The degradation of the insulation system in liquid-filled power transformers is a serious concern for electric power utilities. The insulation system’s ageing is accelerated by moisture, acids, oxidation products, and other decay particles (soluble and colloidal). The presence of these ageing by-products is detrimental to the insulation system and may further lead to premature ageing and serious consequences. The ageing mechanisms of oil-paper insulation are complex, highly interrelated, and strongly temperature-dependent. The operating temperature of the transformer insulating system has a direct relationship with the loading profile. The major aspect that is witnessed with the fluctuating temperatures is moisture migration and subsequent bubble evolution. In other words, gas bubbles evolve from the release of water vapor from the cellulosic insulation wrapped around the transformer windings. The models presented in the existing standards, such as the IEC Std. 60076-7:2018 and the IEEE Std. C57.91:2011, are mainly based on the insulation temperature, which acts as a key parameter. Several studies have investigated the moisture dynamics and bubbling phenomenon as a function of the water content in the paper and the state of the insulation system. Some studies have reported different prototypes for the estimation of the bubble inception temperatures under selected conditions. However, there are various attributes of the insulation system that are to be considered, especially when expanding the models for the alternative liquids. This paper reviews various evaluation models reported in the literature that help understand the bubbling phenomenon in transformer insulation. The discussions also keep us in the loop on the estimation of bubbling behavior in alternative dielectric liquids and key attributable factors for use in transformers. In addition, useful tutorial elements focusing on the bubbling issue in transformers as well as some critical analyses are addressed for future research on this topic.

The bubbling inception temperature (BIT) of insulating materials used in transformers is critical for their performance and lifespan. This study, which represents the first part of a two-part series, provides a comparative analysis of the BIT for kraft paper, thermally upgraded kraft paper (TUK), and aramid paper impregnated with mineral oil. A customized experimental setup was used to measure the BIT under controlled laboratory conditions. The uniqueness of the setup lies in its precise control of dynamic load conditions via an autotransformer, real-time bubble detection, continuous moisture in oil and temperature monitoring using sensors, the use of capacitive measurement to assess moisture content in paper, and the flexibility to test different oils and insulation materials. This combination enables accurate analysis of bubble formation in oil-paper insulation systems under realistic conditions. Results show that TUK paper has the highest BIT, followed by kraft and aramid papers. Additionally, the study introduces new empirical equations for predicting BIT based on water content for each paper type, notably filling a gap for aramid paper. These equations are valuable for practical engineering applications. The research underscores the importance of moisture control in determining BIT and suggests future studies focused on standardizing methodologies and exploring different dielectric fluids. The findings contribute to improving the design, maintenance, and reliability of transformer insulation systems. Part 2 of this study will further explore the long-term effects of thermal aging and alternative dielectric fluids on BIT.

During operation, due to an unforeseen circumstance, an in-service transformer could experience some faults, which could be of electrical or thermal origin. Transformers diagnostic and monitoring through Dissolved Gas Analysis (DGA) is an imperative way for sustainable and reliable power distribution. This method is used in the early detection of faults in liquid filled power systems. In this contribution, a novel approach integrating uncertainty into DGA for power transformers is proposed. By modifying traditional empirical methods like the Duval’s Triangle and Duval’s Pentagon to include uncertainty, this study demonstrates how these modifications affect fault diagnostics. The results indicate that incorporating gas chromatograph’s uncertainty enhances diagnostic accuracy for IEC, Rogers, and Doernenburg methods, improving effective fault detection. Additionally, artificial intelligence (AI) models such as Random Forest, MLP, and SVM, trained with probabilities derived from the modified methods, show significant improvements in diagnostic performance. These models demonstrated increased robustness against different levels of uncertainty, achieving 87.76% accuracy for the Random Forest model and 86.73% for the SVM model with a 30% uncertainty level on the IEC TC10 Database. Therefore, the incorporation of the Gas chromatograph’s uncertainty in DGA diagnostic methods appear to be a promising approach to support transformer monitoring and fault diagnostic both for transformer owners and the producer.

Bubbling Inception Temperature (BIT) is a critical metric that indicates the temperature at which gas bubbles form due to cellulose decomposition in a paper–oil insulation system. It serves as a key indicator of the thermal stability of transformer insulation, offering valuable insights into its performance under elevated temperatures. Building on findings from a companion study that examined the BIT of Kraft paper (KP), thermally upgraded Kraft paper (TUK), and aramid paper in mineral oil, this research expands the analysis to assess the impact of moisture, aging, and alternative dielectric fluids. Using the same customized experimental setup featuring precise dynamic load control, real-time bubble detection, and continuous monitoring of moisture and temperature, this study evaluates BIT across four distinct oil–paper aging stages: new (0 h) and 2 weeks, 4 weeks, and 6 weeks of accelerated thermal aging. This approach enables a comparative analysis of BIT in various paper–oil systems, focusing on both mineral oil and synthetic esters, as well as the influence of different moisture levels in the paper insulation. The results show that BIT decreases with aging, indicating reduced thermal stability. Furthermore, KP impregnated with synthetic ester exhibits a higher BIT than when impregnated with mineral oil, suggesting that synthetic esters may offer better resistance to bubble formation under thermal stress. Based on these results, empirical BIT models were developed as a function of degree of polymerization (DP) and water content in paper (WCP). This study further demonstrates how these models can be applied to quantify safety margins under emergency overloading conditions, providing a practical tool for operational decision-making in transformer thermal risk management.

The flow shop scheduling problem is traditionally studied under the assumption that the optimal solution is a permutation schedule. However, for many practical problems, this may not be the case. Since finding a flow shop schedule is significantly simpler with the permutation consideration restrection, it becomes interesting to know a priori at what extent relaxing this constraint could be beneficial. In this paper, we study the conditions under which this assumption holds, particularly in flow shops with missing operations. We focus our analysis on the makespan and total completion time. Preliminary results show that, in certain cases, non-permutation schedules can yield significantly better outcomes.