A New Technique to Estimate the Degree of Polymerization of Insulation Paper Using Multiple Aging Parameters of Transformer Oil

Abstract

Transformer insulation paper is a key indicator for transformer remaining operational life. Paper decomposition is evaluated using the degree of polymerization (DP) which calls for samples of insulation paper from operating transformers. Since collecting such paper samples is extremely difficult, other indicators have been used to indirectly reveal the DP value of insulation paper. This includes dissolved gases in transformer oil such as carbon oxides and hydrocarbon gases, furan compounds, methanol, ethanol, and moisture along with some oil characteristics such as interfacial tension. However, for the same oil sample, these individual parameters lead to different DP values. This is attributed to the lack of accuracy of the established mathematical and artificial intelligence models correlating DP with each of the above mentioned individual parameters. This paper presents a self-learning method to estimate the DP value of transformer insulation paper based on multiple transformer oil aging parameters. The proposed method comprises data processing, fuzzy c-means and linear regression. Results reveal that estimating the DP value based on multiple aging parameters is more accurate than estimating it using one single parameter as per the current practice. The proposed method not only helps to understand the correlation between multiple oil aging parameters and the DP value of paper insulation, but also promotes the establishment of more accurate life assessment models for power transformers based on these oil aging parameters.INDEX TERMS Degree of polymerization, oil aging parameters, paper insulation, power transformers.

The Degree of Polymerization in a Prediction Model of Insulating Paper and the Remaining Life of Power Transformers

“…It is an indicator about the strength between the relationship of two or more variables. It can be computed as follows [20]:…”

Determination of Transformers’ Insulating Paper State Based on Classification Techniques

“…The power transformer is one of the most important assets in a power system. Malfunctions often occur in a transformer insulating system due to electrical and thermal stresses, and then electrical utilities incur tremendous losses due to the switching off operation of the transformer to repair its faults [1][2][3][4]. The health index (HI) is an indicator to the transformer condition.…”

“…Attributing the value of the DP and then the insulating paper state to a single indicator can lead to inconsistent results for the same oil-paper sample due to the poor accuracy of the mathematical model to correlate the individual indicator with the DP value. Most research has utilized the 2-FAL as an indicator of the DP value, but each model can provide different results because a single indicator fails to provide an accurate value of the DP [4]. Therefore, in this study, classification techniques were used to construct a prediction model for the insulating paper degradation rate.…”

Effect of moisture content on the accuracy and economic feasibility of oil–paper insulation prediction model

“…However, transformer oil performance could be recovered through dehydration and replacement, while the cellulose degradation could lead to irreversible mechanical strength reduction. Therefore, being aware of the status of cellulose paper and pressboard in transformers approaching the end of their lifetime is a crucial issue for the power grid [5, 6 ]. Degree of polymerisation (DP) is always chosen as the indicator to reflect the overall status of cellulose paper due to its linear relationship with the tensile strength of cellulose paper and pressboard.…”