新型基于反射系数谱的电力电缆局部缺陷定位方法

发布时间：2018-02-28 03:50

本文关键词： 微弱缺陷反射系数谱精细频谱分析 Kaiser窗距离窗　出处：《电网技术》2017年09期 　论文类型：期刊论文

【摘要】：为解决传统时域反射法(time domain reflectometry,TDR)和宽频阻抗谱法(broadband impedance spectroscopy,BIS)对电缆微弱局部缺陷进行定位时存在的问题,提出一种新型基于反射系数谱的电力电缆局部缺陷精确定位方法。首先结合电缆分布参数模型及精细频谱分析、Kaiser窗、距离窗处理技术详细阐明了反射系数谱定位方法的基本原理;然后对不同损伤程度的电缆缺陷进行建模定位仿真,检验该方法的可行性与识别灵敏度;最后在实验室65.5 m长的10 kV交联聚乙烯(cross-linked polyethylene,XLPE)电力电缆上制作了微弱局部缺陷对该方法的准确性进行了验证。仿真和实验结果表明:该方法能对不同损伤程度的某些局部缺陷进行有效定位,即使电缆损伤程度较弱时,该方法也具有较高的识别灵敏度和识别精度,定位误差0.6%。与传统TDR相比,该方法对微弱缺陷的识别能力更强;与BIS法相比,该方法所需测试频率更低,所需测试点数更少。
[Abstract]:In order to solve the problems existing in the localization of weak local defects of cables by traditional time-domain reflection method (TDR) and broadband impedance spectroscopy (BIS). In this paper, a new method for accurate localization of local defects of power cables based on reflection coefficient spectrum is proposed. Firstly, the Kaiser window is analyzed by combining the cable distribution parameter model and fine spectrum analysis. The basic principle of the reflectance spectrum localization method is explained in detail by the distance window processing technology, and then the cable defects with different damage degree are modeled and simulated to verify the feasibility and the identification sensitivity of the method. Finally, the accuracy of the method is verified by making a weak local defect on the 65.5 m long cross-linked polyethylene cross-linked polyethylene (XLPE) power cable in laboratory. The simulation and experimental results show that the proposed method can be applied to some power cable with different damage degree. Some local defects are effectively located, Even when the cable damage degree is weak, the method has higher sensitivity and accuracy, and the positioning error is 0.60.Compared with the traditional TDR, the method has a stronger ability to identify weak defects, and compared with the BIS method, the proposed method has better ability to identify weak defects. This method requires lower test frequency and fewer test points.
【作者单位】：四川大学电气信息学院;
【基金】：中国博士后科学基金项目(2015T80976)~~
【分类号】：TM855

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