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Greenhouse gas CO2 has rarely been used as an insulating gas in cellulose/oil insulated energy devices since the benefits of it on dielectric properties of cellulose/oil have yet to be known. This work reveals that CO2 can be adsorbed by unaged and aged cellulose, improving the breakdown strength of cellulose/oil composite. The traps introduced by CO2 adsorption are proved to be the key factor. The adsorption of CO2 makes shallow traps shallower, while making deep traps deeper. These changes in traps hinder the charge accumulation on dielectric surfaces and enhance the electron capture ability in the dielectric bulk. Moreover, the charge mobility and electrical conductivity of cellulose/oil composite increase after adsorbing CO2, while the surface potential is reduced. Therefore, the collisional ionization is hindered and further electrical breakdown is improved. In the meantime, the energy level distribution models of cellulose–air and cellulose–CO2 composites were comparably studied. This work is conducive to understanding the changes in electrical properties after CO2 adsorption and the prospects of CO2 in cellulose/oil insulation. [ABSTRACT FROM AUTHOR]

In order to detect the oil leakage problem of transformers in time and avoid the abnormal operation of power system caused by transformer oil leakage, this paper proposed a transformer oil leakage detection method based on improved YOLOv5. Firstly, by adding 4 × sampling layer to the FPN structure, the feature information was fused across layers to improve the detection accuracy of the model. Secondly, the Wise-IoU (WIoU) bounding loss function with dynamic non-monotony focusing mechanism was introduced to accelerate the training and reasoning of the network, and the overall performance of the model was further improved by balancing the learning of low and high-quality samples. Finally, the model was improved from Anchor-based to Anchor-free, which greatly reduced the number of parameters and calculation amount of the model, and had better performance than the original model. Used the self-built outdoor transformer oil leakage data set for training and testing, the results showed that compared with the original model, the improved network precision was increased by 7.3%, the recall was increased by 5.0%, the mAP0.5 was increased by 6.4%, the number of parameters was decreased by 28.2%, and the reasoning speed increased by 51.7%, which was beneficial to engineering deployment. [ABSTRACT FROM AUTHOR]

Transformer oil plays a critical role in ensuring the insulation and normal operation of power transformers. However, during operation, the transformer oil would undergo oxidation and degradation, leading to the formation of acids and polar compounds that increase its acidity and degrade its performance. Hence, the recycling and regeneration of aged transformer oil is an essential practice in transformer maintenance, contributing significantly to the extension of a transformer's operational lifespan and the reduction of potential risks. Adsorption separation is a preferred method for regeneration, offering simplicity and high efficiency. This study focuses on impregnated γ‐Al2O3 as an adsorbent for acidity removal in waste transformer oil. We conducted static adsorption experiments to investigate the optimal loading amount of the active component and the best calcination temperature for acid removal. XRD technique is employed to monitor the phase changes of the carrier during the calcination process. BET analysis is used to observe variations in the specific surface area and pore volume of the adsorbent during the modification process. DTG‐TGA is utilized to assess the weight loss during the calcination process. FTIR is employed to observe changes in functional groups during modification and adsorption processes. Along with these investigations, a study is also carried out to assess the dynamic acid removal performance of the acid‐removing adsorbent loaded into a fixed bed. The findings contribute to the development of sustainable methods for regenerating transformer oil, ensuring efficient and environmentally friendly operations. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis (DGA) in transformer oil, which analyzes its gas content, is valuable for promptly detecting potential faults in oil-immersed transformers. Given the limitations of traditional transformer fault diagnostic methods, such as insufficient gas characteristic components and a high misjudgment rate for transformer faults, this study proposes a transformer fault diagnosis model based on multi-scale approximate entropy and optimized convolutional neural networks (CNNs). This study introduces an improved sparrow search algorithm (ISSA) for optimizing CNN parameters, establishing the ISSA-CNN transformer fault diagnosis model. The dissolved gas components in the transformer oil are analyzed, and the multi-scale approximate entropy of the gas content under different fault modes is calculated. The computed entropy values are then used as feature parameters for the ISSA-CNN model to derive diagnostic results. Experimental data analysis demonstrates that multi-scale approximate entropy effectively characterizes the dissolved gas components in the transformer oil, significantly improving the diagnostic efficiency. Comparative analysis with BPNN, ELM, and CNNs validates the effectiveness and superiority of the proposed ISSA-CNN diagnostic model across various evaluation metrics. [ABSTRACT FROM AUTHOR]

Because large oil-immersed transformers are enclosed by a metal shell, the on-site localization means it is difficult to achieve the accurate location of the patrol micro-robot inside a given transformer. To address this issue, a spatial ultrasonic localization method based on wavelet decomposition and PHAT-β-γ generalized cross correlation is proposed in this paper. The method is carried out with a five-element stereo ultrasonic array for the location of a transformer patrol robot. Firstly, the localization signal is decomposed into wavelet coefficients of different scales, which would realize the adaptive decomposition of the frequency of the localization signal from low frequencies to high frequencies. Then, the wavelet coefficients are denoised and reconstructed by using the semi-soft threshold function. Second, a modified phase transform-beta-gamma (PHAT-β-γ) method is used to calculate the exact time delay between different sensors by increasing the weights of the PHAT weighting function and introducing a correlation function. Finally, by using the proposed method, the accurate localization of the transformer patrol micro-robot is achieved with a five-element stereo ultrasonic array. The simulation and test results show that inside a transformer experimental oil tank (120 cm × 100 cm × 100 cm, L × W × H), the relative error of transformer patrol micro-robot spatial localization is within 4.1%, and the maximum localization error is less than 3 cm, which meets the requirement of engineering localization. [ABSTRACT FROM AUTHOR]

The analysis of gases dissolved in oil is the main technique used to predict possible failures in power transformers. Currently, there are some assessment algorithms for the predictive analysis, but all of them have in common the fact that they are based on the classical gas formation theory. This work aims to analyze the use of decision trees (DTs) for the analysis of gases dissolved in mineral oil, also establishing a relationship associated with decision making during the formation of trees and the formation of gases. In this way, some important aspects involving the insulating mineral oil can be better understood and quantified. For this purpose, one dataset containing 201 samples is used in the study. The results provided by the tree are arranged in a Cartesian plane, and the gases are analyzed based on the ratios used by Doernenburg. DT is discussed in detail to validate the algorithm performance based on the classical gas formation theory. [ABSTRACT FROM AUTHOR]

