A New Cable Defect Diagnosis Method Based on Reflection Coefficient and Kernel Function Construction
Rao Xianjie1, Xu Zhonglin1, Liu Xiangyu1, Guan Huifang1, Zhou kai2
1. Chengdu Power Supply Company State Grid Sichuan Power Supply Company Chengdu 610041 China; 2. College of Electrical Engineering Sichuan University Chengdu 610065 China
Abstract：In recent years, as its excellent mechanical and electrical properties, cross-linked polyethylene (XLPE) power cable is widely used in the construction of the urban power system. With the increase in service time, various cable local defects often occur due to the hostile operating environment and poor product quality. The long-term development of the defect may cause a cable insulation failure, which seriously threatens the stability of the urban power transmission and distribution system. Therefore, it is of great significance to accurately diagnose cable local defects as early as possible. Recently, by using the broadband impedance spectrum (BIS) and reflection coefficient spectrum (RCS), a series of cable defect location methods based on frequency domain reflectometry has been proposed. However, the existing methods have some problems in engineering applications, such as low reliability of the positioning result and the inability to determine defect type. To solve the above problems, this paper presents a new cable defect diagnosis method based on RCS. By establishing the defect diagnosis function based on RCS and kernel function construction, it can accurately obtain the characteristics of the defect, such as location, type and severity. Firstly, based on distributed parameter model of the cable line, the RCS mathematical model of the cable line with the local defect is established. The RCS is closely related to the position and characteristic impedance of the local defect, so it can be used to determine the characteristics of the local defect. Then, according to the mathematical model of RCS, a reasonable kernel function is designed in this paper, and a new defect diagnosis function based on RCS is obtained. By the periodicity of the trigonometric function, the proposed defect diagnosis function can form a peak at the position of the defect. Finally, the peak can be used to determine the characteristics of the defect, such as location, type and severity. When the characteristic impedance of the defect is greater than that of normal cable, the corresponding peak is positive polarity. On the contrary, when the characteristic impedance of the defect is smaller than that of normal cable, the corresponding peak is negative polarity. In this paper, two classic cable defect models are established. In the defect model Ⅰ, there is series defect-resistance in the cable core or shielding layer to simulate the damage defect of the cable core or shielding layer, leading to the increase of characteristic impedance. In addition, the higher the defect-resistance value, the more serious the defect. In the defect model Ⅱ, there is parallel defect-resistance between the cable core and shielding layer to simulate the damage defect of cable insulation, leading to the decrease of characteristic impedance. At the same time, the lower the defect-resistance value, the more serious the defect. In the simulation process, four simulated 250 m 10 kV XLPE power cable samples are built, and all the defects are located at 200m. The types of No.1~4 simulated cable samples are the defect model Ⅰ, Ⅰ, Ⅱ and Ⅱ, respectively. While the defect-resistance values are 60 Ω, 120 Ω, 60 Ω and 120 Ω, respectively. Simulation results show: (1) The defect positioning errors in all simulated cable samples are 0.2m (0.1%), which proves that the proposed method can accurately locate the defect. (2) In the defect diagnosis functions of No.1, No.2 simulated cable samples, the peaks at the defect are positive polarity. However, for No.3, No.4 simulated cable samples, the peaks at the defect are negative polarity. As a result, the polarity of the peak can be used to determine the type of defect. (3) As the peak absolute values of No.1~No.4 simulated cable samples are 799.1, 1 112.8, 446.7 and 254.7, respectively. The peak absolute value of the No.2 simulated cable sample is greater than that of the 1# simulated cable sample. On the other hand, the peak absolute value of the No.4 simulated cable sample is smaller than that of the No.3 simulated cable sample. So the peak absolute value can be employed to assess the severity of the defect. In the measurement process, the experimental subjects are 40 m SYV50-5-1 communication coaxial cable and 105 m 10 kV XLPE power cable. The No.1~No.3 measured cable samples are the communication coaxial cable, while the No.4 measured cable sample is the XLPE power cable. As same as the simulation results, the proposed defect diagnosis function can accurately locate the defect, in which the maximum positioning error is only 0.7%. At the same time, the type and severity of the defect can be determined from the defect diagnosis function. The conclusions of this article are summarized as follows: (1) The peak of the defect diagnosis function can be used to obtain the location and type of the defect. For the peak of the defect diagnosis function, the abscissa value is the position of the defect, and the sign (positive or negative) of ordinate value is used to determine the type of defect, in which the positive sign and negative sign correspond to defects with increase and decrease of characteristic impedance, respectively. (2) The damage to the cable core or shielding layer causes the increase of the characteristic impedance at the defect, and the proposed defect diagnosis function forms a positive peak. The damage to the cable insulation causes the decrease of the characteristic impedance at the defect, and the proposed defect diagnosis function forms a negative peak. (3) In the proposed defect diagnosis function, the peak absolute value is related to the severity of the defect. The higher the absolute value, the more serious the defect. (4) The simulation and measurement results show that the proposed method can not only accurately locate the defect in the cable, but also determines the type and severity of the defect.
饶显杰, 徐忠林, 刘翔宇, 关惠方, 周凯. 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, Guan Huifang, Zhou kai. A New Cable Defect Diagnosis Method Based on Reflection Coefficient and Kernel Function Construction. Transactions of China Electrotechnical Society, 2024, 39(7): 2184-2192.
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2024, Vol. 39 
