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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.

Ester-based dielectric fluids have gained widespread popularity for applications in high voltage apparatus. Synthetic and natural esters have been subjected to research for decades vis-à-vis mineral insulating oils around the world. Although many researchers favor the application of ester fluids, utilities are still uncertain and application of these alternatives remains a challenge. The intent of this survey is to present recent research progress and highlight the state of the art of key aspects that should be emphasized in future research. The contemporary research scenarios pertaining to the performance of ester fluids versus mineral oils, miscibility, and retrofilling of insulating fluids are discussed. In addition, pre-breakdown phenomena, usage of esters in on-load tap changers, environmental and fire resistance properties, and use of esters in cold climates are also discussed. Importantly, challenges and future aspects that should be investigated to improve the existing knowledge of ester dielectric fluids for applications in transformer technology are highlighted.