Abstract This paper addressed the challenges associated with the complexity, numerous parameters, computational resource demands, and slow processing speed of transformer fault identification models based on deep learning technologies. Sparse convolutional neural network (CNN) approach is proposed for identifying faults related to dissolved gases in oil. Leveraging an improved Gramian angular field, one‐dimensional fault samples are converted into two‐dimensional feature images and data augmentation is implemented to meet the input requirements of deep learning models. Building upon visual geometry group (VGG)19 and residual networks (ResNet)50 networks for fault diagnosis, sparsity techniques are introduced through pruning, the fusion of convolution layers and batch normalization layers, and parameter quantization. Numerical experiments and performance evaluations on dissolved gas in transformer oil fault data demonstrate that the proposed method effectively reduced model complexity, minimized parameter count, conserved computational resources, and improved processing speed while maintaining a considerable level of fault identification accuracy. This made it applicable to edge computing platforms characterized by small form factors and low power consumption in the power industry.

Waste materials possess untapped potential when harnessed effectively. One such resource is waste transformer oil, derived from electrical transformers, which can be converted into a valuable fuel source for compression-ignition (CI) engines using pyrolysis techniques. In this study, we explored the utilization of a 50 % blend of waste transformer oil with diesel, augmented with Titanium dioxide nanoparticles (referred to as "nano fuel of waste transformer oil"), in an unmodified CI engine. To further enhance the performance of this nano fuel, we introduced hydrogen (at flow rates of 2.5 LPM, 5.0 LPM, and 7.5 LPM) into the inlet section during the suction phase, promoting premixing within the cylinder. Hydrogen premixing significantly improved combustion dynamics. Notably, when 7.5 LPM of hydrogen was introduced alongside waste transformer oil-derived nano fuel, a remarkable 37.30 % increase in brake thermal efficiency was achieved. Moreover, this configuration exhibited a 15.33 % reduction in brake-specific fuel consumption (BSFC), a 27.09 % decrease in carbon monoxide (CO) emissions, a 37.50 % decrease in unburned hydrocarbon (UHC) emissions, and a 28.53 % reduction in smoke opacity compared to traditional diesel operation. These results highlight the potential for enhanced CI engine performance and reduced emissions through the strategic combination of waste transformer oil-based nano-fuel and hydrogen premixing. Consequently, we recommend further exploration of hydrogen premixing in conjunction with nano-enhanced waste transformer oil for superior CI engine performance. [Display omitted] • Titanium dioxide nano-fuels from waste transformer oil biodiesel. • Hydrogen premixing experimented with the use of titanium dioxide nano-fuel. • Hydrogen induction optimized to maximize engine performance and minimize emissions. • Optimal induction of hydrogen 7.5 LPM recorded 37.30 % BTE and 15.33 % BSFC. • Compared with diesel the optimal hydrogen induction reduced CO by 27.09 %, UHC by 37.50 % and smoke opacity by 28.53 %. [ABSTRACT FROM AUTHOR]

Acetone is a reliable index to evaluate the aging state of oil paper insulation. It is of great significance to study a fast, highly sensitive and accurate method of detecting acetone in oil to ensure the safe and stable operation of oil-immersed transformers. In this paper, a method based on surface enhanced Raman spectroscopy for the detection of acetone in oil is investigated. Based on the mechanism of surface-enhanced Raman spectroscopy (SERS), a simulation model of surface-enhanced substrate was constructed using COMSOL software, and it was determined that the enhancement effect was better when silver nanowires/zinc oxide nanorods (Ag/ZnO) composite structure was selected. Ag/ZnO substrates were prepared by solvothermal method and experimentally tuned and characterized to obtain SERS substrates with high enhancement properties. The substrate coupled partial least square (PLS) model was used to establish a method for the detection of acetone in insulating oils, and its limit of quantification for acetone was 0.003 mg/g, which meets the requirements for engineering testing. [ABSTRACT FROM AUTHOR]

When an inter-turn short-circuit fault occurs during the operation of a transformer, the arc generates energy that causes the temperature in the tank to rise. This in turn increases the temperature of the insulating oil, vaporizing it, and the rising pressure in the tank acts on the tank such that it can easily explode. The arc energy is related to the initial pressure of the gas and its production in the tank. The pressure wave propagates in insulating oil, and the transient pressure at any point in the path of the pressure wave is the superposition of vectors of forward- and backward-traveling waves. The authors of this study applied a finite element simulation software to establish a model of the transformer tank and used it to analyze the changes in pressure in the tank and on the wall as well as the factors influencing this phenomenon. The results show that the wall pressure of the transformer increases with time after the failure of the interturn short circuit. The pressure wave travels from the initial position of the arc to the periphery and decreases with diffusion effects. The influence of pressure on the transformer tank can be reduced by selecting an appropriate location for the pressure-release valve and can in turn prevent the tank from rupturing due to the impact of rising pressure. [ABSTRACT FROM AUTHOR]

This paper addressed the challenges associated with the complexity, numerous parameters, computational resource demands, and slow processing speed of transformer fault identification models based on deep learning technologies. Sparse convolutional neural network (CNN) approach is proposed for identifying faults related to dissolved gases in oil. Leveraging an improved Gramian angular field, one‐dimensional fault samples are converted into two‐dimensional feature images and data augmentation is implemented to meet the input requirements of deep learning models. Building upon visual geometry group (VGG)19 and residual networks (ResNet)50 networks for fault diagnosis, sparsity techniques are introduced through pruning, the fusion of convolution layers and batch normalization layers, and parameter quantization. Numerical experiments and performance evaluations on dissolved gas in transformer oil fault data demonstrate that the proposed method effectively reduced model complexity, minimized parameter count, conserved computational resources, and improved processing speed while maintaining a considerable level of fault identification accuracy. This made it applicable to edge computing platforms characterized by small form factors and low power consumption in the power industry. [ABSTRACT FROM AUTHOR]

