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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.

The reliability of power transformers operation largely depends on the insulation system condition, especially on the condition of paper insulation. Determining the condition of paper insulation of transformers in use cannot be made by direct measurements. The most common way of assessing the paper insulation condition is by analyzing the content of 2-furfural dissolved in the insulating oil and by applying correlations between the degree of paper polymerization and the 2-furfural content in the insulating oil. The concentration of 2-furfural in the oil depends on the moisture content of the insulation system and the oxygen and acid content in the oil. In order to gain a better insight into the cumulative content of 2-furfural produced by paper degradation, excluding changes in the 2-furfural content caused by migration between paper and oil at different operating temperatures, dependence of the degree of polymerization of kraft paper to 2-FAL content in the oil, corrected to a reference temperature of 20°C, for a moisture content in cellulose of 1-1.5% and 3-4% is presented in this paper. This diagram has been verified on 4 transformers according to available data from the database of the Electrical Engineering Institute Nikola Tesla. In the future, this diagram should be completed for moisture content of 2.5% in cellulose insulation.

The paper presents the results of research and analysis of literature data on the paper insulation's thermal aging. An exponential decrease in the degree of polymerization of paper insulation from the aging time is established, at the starting time of transformers’ operation the aging rate of paper insulation is maximal and after that its slowing down. It was found out that the degree of polymerization at the starting time of the transformer's operation can be in the range of 900 units, this is due to its drying during the manufacturing process.

High-voltage DC (HVDC) cables show more efficiency than high-voltage AC cables over the long distance power transmission and have attracted significant attention recently due to rapid developments in off-shore wind farm. Polypropylene laminated paper (PPLP) insulation based HVDC cables have provided a reliable operation for many years and are the highest voltage level in operation at the moment. In this study, the authors intend to understand the electrical performance of PPLP insulation system by examining space charge in detail, especially at the interfaces. It has been found that the interface between polypropylene and Kraft paper acts as trap for charge carriers, resulting in an optimal electric field distribution in PPLP insulation system. The optimal electric field distribution enhances overall electrical performance of PPLP insulation, including higher dc breakdown strength and lower electrical conductivity.

This paper investigates thermal overloading, voltage dips and insulation failure across a distribution transformer (DT), under residential and battery electric vehicle (BEV) loadings. The objective of this paper is to discuss the charging impact of BEVs on voltage across consumer-service points, as well as across the life of paper insulation under varying ambient temperatures (during winter and summer), with and without a centralized battery energy storage system (BESS). This study contributes in two ways. The first part of this study deals with coordinated and uncoordinated BEV charging scenarios. The second part of this study deals with maximum utilization of a test DT rated under dynamic thermal rating (DRoDT). The DRoDT integration with BESS is carried out to flatten the load spikes, to obtain maximum DT utilization, to achieve active power and voltage supports in addition to an enhanced DT lifespan. The obtained results indicate that, when test DT operates under the proposed hybrid technique (combining both dynamic transformer ratings and a centralized BESS), it attains maximum utilization, lower hot-spot temperature, enhanced lifespan, less degraded paper insulation and an improved voltage across each consumer service point. The proposed technique is furthermore found effective in maintaining the loading across the distribution transformer within the nominal limits. However, under excess loading during peak hours, the proposed technique provides relief to the DT to a certain extent. To achieve an optimal DT operation and an enhanced BESS lifespan, the BESS is operated under nominal charging and discharging cyclic limits. Under the proposed DRoDT integration with BESS, DT attains 25.9% more life when loaded with coordinated BEV charging, in comparison to no BESS integration under the same loading scenario. The worst loading due to uncoordinated BEV charging also brings 51% increase in DT life when loaded under the proposed technique.

Careful monitoring of high voltage equipments and diagnosing the critical conditions before they lead to a disaster are prerequisites of condition-based maintenance. Moisture content in a transformer is regarded as one of the major factors in diagnosing its conditions. It causes many problems for a power transformer including electrical breakdown between either its windings or one winding with neutral, increase in the amount of partial discharge and sundry minor problems. Since paper insulation of a power transformer carries large portion of water content, determining moisture content in this part of the transformer is essential. However, the problem is that the direct measurement of moisture in paper is impossible. Therefore, various methods have been proposed to measure the moisture content in a transformer but each one has its limitations. In this study, an approach is introduced to measure water content in a transformer by analysing the moisture dynamics in oil, tracking its variations and analysis of parameters such as temperature, without necessity of disconnecting the transformer from the power grid.

In order to explore a non-destructive method for measuring the polymerization degree (DP) of paper insulation in transformer, a new method that based on the optical properties of free fiber particles in transformer oil was studied. The chromatic dispersion images of fibers with different aging degree were obtained by polarizing microscope, and the eigenvalues (r, b, and Mahalanobis distance) of the images were extracted by the RGB (red, blue, and green) tricolor analysis method. Then, the correlation between the three eigenvalues and DP of paper insulation were simulated respectively. The results showed that the color of images changed from blue-purple to orange-yellow gradually with the increase of aging degree. For the three eigenvalues, the relationship between Mahalanobis distance and DP had the best goodness of fit (R2 = 0.98), higher than that of r (0.94) and b (0.94). The mean square error of the relationship between Mahalanobis distance and DP (52.17) was also significantly lower than that of r and b (97.58, 98.05). Therefore, the DP of unknown paper insulation could be calculated by the simulated relationship of Mahalanobis distance and DP.

This article presents an algorithm for modelling an Adaptive Neuro Fuzzy Inference System (ANFIS) for power transformer paper conditions in order to estimate the transformer’s expected life. The dielectric characteristics, dissolved gasses, and furfural of 108 running transformers were collected, which were divided into 76 training datasets and another 32 testing datasets. The degree of polymerization (DP) of the transformer paper was predicted using the ANFIS model based on using the dielectric characteristics and dissolved gases as input. These inputs were analyzed, and the best combination was selected, whereas CO + CO2, acidity, interfacial tension, and color were correlated with the paper’s deterioration condition and were chosen as the input variables. The best combination of input variables and membership function was selected to build the optimal ANFIS model, which was then compared and evaluated. The proposed ANFIS model has 89.07% training accuracy and 85.75% testing accuracy and was applied to a transformer paper insulation assessment and an estimation of the expected life of four Indonesian transformers for which furfural data is unavailable. This proposed algorithm can be used as a furfural alternative for the general assessment of transformer paper conditions and the estimation of expected life and provides a helpful assistance for experts in transformer condition assessment.