Kinetic Assessment of Mechanical Properties of a Cellulose Board Aged in Mineral Oil and Synthetic Ester

Kinetic Assessment of Kraft and Thermally Upgraded Kraft Papers Aged in Five Alternative Dielectric Fluids

The lifespan of an electrical transformer, primarily determined by the condition of its solid insulation, is well known under various operating conditions when mineral oil is the coolant in these machines. However, there is a trend toward replacing this oil with biodegradable fluids, especially esters; therefore, an understanding of the ageing of solid insulation with these fluids is essential. Currently available data do not allow for the selection of the best ester among those available on the market, as each study applies different conditions, making it impossible to compare results. Thus, this paper analyses the degradation of Kraft and Thermally Upgraded Kraft papers with the following five most promising commercial esters: sunflower, rapeseed, soybean, palm, and synthetic. The materials underwent accelerated thermal ageing at 130, 150, and 170 °C, and the integrity of the papers was evaluated through their polymerisation degree and the obtaining of the degradation kinetic models. The wide range of materials studied in this work, which were subjected to the same treatments, allows for a comparison of the esters, revealing significant differences in the impact of the alternative fluids. Sunflower, rapeseed, and soybean esters provided the best paper protection, i.e., the degree of polymerisation of Kraft paper in the tests at 150 °C decreased by 71% with these fluids, compared to the 83% reduction with mineral oil, 79% reduction with palm ester, and 75% reduction with synthetic ester. Furthermore, different kinetic models were obtained to predict the degradation; it was concluded that the Emsley model provides the best fit. Additionally, it was found that the behaviour of a dielectric fluid with one type of paper cannot be extrapolated, which is only noticeable in broad-scope studies.

Concentration Prediction of Polymer Insulation Aging Indicator-Alcohols in Oil Based on Genetic Algorithm-Optimized Support Vector Machines

The predictive model of aging indicator based on intelligent algorithms has become an auxiliary method for the aging condition of transformer polymer insulation. However, most of the current research on the concentration prediction of aging products focuses on dissolved gases in oil, and the concentration prediction of alcohols in oil is ignored. As new types of aging indicators, alcohols (methanol, ethanol) are becoming prevalent in the aging evaluation of transformer polymer insulation. To address this, this study proposes a prediction model for the concentration of alcohols based on a genetic-algorithm-optimized support vector machine (GA-SVM). Firstly, accelerated thermal aging experiments on oil-paper insulation are conducted, and the concentration of alcohols is measured. Then, the data of the past 4 days of aging are used as the input feature of SVM, and the GA algorithm is utilized to optimize the kernel function parameter and penalty factor of SVM. Moreover, the concentrations of methanol and ethanol are predicted, after which the prediction accuracy of other algorithms and GA-SVM are compared. Finally, an industrial software program for predicting the concentration of methanol and ethanol is established. The results show that the mean square errors (MSE) of methanol and ethanol concentration predictions of the model proposed in this paper are 0.008 and 0.003, respectively. The prediction model proposed in this paper can track changes in methanol and ethanol concentrations well, providing a theoretical basis for the field of alcohol concentration prediction in transformer oil.