In this study, we examine the effects of various shapes of nanoparticles in a steady flow of hybrid nanofluids between two stretchable rotating disks. The steady flow of hybrid nanofluids with transformer oil as the base fluid and Fe3O4+TiO2 as the hybrid nanofluid is considered. Several shapes of Fe3O4+TiO2 hybrid nanofluids, including sphere, brick, blade, cylinder, and platelet, are studied. Every shape exists in the same volume of a nanoparticle. The leading equations (partial differential equations (PDEs)) are transformed to the nonlinear ordinary differential equations (ODEs) with the help of similarity transformations. The system of equations takes the form of ODEs depending on the boundary conditions, whose solutions are computed numerically by the bvp4c MATLAB solver. The outputs are compared with the previous findings, and an intriguing pattern is discovered, such that the tangential velocity is increased for the rotation parameter, while it is decreased by the stretching values because of the lower disk. For the reaction rate parameter, the concentration boundary layer becomes shorter, and the activation energy component increases the rate at which mass transfers come to the higher disk but have the opposite effect on the bottom disk. The ranges of various parameters taken into account are Pr = 6.2, Re = 2, M = 1.0, ϕ1= ϕ2 = 0.03, K = 0.5, S = −0.1, Br = 0.3, Sc = 2.0, α1= 0.2, γ = 0.1, En = 2.0, and q = 1.0, and the rotation factor K is within the range of 0 to 1. [ABSTRACT FROM AUTHOR]

Transformers are indispensable in the industry sector and society in general, as they play an important role in power distribution, allowing the delivery of electricity to different loads and locations. Because of their great importance, it is necessary that they have high reliability, so that their failure does not cause additional losses to the companies. Inside a transformer, the primary and secondary turns are insulated by oil. Analyzing oil samples, it is possible to diagnose the health status or type of fault in the transformer. This paper combines Fuzzy Logic and Neural Network techniques, with the main objective of detecting and if possible predicting failures, so that the maintenance technicians can make decisions and take action at the right time. The results showed an accuracy of up to 95% in detecting failures. This study also highlights the importance of predictive maintenance and provides a unique approach to support decision-making for maintenance technicians. [ABSTRACT FROM AUTHOR]

This work examines the application of machine learning (ML) algorithms to evaluate dissolved gas analysis (DGA) data to quickly identify incipient faults in oil-immersed transformers (OITs). Transformers are pivotal equipment in the transmission and distribution of electrical power. The failure of a particular unit during service may interrupt a massive number of consumers and disrupt commercial activities in that area. Therefore, several monitoring techniques are proposed to ensure that the unit maintains an adequate level of functionality in addition to an extended useful lifespan. DGA is a technique commonly employed for monitoring the state of OITs. The understanding of DGA samples is conversely unsatisfactory from the perspective of evaluating incipient faults and relies mainly on the proficiency of test engineers. In the current work, a multi-classification model that is centered on ML algorithms is demonstrated to have a logical, precise, and perfect understanding of DGA. The proposed model is used to analyze 138 transformer oil (TO) samples that exhibited different stray gassing characteristics in various South African substations. The proposed model combines the design of four ML classifiers and enhances diagnosis accuracy and trust between the transformer manufacturer and power utility. Furthermore, case reports on transformer failure analysis using the proposed model, IEC 60599:2022, and Eskom (Specification—Ref: 240-75661431) standards are presented. In addition, a comparison analysis is conducted in this work against the conventional DGA approaches to validate the proposed model. The proposed model demonstrates the highest degree of accuracy of 87.7%, which was produced by Bagged Trees, followed by Fine KNN with 86.2%, and the third in rank is Quadratic SVM with 84.1%. [ABSTRACT FROM AUTHOR]

The primary goal of this study is to evaluate the electrical characteristics of nano-particle-enhanced maize oil biodiesel for application in hybrid electric vehicles. The production of corn oil biodiesel begins with the implementation of the transesterification process, followed by the incorporation of varying compositions of CuO and TiO2 nano particles. Parameters such as breakdown voltage, permittivity, resistivity and electrical conductivity are used to evaluate the electrical properties of biodiesel blends. The resistivity value exhibits an upward trend as the diameter increases due to the dispersion of CuO along with TiO2 nano powder. Conversely, the breakdown voltage, permittivity and electrical conductivity experience a drop as the diameter increases. The mixing of CuO along with TiO2 nanoparticles has resulted in an overall improvement in electrical characteristics. [ABSTRACT FROM AUTHOR]

This study is a numerical analysis of the transition process from the second to the third mode in transformer oil. In this study, it was determined how to change from the second to the third mode, which is thought to be a precursor to the process of electrical breakdown, which results in a significant loss of electrical energy and harm to electrical devices and equipment. The initiation time, length, rate of propagation velocity, and radius of the streamer discharge were determined. The transition from the second to the third mode during the electrical discharge process may lead to the occurrence of an electrical breakdown, which is one of the greatest challenges facing scientists and engineers who deal with the electrical power of transmission and generation devices. The model was designed by AutoCAD software, which included two electrodes: a needle, with a tip radius of (4μm), and a plate, with a gap of (1 mm) between them. The gap between the two electrodes was filled with transformer oil. The equations, which are Poisson's equation and the continuity equations for positive and negative ions and electrons, which are generated by applying an applied voltage (from 27 kV to within 278 kV), had been solved by a simulation process. All the schematics and drawings were extracted by the Comsol Multiphysics program. [ABSTRACT FROM AUTHOR]

Aiming at the need of rapid and sensitive detection of acetylene dissolved in oil, a small‐volume T‐type photoacoustic (PA) cell was proposed. By optimizing the design parameters, the volume of the T‐type cell was reduced to 30 mL, which resulted in a significant reduction in the sampling amount of the transformer oil. A laser PA spectroscopy system for dissolved gas analysis was built combined with the headspace degassing method. A near‐infrared tunable laser with wavelength modulation spectroscopy was employed to detect the escaped acetylene gas from oil. Test results show that the detection limit of dissolved acetylene reaches 0.2 μL/L when the sampled oil volume is 50 mL. In addition, the degassing balance time can be shortened to 270 s. This scheme has the superiority of small oil extraction volume, high accuracy, and short response time, providing a new method for analyzing the dissolved characteristic gases in oil. [ABSTRACT FROM AUTHOR]

