计及双界面的电缆绝缘水树缺陷时域反射解析模型

doi:10.19595/j.cnki.1000-6753.tces.230994

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (1547 KB) HTML
输出: BibTeX | EndNote (RIS)

摘要配电网中中压电缆的敷设条件相对较差,在环境水分和工作电压等因素的共同作用下容易在绝缘中生成水树缺陷,影响电缆绝缘的性能。时域反射法（TDR）可用于检测电缆故障,近来也有研究将其用于检测电缆绝缘局部缺陷,但检测结果与绝缘缺陷情况的关联尚不明晰。针对此问题,该文基于等效电路原理推导了同轴绝缘含水树缺陷时的等效介电常数,在此基础上结合传输线理论建立计及双界面反射的时域解析模型,可用于分析与预测电缆绝缘局部存在水树缺陷时TDR实验的反射波形。通过制作含模拟水树的电缆试样并进行TDR实验,考察了模拟水树缺陷的含水量、缺陷段长度和电导率等因素对TDR实验结果的影响。结果显示,模拟水树的TDR实验结果与解析模型的计算结果具有较好的对应性,初步验证了该模型用于分析与预测电缆绝缘局部缺陷反射特性的有效性。预期该解析模型有助于为TDR在电缆绝缘水树检测中的应用提供参考。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	叶源
	胡晓

关键词 ：电力电缆, 绝缘老化, 水树, 有限元, 时域反射, 双界面

Abstract：Due to the poor operating environment, medium voltage cables in distribution networks are prone to water tree aging under the combined effect of moisture and voltage, which can degrade the insulation performance. The time-domain reflectometry (TDR) method can be used to detect cable faults, and some recent studies have used it to detect the aging of the local insulation of cables. However, the relationship between the detection results and the aging condition of the insulation is not clear. To address this problem, an equivalent permittivity for the coaxial insulation with water tree defects is derived. Moreover, a time-domain analytical model considering the reflection of dual interfaces is developed to analyze and predict the results of TDR experiments on cables with local water tree defects.
First, the equivalent permittivity of the coaxial insulation with water trees was derived based on circuit theory, and the effectiveness of using the equivalent permittivity to characterize the reflection characteristics of water tree defects in cable insulation was verified through finite element simulation. Then, a time-domain dual-interface reflection model considering the interfaces at both ends of the defect was established based on transmission line theory and the equivalent permittivity. This model was used to analyze the relationship between the reflection of water tree defects and the condition of water treeing as well as the characteristics of the input signal (such as amplitude and waveform). Finally, by making cable samples with mimic water tree defects and conducting TDR tests, the effects of factors such as water content, length, and conductivity of the defects on TDR test results were investigated. The results showed that the TDR test results of the mimic water tree defects agreed well with the calculated results of the proposed analytical model, preliminarily validating the effectiveness of the proposed model for analyzing and predicting the reflection characteristics of local water tree defects in cable insulation.
The following conclusions can be drawn from this study: (1) The equivalent permittivity can characterize the wave reflection characteristics of local defects in cable insulation. The proposed analytical model considering multiple reflections at both ends of the defect can more accurately predict the reflected waveform generated by local insulation defects. (2) The TDR experiments on the mimic water tree defects showed that the reflection magnitude and waveform were closely related to the water content (quantity of the mimic water trees) and length of the defective section. For example, in this experiment, adding 5 small holes to the defective section increased the amplitude of the reflected wave to 2.6 times the original value, while doubling the length of the defective section increased the amplitude of the reflected wave to 1.6 times the original value. This preliminary result indicates that TDR measurements can provide a basis for assessing the extent and range of similar insulation defects (such as localized moisture and water treeing). The analytical model established in this study helps to link the water treeing condition of cable insulation with TDR test results, and it is expected to provide a reference for the application of TDR testing in detecting water treeing in cable insulation.

Key words： Power cable insulation aging water tree finite element (FEM) time domain reflectometry (TDR) dual interfaces

收稿日期: 2023-06-27

PACS:

TM247

基金资助:浙江天际互感器股份有限公司企业博士后科研项目资助（243930）

通讯作者: 胡晓男,1985年生,讲师,博士,研究方向为电力设备绝缘高频测试与诊断。E-mail：xhu3@gzu.edu.cn

作者简介: 叶源男,1997年生,硕士研究生,研究方向为电缆绝缘缺陷检测。E-mail：yu.ye97@foxmail.com

引用本文:

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. Transactions of China Electrotechnical Society, 2024, 39(1): 55-64.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.230994 https://dgjsxb.ces-transaction.com/CN/Y2024/V39/I1/55

