Insulation Diagnosis of Service Aged XLPE Power Cables Using Statistical Analysis and Fuzzy Inference

Cables that have been in service for over 20 years in Shanghai, a city with abundant surface water, failed more frequently and induced different cable accidents. This necessitates researches on the insulation aging state of cables working in special circumstances. We performed multi-parameter tests with samples from about 300 cable lines in Shanghai. The tests included water tree investigation, tensile test, dielectric spectroscopy test, thermogravimetric analysis (TGA), fourier transform infrared spectroscopy (FTIR), and electrical aging test. Then, we carried out regression analysis between every two test parameters. Moreover, through two-sample t-Test and analysis of va- riance (ANOVA) of each test parameter, we analyzed the influences of cable-laying method and sampling section on the degradation of cable insulation respectively. Furthermore, the test parameters which have strong correlation in the regression analysis or significant differ- ences in the t-Test or ANOVA analysis were determined to be the ones identifying the XLPE cable insulation aging state. The thresholds for distinguishing insulation aging states had been also obtained with the aid of statistical analysis and fuzzy clustering. Based on the fuzzy in- ference, we established a cable insulation aging diagnosis model using the intensity transfer method. The results of regression analysis indicate that the degradation of cable insulation accelerates as the degree of in-service aging increases. This validates the rule that the in- crease of microscopic imperfections in solid material enhances the dielectric breakdown strength. The results of the two-sample t-Test and the ANOVA indicate that the direct-buried cables are more sensitive to insulation degradation than duct cables. This confirms that the tensile strength and breakdown strength are reliable functional parameters in cable insulation evaluations. A case study further indicates that the proposed diagnosis model based on the fuzzy inference can reflect the comprehensive aging state of cable insulation well, and that the cable service time has no correlation with the insulation aging state.

Cables that have been in service for over 20 years in Shanghai, a city with abundant surface water, failed more frequently and induced different cable accidents. This necessitates researches on the insulation aging state of cables working in special circumstances. We performed multi-parameter tests with samples from about 300 cable lines in Shanghai. The tests included water tree investigation, tensile test, dielectric spectroscopy test, thermogravimetric analysis （TGA）, fourier transform infrared spectroscopy （FTIR）, and electrical aging test. Then, we carried out regression analysis between every two test parameters. Moreover, through two-sample t-Test and analysis of va- riance （ANOVA） of each test parameter, we analyzed the influences of cable-laying method and sampling section on the degradation of cable insulation respectively. Furthermore, the test parameters which have strong correlation in the regression analysis or significant differ- ences in the t-Test or ANOVA analysis were determined to be the ones identifying the XLPE cable insulation aging state. The thresholds for distinguishing insulation aging states had been also obtained with the aid of statistical analysis and fuzzy clustering. Based on the fuzzy in- ference, we established a cable insulation aging diagnosis model using the intensity transfer method. The results of regression analysis indicate that the degradation of cable insulation accelerates as the degree of in-service aging increases. This validates the rule that the in- crease of microscopic imperfections in solid material enhances the dielectric breakdown strength. The results of the two-sample t-Test and the ANOVA indicate that the direct-buffed cables are more sensitive to insulation degradation than duct cables. This confirms that the tensile strength and breakdown strength are reliable functional parameters in cable insulation evaluations. A case study further indicates that the proposed diagnosis model based on the fuzzy inference can reflect the comprehensive aging state of cable insulation well, and that the cable service time has no correlation with the insulation aging state.