We have carried out the reactive force field simulations on the pyrolysis process of the transformer insulating oil at different temperatures from 2900 to 3500 K. The selected principal components of the oil molecules are alkanes (C15H32), cycloalkanes (C15H28), and aromatics (C15H16). The effects of formic acid (HCOOH) and acetic acid (CH3COOH) on the pyrolysis mechanism of insulating oils are also studied. The calculated results suggest that with the increasing temperatures, the cracking process of three types of oil molecules accelerates and their speeds of cracking decrease in this order: C15H32 > C15H28 > C15H16. The main pyrolysis products are C2H4, CH4, C2H2, ·CH3, ·C2H3, etc. For the mixture of three types of hydrocarbons, the C2H4 is initially the most abundant product but its concentration decreases gradually and finally the C2H2 and CH4 become the major products. The variation trend of the number of C‒C bonds indicates that the C‒C bonds of oil molecules (especially C15H32 molecules) are broken rapidly at the early stage of pyrolysis process to produce small molecular fragments and then these small fragments are recombined into new pyrolysis products. The evolution of the different types of carbon atoms (sp3-C, sp2-C, sp-C) shows that the sp3-C atoms are initially converted to sp2-C atoms. After the early stage of reactions, many sp2-C atoms are finally turned into sp-C atoms. The addition of formic acid and acetic acid in the insulating oils leads to the formation of the CO and H2O molecules. Both the acetic acid and formic acid could accelerate the pyrolysis process of oil molecules and could further increase the degree of unsaturation of products, which is consistent with the experimental observations. [ABSTRACT FROM AUTHOR]

This study investigates the impact of incorporating TiO2 nanoparticles into two types of oils at different temperatures and with varying volume fractions: transformer oil (NYTRO LIBRA) and virgin coconut oil (manufactured by Govi Aruna Pvt. Ltd., Gampaha, Sri Lanka). The nanofluids were prepared using a two-step method by adding CTAB (cetyltrimethylammonium bromide) surfactant. To minimize nanoparticle agglomeration, this study employed relatively low-volume fractions. Thermal properties by means of thermal conductivity, thermal diffusivity, and volumetric heat capacity were measured in accordance with ASTM (American Society for Testing and Materials) standard methods using a multifunctional thermal conductivity meter (LAMBDA thermal conductivity meter). The measured thermal conductivity values were compared with theoretical models and previous research findings. It was confirmed that the modification of thermal properties was enhanced by doping TiO2 nanoparticles with different volume fractions. [ABSTRACT FROM AUTHOR]

When the converter transformer core is grounded at multi-points, a fault loop will be formed and a circulation will be generated, leading to local overheating of the magnetic core and decomposition of insulating oil. Therefore, the converter transformer core will be single point grounded. As a rare internal lead of converter transformer, the ground current on the core ground lead can often reflect the running state of converter transformer. In this paper, theoretical analysis, modeling simulation and experimental research are carried out on the ground current of converter transformer at one point. Firstly, according to the structure characteristics of converter transformer, the analytical modeling of ground current is carried out. Secondly, based on the structure characteristics of oil paper insulation, the equivalent capacitance of converter transformer is calculated. Then, the analytical and finite element simulation model of converter transformer is established, and the ground current of converter transformer is calculated. Finally, the correctness of the proposed scheme is verified by measuring the grounding current of converter transformer core. [ABSTRACT FROM AUTHOR]

As the focus tilts toward online detection methodologies for transformer oil aging, bypassing challenges associated with traditional offline methods, such as sample contamination and misinterpretation, fiber optic sensors are gaining traction due to their compact nature, cost-effectiveness, and resilience to electromagnetic disturbances that are typical in high-voltage environments. This study delves into the sensitivity analysis of intensity-modulated plastic optical fiber sensors. The investigation encompasses key determinants such as the influence of optical source wavelengths, noise response dynamics, ramifications of varying sensing lengths, and repeatability assessments. Our findings highlight that elongating sensing length detrimentally affects both linearity response and repeatability, largely attributed to a diminished resistance to noise. Additionally, the choice of the optical source wavelength proved to be a critical variable in assessing sensor sensitivity. [ABSTRACT FROM AUTHOR]

This paper proposes a method for recognising the fault type using a set of diagnostic criteria. Existing approaches to the interpretation of dissolved gas analysis (DGA) results usually rely on single diagnostic criteria. The proposed method solves the problems of incomplete use of diagnostic information and discrepancies in the norms and criteria for recognising the same fault inherent in the existing methods. The feature of the method is the analysis of the compliance of the DGA results of the diagnosed equipment with the ones of the equipment with a confirmed diagnosis from the fault database. This approach outperforms the existing methods in the number of faults recognised, allowing a wider range of faults to be detected, including combined faults. In addition, the method provides an opportunity to identify possible causes and localise the fault. This feature is unique and provides a significant advantage when interpreting DGA results. The proposed method demonstrates significantly higher accuracy (100%) in recognising various fault types, including combined faults, in comparison to existing methods (about 50%). The proposed method has a 60% success rate in determining the cause and location of a fault, but this figure depends on the database with faults of various types used. [ABSTRACT FROM AUTHOR]

The retrofilling process involves the substitution of mineral oil in an existing transformer with ester oil serves as an alternate method to prolong the transformer lifetime. This pertains to the excellent characteristics of ester oil, recognized for its moisture resilience which in turns imparts a unique advantage by extending the lifespan of insulation paper. Nevertheless, the retrofilling procedure involves mixing the new ester oil with the existing aged mineral oil (AMO) in the transformer oil, which ultimately impacts the performance of the new ester oil. Hence, it is crucial to evaluate the performance of mixed oil to identify any indications of oil deterioration due to retrofilling before energising the transformer. In evaluating the performance of aged mineral-synthetic ester (AMO/SE) retrofilled oil, this work presents the analysis of the total acid number (TAN) and Fourier Transform Infrared (FTIR) spectra. Prior to the retrofilling process using fresh-treated synthetic ester oil, four distinct amounts of aged mineral oil (AMO) were prepared, comprising 3 %, 5 %, 7 % and 9 %. This option provides the opportunity to simulate varying levels of remaining mineral oil present in the transformer. The AMO samples were prepared by subjecting the fresh mineral oil to thermal ageing in a vacuum oven at a temperature of 130 ℃ for two different durations, i.e. 75 and 150 hours. Throughout the collected data, the amount of acids in AMO/SE oil has exhibited a rising trend with a decreased in proportions of AMO in the fresh-treated synthetic ester oil for both AMO conditions. Meanwhile, the severity of AMO conditions give a significant effect to the acid growth in AMO/SE retrofilled oil. In conjunction with FTIR spectra, the rise in acid concentration in retrofilled AMO/SE oils was clearly observable in the C=O stretch in the region of 1710-1770 cm-1, which exhibited a significant association with the TAN value. [ABSTRACT FROM AUTHOR]