[1] Shu Wen, Guo Jun, Boggs S A.Water treeing in low voltage cables[J]. IEEE Electrical Insulation Magazine, 2013, 29(2): 63-68.
[2] 吴明祥, 欧阳本红, 李文杰. 交联电缆常见故障及原因分析[J]. 中国电力, 2013, 46(5): 66-70.
Wu Mingxiang, Ouyang Benhong, Li Wenjie.Common faults and cause analysis of XLPE cables[J]. Electric Power, 2013, 46(5): 66-70.
[3] Ross R, Smit J J.Composition and growth of water trees in XLPE[J]. IEEE Transactions on Electrical Insulation, 1992, 27(3): 519-531.
[4] Burkes K M.Water tree analysis and on-line detection algorithm using time domain relectometry[D]. Clemson: Clemson University, 2014.
[5] 周凯, 陈泽龙, 尹游, 等. XLPE电缆水树老化及其诊断技术的研究进展[J]. 绝缘材料, 2019, 52(2): 7-14.
Zhou Kai, Chen Zelong, Yin You, et al.Research progress in water tree ageing of XLPE cables and its diagnosis technologies[J]. Insulating Materials, 2019, 52(2): 7-14.
[6] 王昊月, 李成榕, 王伟, 等. 高压频域介电谱诊断XLPE电缆局部绝缘老化缺陷的研究[J]. 电工技术学报, 2022, 37(6): 1542-1553.
Wang Haoyue, Li Chengrong, Wang Wei, et al.Local aging diagnosis of XLPE cables using high voltage frequency domain dielectric spectroscopy[J]. Transactions of China Electrotechnical Society, 2022, 37(6): 1542-1553.
[7] 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.
[8] Suzuki K, Tanaka Y, Takada T, et al.Correlation between space charge distribution and water-tree location in aged XLPE cable[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2001, 8(1): 78-81.
[9] 李陈, 雷勇, 周凯, 等. 极化去极化电流技术用于诊断XLPE电缆绝缘老化状态[J]. 电工电能新技术, 2014, 33(4): 32-35, 66.
Li Chen, Lei Yong, Zhou Kai, et al.Diagnosis of XLPE cable insulation using polarization and depolarization current measurements[J]. Advanced Technology of Electrical Engineering and Energy, 2014, 33(4): 32-35, 66.
[10] 周湶, 王鑫源, 欧阳希, 等. 基于反射系数谱的XLPE电缆水树缺陷定位方法[J]. 电工电能新技术, 2021, 40(7): 28-39.
Zhou Quan, Wang Xinyuan, Ouyang Xi, et al.Location of water tree in XLPE cable based on reflection coefficient spectrum[J]. Advanced Technology of Electrical Engineering and Energy, 2021, 40(7): 28-39.
[11] 饶显杰, 徐忠林, 陈勃, 等. 基于频域反射的电缆缺陷定位优化方法[J]. 电网技术, 2022, 46(9): 3681-3689.
Rao Xianjie, Xu Zhonglin, Chen Bo, et al.Cable defect location optimization based on frequency domain reflection[J]. Power System Technology, 2022, 46(9): 3681-3689.
[12] 赵洪山, 孙京杰, 许向东. 基于反射系数谱积分的电缆缺陷诊断方法[J]. 电网技术, 2022, 46(11): 4548-4556.
Zhao Hongshan, Sun Jingjie, Xu Xiangdong.Diagnosis of local defects in cables based on integral reflection coefficient spectrum[J]. Power System Technology, 2022, 46(11): 4548-4556.
[13] 范伟松, 厉冰. 基于行波信号的配网成盘电缆长度快速检测方法研究[J]. 高压电器, 2022, 58(9): 190-196.
Fan Weisong, Li Bing.Study on fast detection method for drum cable length of distribution network based on traveling wave signal[J]. High Voltage Apparatus, 2022, 58(9): 190-196.
[14] 陶宇航, 张熹, 宫祥龙. 10kV电缆故障测距及定位典型案例分析[J]. 电气技术, 2022, 23(2): 88-93.
Tao Yuhang, Zhang Xi, Gong Xianglong.Typical cases analysis of 10kV cable fault location[J]. Electrical Engineering, 2022, 23(2): 88-93.
[15] 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.
[16] Reyes V, Celeita D, Ramos G.A simulation study on locating water trees on single core XLPE underground cables using reflectometry diagnosis techniques[C]//2021 IEEE/IAS 57th Industrial and Commercial Power Systems Technical Conference (I&CPS), Las Vegas, NV, USA, 2021: 1-7.
[17] Burkes K W, Makram E B, Hadidi R.Water tree detection in underground cables using time domain reflectometry[J]. IEEE Power and Energy Technology Systems Journal, 2015, 2(2): 53-62.
[18] Balanis C A.Advanced engineering electromagnetics[M]. 2nd ed. Hoboken: John Wiley & Sons, 2012.
[19] Ross R.Inception and propagation mechanisms of water treeing[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 1998, 5(5): 660-680.
[20] Tozzi M, Cavallini A, Montanari G C, et al.PD detection in extruded power cables: an approximate propagation model[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2008, 15(3): 832-840.
[21] Md Thayoob Y H, Ariffin A M, Sulaiman S. Analysis of high frequency wave propagation characteristics in medium voltage XLPE cable model[C]//2010 International Conference on Computer Applications and Industrial Electronics, Kuala Lumpur, Malaysia, 2011: 665-670.
[22] 周凯, 黄科荣, 黄明, 等. 交联聚乙烯电缆绝缘中的水树生长特性[J]. 高电压技术, 2019, 45(10): 3207-3213.
Zhou Kai, Huang Kerong, Huang Ming, et al.Water tree growth characteristics in XLPE power cable insulation[J]. High Voltage Engineering, 2019, 45(10): 3207-3213.
[23] Hvidsten S, Ildstad E, Sletbak J, et al.Understanding water treeing mechanisms in the development of diagnostic test methods[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 1998, 5(5): 754-760.