| Title |
Furfural-DP Correlation Analysis and Statistical Validation for Residual Life Prediction Based on Dismantling Transformer |
| Authors |
김아름(Ah-Reum Kim) ; 전태현(Tae-Hyun Jun) ; 곽병섭(Byeong-Sub Kwak) ; 김은영(Eun-Young Kim) ; 박현주(Hyun-Joo Park) |
| DOI |
https://doi.org/10.5370/KIEE.2025.74.12.2264 |
| Keywords |
Transformer; Insulating Paper; Furfural; Residual Life; Dismantling |
| Abstract |
The ageing of long-term operating transformers has become more severe, increasing the likelihood of equipment failure due to insulation degradation and posing a direct threat to the stability of the power system. Although the degree of polymerization of the insulation (DP) is a key indicator for evaluating the condition of transformer it is difficult to collect samples directly from operating transformers. Furfural generation and insulation degradation occur differently depending on the actual operating conditions(temperature, load, etc.), so it is not possible to apply a single consistent model to all utilities. Therefore, it is crucial to verify the reliability of the model using dismantling data. This study analyzed the relationship between furfural concentration and DP comprehensively, using actual measurement data from 30 dismantled transforemrs, and strictly verified the statistical validity of the prediction model using the bootstrapping technique. The analysis results showed a strong logarithmic relationship between furfural concentration and DP, with an R2 value of 0.88. The critical value of furfural concentration corresponding to the limit of the insulation paper life was calculated to be approximately 597.8 ppb. The 95% confidence interval for this predicted value is [444.8 ppb ~ 834.5 ppb], which proves the model’s reliability quantitatively. Additionally it was confirmed that a minimum number of 22 samples is required for stable model prediction. This study is expected to contribute to the development of data-based maintenance strategies that estimate the remaining lifespan of operating transformers and establish reasonable replacement priorities through empirical analysis. |