Transformer oil is used to maintain the core and winding of the transformer. The main tasks of transformer oil as an industrial oil are insulation and cooling of a transformer. In the current study, the thermophysical and thermal properties of transformer oil nanofluid with volume percentages of 0.017% and 0.56% have been experimentally investigated which improves the performance of the base fluid due to the high thermal conductivity coefficient of carbon nanotubes compared to transformer oil. Ultrasonic baths and chemically functionalizing techniques were utilized in a two-step process to create nanofluids, which were stabilized by these techniques. In a double-tube carbon steel heat exchanger, the nanofluids were employed, and the thermal characteristics of the base fluid and nanofluids were evaluated. The findings indicated that the effective thermal conductivity improves with increasing temperature and CNT concentration, reaching its maximum value at a temperature of 45 °C in a volume fraction of 0.56. Observations showed that the heat transfer coefficient rose with rising Reynolds number and volume percentage, whereas the friction factor reduced when the hot fluid flowed at an intake temperature of 80 °C compared to the nanofluid in the outer tube. The flow rates for the nanofluids were calculated to be 0.2, 0.3, 0.4 lit/s, and 0.18 lit/s for the hot fluid, respectively. As a general conclusion, carbon nanotubes have a very high potential to improve the thermal performance of transformer oils, which is a serious challenge. [ABSTRACT FROM AUTHOR]

Power transformers are the most important and expensive assets in high-voltage power systems. To ensure an adequate level of reliability throughout the transformer's lifetime, its maintenance strategy must be well defined. When an incipient fault occurs in the transformer insulation, a gas concentration pattern, representative of the type of fault, is generated. Fault-identification methods use gas concentrations and their ratios to identify the type of fault. None of the traditional or new fault-identification methods attempt to detect transformer oil contamination from on-load tap changer (OLTC) gases. In this study, from dissolved gas analysis (DGA) samples of transformers identified as contaminated in a previous study, fault-identification methods based on graphical representations were used to observe the patterns of results. From such patterns, Duval's triangle and pentagon methods were modified to include a new zone indicating oil contamination (OC) from OLTC gases. Finally, the proposed modifications were validated using 75 DGA samples extracted from previous studies that were identified as D1 or D2 faults or contaminated from OLTC. This validation showed that only 14.7% and 13.3% of the DGA samples fell within the new OC zone of the proposed triangle and pentagon, respectively. [ABSTRACT FROM AUTHOR]

To detect the oil leakage problem of the power transformer in time and guarantee the normal operation of the power system, this paper proposes a lightweight-attention YOLOv8s, called LA_YOLOv8s, for oil leakage detection in the power transformer. First, the idea of lightweight structure + attention is used to redesign the backbone and neck of the original network respectively, which greatly reduces the number of parameters and calculation amount, but also retains strong feature extraction ability. Second, the dynamic head frame Dyhead is used to improve the representation ability of the detection head. Finally, the Wise-IoU bounding box loss function with a dynamic non-monotonic focusing mechanism is used to further improve the overall performance of the model by balancing the learning of high and low-quality samples. Using a self-constructed outdoor power transformer oil leakage dataset for training and testing, the results show that compared with the original model, the attention of LA_YOLOv8s becomes focused, enabling the network to detect oil leaks in power transformers better. LA_YOLOv8s greatly reduces the number of parameters and computation, while maintaining the same detection performance as the original network, which is conducive to engineering deployment. • The backbone and neck of YOLOv8s are redesigned. • The Dyhead is introduced to enhance the detection capability of the head network. • Introduced Wise-IoU to trade off the learning between low-quality and high-quality samples better. • Parameters and computation drop by 55.9% and 36.9%, respectively, with less than 1% change in precision, recall, and mAP. • A visualization algorithm is used to preliminarily validate the reliability of the model. [ABSTRACT FROM AUTHOR]

Along with the increase in demand for electricity, dependability requirements for the power supply system also increase. Transformers are the backbone of the electrical system; thus, they must function reliably and safely. In this study, a method based on Fourier-transform infrared spectroscopy technology is proposed for assessing the ageing level of insulating oils. Copper-catalysed thermal ageing is applied to four distinct insulating oils at a temperature of 120°C12{0}^{^\circ }{\rm{C}}. The insulating oil ageing peaks are promptly measured using infrared spectroscopy, and the peak area ratios are associated with the interfacial tension and dielectric loss factor. It is observed that the peak area ratio of the ageing peaks and interfacial tension are significantly correlated, and this relationship can be used to determine the ageing condition of the insulating oil. The findings of this study can be used to design sensors for online monitoring of insulating oil quality.

The use of alternative insulating liquids instead of mineral oil in transformers is spreading around the world due to their superior fire resistance. Furthermore, researchers have demonstrated that these oils increase the lifespan of the solid insulation of the transformers and, thus, the life expectancy of the equipment. Retrofilling of transformers with natural and synthetic esters allows companies to benefit from the properties of using these liquids without making an investment into new machinery. This paper investigated the ageing process of Kraft paper that was retrofilled with a natural ester in the laboratory. The Kraft paper samples were subjected to accelerated thermal ageing in an oven at 130 °C, and markers such as the degree of polymerisation and tensile strength were measured. The ageing tests comprised a first period, where the samples were immersed in mineral oil, followed by a replacement of the oil with a natural ester. As moisture is a determinant factor for paper ageing, two sets of samples with different moisture contents were tested. The results showed that the retrofilling of the transformers may slow down the degradation rate of the solid insulation despite the presence of remaining mineral oil adsorbed in the paper. [ABSTRACT FROM AUTHOR]

To overcome the difficulty in tracking the trajectory of an inspection robot inside a transformer, this paper proposes a distributed model predictive control method. First, the kinematics and dynamics models of a robot in transformer oil are established based on the Lagrange equation. Then, by using the nonlinear model predictive control method and following the distributed control theory, the motion of a robot in transformer oil is decoupled into five independent subsystems. Based on this, a distributed model predictive control (DMPC) method is then developed. Finally, the simulation results indicate that a robot motion control system based on DMPC achieves high tracking accuracy and robustness with reduced computing complexity, and it provides an effective solution for the motion control of robots in narrow environments. [ABSTRACT FROM AUTHOR]

