell average constant false alarm rate cable defect identification method considering attenuation compensation[J]. Transa-ctions of China Electrotechnical Society, 2024, 39(16): 5194-5205. [19] Fantoni P F.Condition monitoring of electrical cables using line resonance analysis (LIRA)[C]//17th Inter-national Conference on Nuclear Engineering, Brussels, Belgium, 2010: 171-178. [20] 任广振, 曹俊平, 邹星宇, 等. 基于宽频阻抗谱的电力电缆缓冲层缺陷定位研究[J]. 高压电器, 2025, 61(2): 111-119. Ren Guangzhen, Cao Junping, Zou Xingyu, et al.Research on power cable buffer layer defect localization based on broadband impedance spectro-scopy[J]. High Voltage Apparatus, 2025, 61(2): 111-119. [21] Ohki Y, Yamada T, Hirai N.Precise location of the excessive temperature points in polymer insulated cables[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2013, 20(6): 2099-2106. [22] Zhou Zhiqiang, Zhang Dandan, He Junjia, et al.Local degradation diagnosis for cable insulation based on broadband impedance spectroscopy[J]. IEEE Transac-tions on Dielectrics and Electrical Insulation, 2015, 22(4): 2097-2107. [23] Hu Yuxiao, Chen Lu, Liu Ying, et al.Principle and verification of an improved algorithm for cable fault location based on complex reflection coefficient spectrum[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2022, 30(1): 308-316. [24] Jin Yan, Hu Yuxiao, Shi Chaoqun, et al.An improved algorithm of cable fault diagnosis considering signal attenuation compensation[C]//2024 IEEE 5th Inter-national Conference on Dielectrics (ICD), Toulouse, France, 2024: 1-4. [25] 李蓉, 周凯, 万航, 等. 基于输入阻抗谱的电力电缆本体局部缺陷类型识别及定位[J]. 电工技术学报, 2021, 36(8): 1743-1751. Li Rong, Zhou Kai, Wan Hang, et al.Identification and location of local defects in power cable body based on input impedance spectroscopy[J]. Transa-ctions of China Electrotechnical Society, 2021, 36(8): 1743-1751. [26] 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. [27] 刘鹏程, 田立斌, 高磊, 等. 基于反射系数谱相关系数的电缆故障检测方法[J]. 绝缘材料, 2023, 56(5): 95-102. Liu Pengcheng, Tian Libin, Gao Lei, et al.Detection method of cable fault based on correlation coefficient of reflection coefficient spectrum[J]. Insulating Materials, 2023, 56(5): 95-102. [28] 饶显杰, 周凯, 谢敏, 等. 基于频域反射法的特征时域波形恢复技术[J]. 高电压技术, 2021, 47(4): 1420-1427. Rao Xianjie, Zhou Kai, Xie Min, et al.Recovery technique of characteristic time domain waveform based on frequency domain reflection method[J]. High Voltage Engineering, 2021, 47(4): 1420-1427. [29] 王尉军, 陈旻, 殷慧, 等. 基于反射系数谱Z变换的电缆缺陷定位方法[J]. 绝缘材料, 2024, 57(1): 109-115. Wang Weijun, Chen Min, Yin Hui, et al.Cable defect location method based on Z transformation of reflection coefficient spectrum[J]. Insulating Materials, 2024, 57(1): 109-115. [30] 贾楠, 李唐军, 孙剑, 等. 高非线性光纤正常色散区利用皮秒脉冲产生超连续谱的相干特性[J]. 物理学报, 2014, 63(8): 178-186. Jia Nan, Li Tangjun, Sun Jian, et al.Coherence properties of supercontinuum generated by a picosecond pulse in normal dispersion region of highly nonlinear fiber[J]. Acta Physica Sinica, 2014, 63(8): 178-186. [31] Li Qian, Jian Ziyun, Lu Wei, et al.Combination and compression of multiple optical pulses in nonlinear fibers with the exponentially decreasing dispersion[J]. IEEE Journal of Quantum Electronics, 2018, 54(2): 1-10. [32] 杨振峰, 杨振军, 张书敏. 超短双曲正割脉冲的两种脉冲宽度比较[J]. 光子学报, 2008, 37(9): 1722-1725. Yang Zhenfeng, Yang Zhenjun, Zhang Shumin.Comparison of two kinds of pulse width of ultrashort hyperbolic secant pulses[J]. Acta Photonica Sinica, 2008, 37(9): 1722-1725. [33] 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, 2213. 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, 2213. [34] 金琰, 胡钰骁, 石超群, 等. 基于反射系数的电缆故障定位及评估优化算法[J]. 高电压技术, 2025, 51(5): 2436-2446. Jin Yan, Hu Yuxiao, Shi Chaoqun, et al.Cable fault location and assessment optimization algorithm based on reflection coefficient[J]. High Voltage Engin-eering, 2025, 51(5): 2436-2446. [35] 陈璐, 李光茂, 杨森, 等. 频域响应法在电力电缆缺陷定位中的研究综述[J]. 南方电网技术, 2023, 17(10): 53-64. Chen Lu, Li Guangmao, Yang Sen, et al.Review of research on frequency domain response method and its application in defect location of power cables[J]. Southern Power System Technology, 2023, 17(10): 53-64.
2025, Vol. 40 
