153 / 2022-03-14 10:52:09

Assessment for Aging State of High-voltage Cable Insulation Based on Fuzzy Clustering Method

XLPE, Cable insulation, State evaluation, Fuzzy clustering method

Purpose/Aim

XLPE is widely used in high voltage cables based on its excellent electrical, physical, and chemical properties, and aging is one of the important reasons for the degradation of XLPE insulation for cables in operation. With the increase of service time of XLPE cables invested in the early stage, it is of great significance to establish an effective aging state assessment model to evaluate the insulation state of high-voltage cables.

Experimental/Modeling methods

Firstly, the XLPE insulation of operating cables with the service time ranging from 0 year to 25 years are sampled and pre-treated for characterizing, and then the relevant experiments closely related to the insulation aging characteristics such as thermal elongation test, AC breakdown test, Fourier Transform Infrared Spectrum and broadband dielectric spectrum of XLPE samples are carried out. After the experiments, the correlation analysis of the obtained insulation influence factors is carried out based on the Pearson Correlation Coefficient. Finally, five strongly related characteristic parameters such as elongation at break, AC breakdown field strength, carbonyl index, melting temperature and dielectric loss are selected as the indexes to evaluate the aging state of cable insulation. At last, the obtained characteristic parameters are further processed based on the fuzzy clustering method, the clustering center is calculated, the membership function is established, and the evaluation model of insulation aging is obtained.

Results/discussion

The aging state of the cables is evaluated through the assessment model, and it is divided into six aging levels according to the insulation aging degree: slight aging, mild aging, moderate aging, severe aging (level: l), severe aging (level: ll) and severe aging (level: III). Corresponding treatments schemes can be formulated according to different relevant grades.

Conclusions

XLPE insulation of operating cables are characterized by electrical, mechanical and physicochemical testing, and the correlation analysis is carried out based on the experimental data. Five influencing variables are selected for analysis based on the fuzzy cluster analysis method, and a correlation model for evaluating the aging state of cable insulation is established.