This work is a maiden attempt to investigate the role of fluorinated boron nitride nanosheets (f-BNNs) in monitoring electrical properties of transformer oil (TO). Ultrathin (~5 nm) f-BNNs of 100–500 nm width were obtained by exfoliating pristine h-BN using NH4F. The exfoliation resulted in attaching few electronegative fluorine (F) to boron atoms and converted h-BN from insulator to semiconductor. TO nanofluids (NFs) at 0.005 wt. % of f-BNNs showed ~27 % surge in AC breakdown voltage (BDV) (validated by Weibull statistics), high volume resistivity and excellent stability, superior than h-BN nanosheets or BN nanoparticle. The leap in the electrical insulation was explained due to the fluorination assisted reduction in charge relaxation time from 60 ms–30 s in h-BN to 63 μs–0.6 s in f-BNNs, higher degree of streamer charge scavenging in the electrical double layers (EDLs) and interfacial polarization at the large oil-nanofiller interface. A theoretical calculation yielded in a potential depth of 13.8 V and 19.5 V at h-BN and f-BNNs surface, respectively, which implied f-BNNs provided a huge potential barrier and propagation delay to the streamer charges. Finally, the superior stability of f-BNNs in TO with time and temperature was observed from multiple tests and was explained owing to its 2D morphology, lipophilicity, and F induced large natural electrostatic repulsion between the nanosheets. Such excellent responses at low filler fractions recommends f-BNNs as a better alternative among all reported BN brothers for future TO applications. [ABSTRACT FROM AUTHOR]

The Ru atomic catalyst-doped GaN (Ru-GaN) model was established by density functional theory (DFT), and the adsorption models of transformer oil dissolved gas (CO, H2, CH4, C2H2, C2H4 and C2H6) by a Ru-GaN monolayer were optimised. From the adsorption energy, differential charge density, electron state density, molecular orbital and desorption properties, the modification mechanism of Ru-GaN and the gas-sensitive response mechanism of Ru-GaN to CO, H2, CH4, C2H2, C2H4 and C2H6 were systematically studied. The introduction of metal Ru increases the defect state of GaN to provide the active site for the adsorption of gas molecules and reduces the adsorption barrier of GaN to gas molecules. The results show that Ru metal-doped GaN can adsorb CO, C2H2 and C2H4 by chemisorption, with Eads of −2.068 eV, −2.422 eV and −1.877 eV and Qt of −0.1696 e, −0.0671 e, −0.1222 e. In addition, charge redistribution leads to changes in the density of states, molecular orbitals and the conductivity of CO, C2H2 and C2H4 adsorption systems, indicating excellent gas sensitivity to the three gases. The desorption time further verified that Ru-GaN is an ideal CO and C2H2 gas scavenger and a recyclable material for C2H4 gas sensors. [ABSTRACT FROM AUTHOR]

Transformers insulated with mineral oil tend to form gases, which might be caused by system faults or extended use. Based on an evaluation of the main failure analysis techniques using combustible gases, this study reviewed the conventional techniques for Dissolved Gas Analysis (DGA), present in the norms IEC 60599 and IEEE Std C57.104, and their failure analysis tendency. Furthermore, to illustrate distinct technique performances and failures, the performance of the following techniques was analyzed based on the IEC TC10 database: Dornenburg, Duval Triangle, Duval Pentagon, IEC ratio method, Key Gas, and Rogers. The objective of this work was to present relevant information to support students and professionals who work in failure analysis and/or assist in the development of new tools in the DGA field. [ABSTRACT FROM AUTHOR]

Dissolved gas in transformer oil (DGA) online monitoring data can reflect equipment insulation performance, which provides an important basis for transformer condition assessment. The accuracy of online monitoring data directly affects the correctness of transformer condition assessment, and the health condition of the online monitoring data device also affects the effectiveness and reasonableness of online monitoring data. With the accumulation of operation time, the DGA online monitoring device faces many problems, resulting in the overall deviation of this indicator data from the actual value, and the transformer online monitoring device cannot play its monitoring effect. To solve these problems, this paper firstly linearizes the data of the DGA online monitoring device by segmentation, extracts the characteristics of the line segment curve, constructs a segmented correlation mining model, and mines the correlation of different indicators of DGA. Then, according to the change of correlation strength and weakness between online monitoring data in time sequence, the data deviation is tracked. Finally, a multi‐indicator back propagation (BP) neural network algorithm optimized by the genetic simulated annealing algorithm is constructed to calibrate the deviated data. The analysis shows that the method can identify the population deviation of online monitoring data and calibrate the deviated data effectively. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

There exists a complicated mapping relationship between the types of transformer faults and the compositions and concentrations of dissolved gases in transformer oil. A methodology based on multi-objective optimization and case-based reasoning (CBR) is proposed to diagnose transformer faults by analyzing the dissolved gas analysis data. Considering the characteristics of the features used in the transformer diagnosis model, the targeted improvements are made in the preprocessing phase of the features to protect the diversities of samples. The quality of the feature subset is quantified by the internal cross-validation based on the CBR, and the feature subset is optimized by NSGA-II, considering both the classification performance and the size of the feature subset. The effect of the algorithm parameters on the performance is investigated, and the results validate the robustness of the proposed methodology. The proposed methodology is applied to diagnose cases from the IEC TC 10 database, achieving a 100 % fault type recognition accuracy and a 92.6 % severity evaluation accuracy. [ABSTRACT FROM AUTHOR]

The vegetable‐insulating oils are used in electrical systems recently. Most of vegetable‐insulating oils are manufactured from new oils. In this paper, we propose to develop the insulating oils from the used cooking oil. The used cooking oils that means thermal degraded—vegetable oil has high kinematic viscosity that does not meet the JIS values. In this study, the kinematic viscosity of thermal degraded oil was decreased using ester exchange reaction. We simulated the thermally‐degraded rice ester oil as used cooking oil, and investigated the potential of used cooking oil as insulating oil. We manufactured rice ester oil after thermal degradation before and after the ester‐exchange reaction and measure kinematic viscosity, AC breakdown voltage, moisture amount and volume resistivity for oil samples. We evaluated the degree of thermal degradation of the sample oil using total polar molecule (TPM). The results are described as follows. (1) The kinematic viscosity and volume resistivity of the rice oil increased with the thermal degradation progress (increase in TPM). The kinematic viscosity of the rice ester oil at each TPM value decreased after the ester‐exchange reaction. (2) After vacuum deaeration (2 h at 80°C and 22 h at room temperature), the moisture content of the rice oil before and after the ester‐exchange reaction was almost the same. (3) The AC breakdown strength after ester‐exchange reaction was higher than that of the untreated oil. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

