Temperature Effect on Electrical Aging Model for Field-Aged Oil Impregnated Paper Insulation

Abstract

The time-to-failure for oil-impregnated paper (OIP) insulation is governed by two primary aging mechanisms: electrical and thermal. The electrical life can be represented as an Inverse Power Law, where lifetime is inversely proportional to applied electric field. The process of thermal aging on the other hand is established by Arrhenius Law, which relates the rate of aging exponentially to temperature. Due to thermal aging, the structure of insulation is altered owing to chemical changes like oxidation, polymerization, and cellulose degradation. For life estimation of a serviceaged high-pressure gas filled (HPGF) cables, electrical endurance tests are normally performed at controlled voltage levels to estimate the time to breakdown. However, it is equally necessary to investigate how thermal aging influence changes in the electrical life of insulation. Therefore, in this paper, firstly short-term ramped stress tests are carried out on elevated thermal aged OIP samples extracted from already field-aged HPGF to find a rough estimate of breakdown voltages at different temperatures. Then, long-term electro-thermal step stress tests are performed on the samples to establish a correlation of temperature on the electrical life of the OIP insulation. The long-term stress tests produce reliable breakdown statistics and Maximum Likelihood Estimation of Inverse Power Law fitted on 2parameter Weibull distributed breakdown data indicate a reduction of model parameter, n from 13.61 to 7.38 with an increase in temperature from 45 to 75 ℃ and a constant shape factor, β of 1.50. The dissipation factor, tanδ related to the aging also shows an increase with temperature across a wide frequency range and is inversely proportional to the breakdown voltage.

Thermal Aging-Based Degradation Parameters Determination for Grid-Aged Oil Paper Insulation

“…Long-term stress tests were performed on the aged samples which produced reliable breakdown statistics and the maximum likelihood estimation of inverse power law was fit on two-parameter Weibull distributed breakdown data. The modeling of the final log-likelihood function with complete and right censored data was computed to be as in (24) which is used to solve for model variables [29] = N c i=1 n i ln β K S n i K S n i t i β−1 e −(K S n i t i ) β − N r j =1 n j K S n j t j β (24) where N c is the number of samples constituting complete failure data set and N r represents the right censored data, β is the shape parameter from Weibull distribution, K is the constant of proportion in power law, S is the voltage stress, and n is the power in power law and t represents the time to failure. Among the variables, the exponent of power law showed a monotonic reduction with an increase in temperature, indicating a significant non-linear decrease in the electrical life of the insulation with an increase in temperature as observed quantitatively in Table IV.…”