Natural fiber reinforced starch polymers are processed by compression molding. Potato, sweet potato, and corn starch are used as matrices. Three types of natural fibers, namely sisal, jute, and cabuya, are used in concentrations varying from 2.5 to 12.5% w/w in the composites. Different plasticizers are used for the starch polymers, such as water and glycerol. Mechanical properties are assessed by tensile and impact tests. In both cases, improved mechanical properties are obtained at increasing fiber contents. Tensile strength appears to be markedly improved with the addition of 10% by weight of sisal fibers, while the best results for impact strength are obtained for cabuya fibers.

The mechanical performance of two commercial papers used as solid insulation in power transformers, namely standard Kraft and a thermally upgraded Kraft paper, were studied during accelerated aging in Luminol oil at 170 °C. The results show a relationship between the degree of polymerization and the mechanical properties measured by tensile testing. A linear relationship was found between the mechanical properties of paper, the tensile index (Tidx), and the concentration of methanol present in the oil. The methanol chemical marker has been proven to be an accurate assessment tool for the aging of cellulosic paper. The results show a promising tool for correlating the methanol concentration in oil, as an indirect indicator, with the mechanical performance of the paper. This approach can be used to monitor the real state of the cellulose chains in the power transformer insulation paper.

Thermally upgraded Kraft (TUK) "Insuldur®" papers prepared from the same batch process but with a different percentage of stabilizing additives (1.2, 2.6 and 4.4% of nitrogen) were studied under accelerated aging at 170 °C. The increment of stabilizing additives in paper with the consequent increment of nitrogen content slows down the rate of molecular chain scission of the cellulose and reduces the paper tensile strength loss rate. However, a progressive reduction in the enhancement of the paper life span by the increment of nitrogenous additives in paper was observed. The cellulose degradation methanol and ethanol markers were found to be reliable and accurate for tracking the aging of TUK papers even with the variation of nitrogen content in paper. A promising correlation between methanol concentration and the paper tensile index with different nitrogen content was determined. This correlation could be used as a basis for developing tools to assess power transformers residual life. FTIR-ATR analyses of dicyandiamide in aged papers showed a rapid reaction and decomposition of dicyandiamide in other compounds that slows down cellulose aging. The nitrogen content of aged paper, measured with the Dumas method, showed a slight variation during the study. This implies that the nitrogen compounds continue to be attached, presumably by polar forces to the paper. Thermogravimetric analysis (TGA) of new and aged Insuldur® paper samples showed that nitrogen additives improve the thermal stability of paper at high temperatures.

This manuscript presents a comprehensive literature review with the aim to provide readers a reference document with up-to-date information on the field of methanol use as a chemical marker. It has been a little more than a decade since methanol was first introduced as a marker for assessing solid insulation condition in power transformers. It all started when methanol was identified in the laboratory during thermal ageing tests carried out with oil-immersed insulating papers and was subsequently also identified in transformer field samples. The first publication on the subject was released in 2007 by our research group. This review covers the methanol fundamentals such as the analytical methods for its determination in transformer oil, which is generally performed by headspace gas chromatography with mass spectrometry or flame ionization as a detector. Current standardization efforts for its determination include ASTM working group 30948 and IEC TC10. Kinetic studies have confirmed the relationship between methanol generation, the number of broken 1,4-β-glycosidic bonds of cellulose and changes in mechanical properties. Laboratory tests have confirmed its stability at different accelerated ageing temperatures. Several utilities have identified methanol during field measurements, case studies on power and some distribution transformers are presented, as well as transformer postmortem investigations. These field-testing results demonstrate its utility in monitoring cellulosic insulation degradation. Recently, a model of methanol interpretation has become available that allows for evaluation of the average degree of polymerization of core type transformer cellulose winding. Methanol has a role as an indicator of cellulosic solid insulation ageing in transformer mineral oil, and it is expected that in the future it will be in routine use by utilities.

In the era of Industry 4.0, digital transformation has spurred the swift advancement of technologies, including intelligent predictive maintenance scheduling, prognostics and health management. The accurate prediction of remaining useful life plays a crucial role in these technologies as it extends power equipment’s safe operational duration and decreases the maintenance costs associated with unforeseen shutdowns. Also, the increased accessibility of data for monitoring system conditions has paved the way for the more immense adoption of machine learning models in prognostics and health management for power transformers. At the moment, with the ever-increasing demand for electricity, there is a corresponding increase in the degradation processes of power transformers. Transformers insulation system and more importantly, the paper insulation happens to be the principal part where the degradation is prominent. Therefore, an accurate prediction of the insulating paper condition through its degree of polymerization is required to guarantee the reliability of power transformers. In this regard, the predictions, reliability, and health monitoring of this power equipment can be actualized by modeling the degradation of transformer insulation paper through several machine learning frameworks. In this view, this review paper has been drafted not only to serve as a guide for researchers interested in the fields of transformer insulation system fault prognosis but also to offer insights into potential research directions as existing literature in modeling and evaluating transformer paper insulation is presented.