As a traditional fossil fuel, petroleum fuel is prone to spill fires during storage, transportation, and use, which poses a significant threat to the secure utilization of energy. This research aims to investigate the effect of porous media on the combustion characteristics of spill fire. Quartz sand (diameter is 1.5 mm) is selected as a porous material, and continuous oil spill fire experiments under different oil discharge rates (25–100 mL·min−1) are carried out on both smooth substrate and porous bed. The spread process, burning rate, flame height and spread speed are measured and analyzed. The results show that no burning layer shrinkage occurs during the spread process. There is a significant increase in the stable burning length and in the time required to reach the quasi-steady burning phase. The burning rate of spill fire on a porous bed is negatively correlated with the thickness of the sand layer and positively correlated with the oil discharge rate, but it is still lower than the burning rate of pool fire under the same equivalent diameter. Transformer oil spill fire propagation speed is divided into three stages: acceleration-uniform-deceleration. When the oil discharge rate is 50 mL·min−1, the uniform propagation speed of flame is 0.55, 0.11 and 0.03 cm·s−1 respectively. Flame height is positively correlated with oil discharge rate and negatively correlated with sand thickness. Furthermore, a dimensionless coefficient, d*, is introduced to modify the flame height and burning rate models, and the correctness of the modified model is verified by experimental data. This study is informative for the safe use of liquid fuels. [ABSTRACT FROM AUTHOR]

This research presents a novel link between refractive index sensing and spectroscopy by designing a "photonic crystal fiber" based "refractive index ( RI )" sensor. The proposed sensor is based on the "surface plasmon resonance" phenomenon. The sensor monitors the transformer oil ( To ) moisture content as moisture depletes its quality. The change in the moisture content changes the RI of the transformer oil. Thus, the presented sensor can identify the minimal change in the RI of the To . The sensor is designed to operate in the RI range of 1.33 - 1.34 RIU , with a step size of 0.002. Using the wavelength interrogation method, the proposed sensor's wavelength sensitivity is 25,000 nm/RIU and 22,500 nm/RIU along X-polarization (X-pol.) and Y-polarization (Y-pol.), respectively. The amplitude interrogation technique is used to calculate the amplitude sensitivity obtained from the proposed sensor, which is 17080 RIU - 1 and 18690 RIU - 1 corresponding to X-pol. and -pol., respectively. The sensor resolution of the sensor is 4.00 × 10 - 6 RIU and 4.44 × 10 - 6 RIU corresponds to X-pol. and Y-pol., respectively. Full-width half maximum obtained from the proposed sensor is 73 nm and 55 nm for X- and Y-pol. , respectively. The figure of merit obtained from the presented sensor is 342.46 RIU - 1 and 409.09 RIU - 1 for X - and Y-pol., respectively. Thus, the prospective sensor can act as an ideal device for the quality assessment of To . [ABSTRACT FROM AUTHOR]

Due to the aging of transformers during their service life, the dielectric resistance of transformer oils decreases. This dangerous situation, which may cause major power outages, needs to be analyzed well in detail. Transformer oils start to deteriorate due to the high electric field between the electrodes. The sharp points on the electrodes accelerate this deterioration process. Therefore, by correctly analyzing the effect of the electrodes on the oil, information about the insulation resistance of the liquid dielectric material can be obtained. In this study, electric fields and potential difference changes of transformer oil under uniform and non-uniform electric fields are investigated. Sphere-sphere electrode configurations were used to provide uniform field, sphere-rod, and rod-rod electrode configurations for non-uniform field effect. With the increase of the non-uniform field effect, higher electric field stress occurred in the transformer oil at the same voltage level. [ABSTRACT FROM AUTHOR]

In this study, camphoric acid was used as a carbon source in the synthesis of the catalyst for the first time. The new Fe3O4/C/SiO2/TiO2 and Fe3O4/C/TiO2 magnetic nano-semiconductors were synthesized and used for hydrogen peroxide-free photodegradation of polychlorinated biphenyls (PCBs) in transformer oil (Askarel) and significant PCB 138 in aqueous solutions under visible light irradiation. Also, interestingly, direct degradation was carried out for Askarel oil in addition to the extraction-degradation method. These nano-reduced band gap semiconductors (2.10 eV) were characterized by field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), N2 adsorption-desorption isotherm analyses, and vibrating sample magnetometer (VSM). The high photocatalytic performance compared to Fe3O4/C/TiO2 and P25 in direct and indirect (extraction-degradation) degradation besides its convenient magnetic recovery are the main advantages of Fe3O4/C/SiO2/TiO2. [ABSTRACT FROM AUTHOR]

The prediction of dissolved gas change trends in power transformer oil is very important for the diagnosis of transformer faults and ensuring its safe operation. Considering the time series and nonlinear features of the gas change trend, this paper proposes a novel robust extreme learning machine (ELM) model combining an improved data decomposition method for gas content forecasting. Firstly, the original data with nonlinear and sudden change properties will make the forecasting model unstable, and thus an improved variational modal decomposition (IPVMD) method is developed to decompose the original data to obtain the multiple modal dataset, in which the marine predators algorithm (MPA) optimization method is utilized to optimize the free parameters of the VMD. Second, the ELM as an efficient and easily implemented tool is used as the basic model for dissolved gas forecasting. However, the traditional ELM with mean square error (MSE) criterion is sensitive to the non-Gaussian measurement noise (or outliers). In addition, considering the nonlinear non-Gaussian properties of the dissolved gas, a new learning criterion, called extended maximum correntropy criterion (ExMCC), is defined by using an extended kernel function in the correntropy framework, and the ExMCC as a learning criterion is introduced into the ELM to develop a novel robust regression model (called ExMCC-ELM) to improve the ability of ELM to process mutational data. Third, a gas-in-oil prediction scheme is proposed by using the ExMCC-ELM performed on each modal obtained by the proposed IPVMD. Finally, we conducted several simulation studies on the measured data, and the results show that the proposed method has good predictive performance. [ABSTRACT FROM AUTHOR]

The present work emphasized overall viability study of using waste transformer oil (WTO) and its combinations on a diesel engine. To accomplish this fuel samples were divided into three sets with different volumetric proportions. Each set of fuel is compared with the others and finally, the reference fuel is regular diesel. After thorough characterization and utilizing spectroscopic methods like CHNS analysis and FTIR, the performance and emission evaluation was done on a one-cylinder diesel engine. It has been discovered that the flash point, calorific value, and viscosity of WTO blends are more than regular diesel fuel. The WTO blends have higher carbon content than diesel but higher Sulphur content than diesel fuel. WTO and its blends have some similar functional groups as diesel fuel. According to the performance and emission evaluation, the diesel engine is capable of operating even when fueled with raw WTO. Among all the samples tested, WTO20 BD10D70 shows the most satisfactory result. The brake thermal efficiency of WTO20BD10D70 was almost similar to diesel fuel, while BSFC and BSEC were found to be only 4.73% and 3.46% higher than diesel fuel, respectively. The exhaust gas temperature was 8% lower than diesel. However, HC emission of WTO20BD10D70 was 20% more than diesel fuel, while CO and NOx emissions were 14% and 4.75% lower than diesel fuel. So, the sample code with WTO20BD10D70 can be recommended as a substitute for regular diesel fuel. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the robot is difficult to locate in the oil-immersed transformer, a visual positioning of the robot is proposed for internal inspection. First, in order to solve the problem of blur, distortion, and low contrast of the image obtained by the camera in the deteriorated and discolored transformer oil, an image enhancement algorithm based on multiscale fusion is developed to provide a reliable data source for robot localization. Then, the FAST key points are extracted and the BRIEF descriptors are calculated from the enhanced images, and the pose transformation of the robot between image frames is calculated by using polar constraint and EPnP method. A pose optimization model of the robot is designed to improve the positioning accuracy. Finally, to verify the effectiveness of the proposed methods, function tests are carried out by using the real continuous image sequence acquired by the robot in Mitsubishi transformer. The experimental results show that the trajectory of the robot in the transformer can be accurately drawn, the position data of the robot can be efficiently obtained, and autonomous positioning of the robot in the transformer can be well achieved. [ABSTRACT FROM AUTHOR]

Power grids transport electricity from the point of generation to the market. Power conversion from HV to LV and vice versa occurs in grids, also known as substations. These substations or power grids can be accessible or situated in remote locations. Transformers are used to convert power; they are an essential part of transmission and distribution networks. The method of grid monitoring and maintenance is essentially a very monotonous one. Monitoring the health of the transformers to maintain an uninterrupted power supply to the customers is difficult in such circumstances. Overvoltage, load currents, oil temperature, transformer oil level, and other parameters are monitored. The condition of the distribution transformer's is evaluated in this article using real-time data from the transformer and specific sensors connected to Raspberry pi and artificial neural networks, are used to analyse the situation and make decisions regarding the health of the transformer. A model has been proposed for continuous monitoring consistent vigilance and swift actions against any faulty situations. [ABSTRACT FROM AUTHOR]

The presence of moisture is one of the factors that promote degradation of transformer insulating oils and deterioration of cellulose insulation materials in oil-immersed power transformers, which affect the lifespan of the transformers. Realizing the importance of moisture in transformer insulating oils, this study compares the effectiveness of three moisture treatment techniques nitrogen bubbling technique (NBT), molecular sieve technique (MST), and vacuum oven technique (VOT)) for mineral oil (MO). The moisture content and AC breakdown voltage of the MO samples before and after moisture treatment were measured using Karl Fischer coulometric titrator and portable oil tester, respectively, in accordance with the American Society for Testing and Materials (ASTM) D1533 and ASTM D1816 standards. The results showed that NBT is the best moisture treatment technique for the MO, where the NBT reduced 80.79% of moisture present in the oil, followed by MST and VOT, which reduced 72.87 and 42.28% of moisture, respectively. The results also showed that the AC breakdown voltage of the MO samples after moisture treatment was improved owing to the reduction in moisture content. [ABSTRACT FROM AUTHOR]

The current work aims to propose an adequate thermodynamic model, in addition to proposing and evaluating two composite models for the thermal decomposition of insulating mineral oil (IMO), considering that the models based on classical diagnostic methods do not have the ability to satisfactorily reproduce empirical data. The simulation results obtained using the proposed model showed better agreement with the presented data than the results obtained using classical models. The proposed model was also used in the development of a phenomenological based diagnostic method. The characteristics of this new phenomenological proposal and the classical diagnostic methods of dissolved gas analysis are compared and discussed; the proposed method showed better performance when compared to Rogers, Doernenburg, or IEC and equivalent performance to Duval triangle method commonly used in this field of knowledge. The general procedure for applying the new diagnostic method is also described. In order to account for the event's dynamics, the suggested model in particular made it feasible to replicate intermediate scenes of equilibrium C(s). Compared to the findings from the classical models found in the literature, the two-dimensional simulation results generated with this model demonstrated a better agreement with the actual data. [ABSTRACT FROM AUTHOR]

The regular operation of transformers is significantly impacted by the insulation effectiveness of the transformer insulation oil–paper. In order to explore the mechanism of the influence of an electric field on the thermal decomposition performance of insulating oil–paper, this paper simulated the process of electrothermal coupling decomposition of insulating oil–paper from the micro-level based on molecular dynamics. It was determined that the insulating oil is made up of three 16-carbon hydrocarbon molecules, while the insulating paper is made up of 30 fibrous disaccharide molecules. Using the molecular dynamics simulation approach, the pyrolysis of the insulating oil and insulating paper under various electric field strengths was simulated, and the lysis of reactants and the distribution of products were statistically examined. This paper also studied how the electric field affected the microscopic process of the insulating oil–paper pyrolysis. The findings demonstrate that under the influence of electrothermal coupling, the big molecules of the insulating oil and insulating paper are pyrolyzed to produce a variety of tiny molecules. For the insulating oil, it is easily subject to electron displacement polarization under the influence of an external electric field since it contains non-polar molecules, especially impacted by an electric field of 100 V/m. For the insulating paper, its polar nature, on the other hand, makes itself a good candidate for guiding polarization when exposed to an external electric field. So, the greater the electric field strength is, the greater the impact on the thermal decomposition of the insulating paper is. [ABSTRACT FROM AUTHOR]