Search Results (287)

The converter transformer is subjected to AC/DC composite voltage during operation, and the sealed and time-varying internal state makes its electric field distribution and charge accumulation unable to be monitored in real-time experiments. In this paper, aiming at the influence of bubbles in the oil passage of the converter transformer on charge accumulation before discharge, a simulation model in a laminar flow environment is established, and four different calculation conditions are set to simulate the charge accumulation in 1 s. It is found that under laminar flow conditions, the trapped bubbles on the insulation paper wall play an obvious role in intensifying the charge accumulation in transformer oil, and the extreme range of charge density will increase by about 10⁴ times. Bubbles aggravate the electric field distortion, and the insulation strength of bubbles is lower, which becomes the weak link of insulation. In the laminar flow environment, the oil flow will take away part of the accumulated charge in the oil, but in the case of trapped bubbles, the charge accumulation in the insulating paper will increase from the order of 10⁻² to 10⁻¹. In the case of no bubbles, the transformer oil layer flow will increase the charge accumulation in the insulation paper by 4–5 orders of magnitude. Therefore, it can be seen that the flow of transformer oil will increase the deterioration level of insulation paper. And when the transformer oil is already in the laminar flow state, the influence of laminar flow velocity on charge accumulation is not obvious. The research results in this paper provide a time-varying simulation reference state for the charge accumulation problem that cannot be measured experimentally under normal charged operation conditions, and we obtain quantitative numerical results, which can provide a valuable reference for the study of transformer operation and insulation discharge characteristics. Full article

The top oil temperature of a transformer is a vital sign reflecting its operational condition. The accurate prediction of this parameter is essential for evaluating insulation performance and extending equipment lifespan. At present, the prediction of oil temperature is mainly based on single-feature prediction. However, it overlooks the influence of other features. This has a negative effect on the prediction accuracy. Furthermore, the training dataset is often made up of data from a single transformer. This leads to the poor generalization of the prediction. To tackle these challenges, this paper leverages large-scale data analysis and processing techniques, and presents a transformer top oil temperature prediction model that combines multiple models. The Convolutional Neural Network was applied in this method to extract spatial features from multiple input variables. Subsequently, a Long Short-Term Memory network was employed to capture dynamic patterns in the time series. Meanwhile, a Transformer encoder enhanced feature interaction and global perception. The spatial characteristics extracted by the CNN and the temporal characteristics extracted by LSTM were further integrated to create a more comprehensive representation. The established model was optimized using the Whale Optimization Algorithm to improve prediction accuracy. The results of the experiment indicate that the maximum RMSE and MAPE of this method on the summer and winter datasets were 0.5884 and 0.79%, respectively, demonstrating superior prediction accuracy. Compared with other models, the proposed model improved prediction performance by 13.74%, 36.66%, and 43.36%, respectively, indicating high generalization capability and accuracy. This provides theoretical support for condition monitoring and fault warning of power equipment. Full article

Power transformer insulation systems, composed of liquid and solid insulators, are continuously exposed to thermal and electrical stresses that degrade their performance over time and may lead to premature failure. Since these stresses are unavoidable during operation, selecting effective insulating materials is critical for long-term reliability. In this study, Kraft insulation paper was used as the solid insulator and impregnated with three different liquids: mineral oil and two natural esters (NE1204 and NE1215), to evaluate their stability under simultaneous thermal and electrical stress. The degradation behavior of the oil-impregnated papers was assessed using frequency-domain dielectric spectroscopy (FDS) and Fourier-transform infrared spectroscopy (FTIR), enabling early fault detection. Comparative analyses were conducted to evaluate the withstand capability of each liquid type during operation. Results revealed strong correlations between FTIR indicators (e.g., oxidation and hydroxyl group loss) and dielectric parameters (permittivity and loss factor), confirming the effectiveness of this combined diagnostic approach. Post-aging breakdown analysis showed that natural esters, particularly NE1215, offered superior preservation of insulation integrity compared to mineral oil. Differences between the two esters also highlight the role of chemical composition in insulation performance. This study reinforces the potential of natural esters as viable, eco-friendly alternatives in thermally and electrically stressed applications. Full article

To quantitatively describe the frequency domain spectroscopy (FDS) characteristics of transformer oil-paper insulation under varying temperature, moisture, and aging conditions, a modified Cole–Cole model is introduced. This model decomposes the dielectric spectrum into polarization, DC conduction, and hopping conduction components, with parameters reflecting insulation characteristics. Methods for determining initial parameter values and optimizing the objective function are proposed. Using a three-electrode setup, FDS measurements were conducted on oil-paper insulation samples at different temperatures, and extracted parameters were analyzed for their variation patterns. Within the frequency range of 1.98 × 10⁻⁴ Hz to 1 × 10³ Hz, the model achieves a goodness-of-fit (R²) exceeding 0.97 for both real and imaginary permittivity components, with the sum of squared errors reduced from 259 to 57.35 at 70 °C, outperforming the fundamental Cole–Cole and Ekanayake’s models. Temperature significantly affects the relaxation and DC conductivity components; both adhere to the Arrhenius equation, enabling precise condition assessment of transformer insulation. Full article

This paper presents the design, development, and characterization of a low-cost and environmentally friendly high-voltage divider optimized for on-site calibration up to 850 kV. Unlike traditional dividers that rely on oil or SF₆ for insulation, both of which pose environmental risk and regulation issues, the proposed system uses modular construction with commercial off-the-shelf components and natural air insulation, minimizing environmental impact and facilitating transport, calibration, and maintenance. Despite using air insulation, the divider demonstrates excellent uncertainty performance. Characterization results show frequency linearity better than 0.2% up to 100 kHz and a bandwidth exceeding 10 MHz, making it suitable for the measurement of a wide range of voltage types. Static and dynamic performance evaluations confirm reliable scale factor stability and low measurement uncertainty: 0.01% for DC (550 kV), 0.3% for AC (405 kV), and 0.7% for impulses such as 1.2/50 µs (850 kV). The system offers a practical and sustainable solution for high-voltage measurements, meeting growing industrial and European environmental demands. Full article

The traditional detection method of transformer oil acid value has limitations, such as long detection period and toxicity of reagents; while, with the traditional spectral analysis, it is difficult to realize the efficient extraction of key features related to the acid value content. Early detection of rising acid levels is critical to prevent transformer insulation degradation, corrosion, and failure. Conversely, delayed detection accelerates aging and can cause costly repairs or unplanned outages. To address this need, this paper proposes a new method for predicting the acid value content of the transformer oil based on the infrared spectra in the transformer oil and a deep neural network (DNN). The infrared spectral data of the transformer oil is acquired by ALPHA II FT-IR spectrometer, the high frequency noise effect of the spectrum is reduced by wavelet packet decomposition (WPD), and the bootstrapping soft shrinkage (BOSS) algorithm is used to extract the spectra with the highest correlation with the acid value content. The BOSS algorithm is used to extract the feature parameters with the highest correlation with the acid value content in the spectrum, and the DNN prediction model is established to realize the fast prediction of the acid value content of the transformer oil. In comparison with the traditional infrared spectral preprocessing method and regression model, the proposed prediction model has a coefficient of determination (R²) of 97.12% and 95.99% for the prediction set and validation set, respectively, which is 4.96% higher than that of the traditional model. In addition, the accuracy is 5.45% higher than the traditional model, and the R² of the proposed prediction model is 95.04% after complete external data validation, indicating that it has good accuracy. The results show that the infrared spectral analysis method combining WPD noise reduction, BOSS feature extraction, and DNN modeling can realize the rapid prediction of the acid value content of the transformer oil based on infrared spectroscopy technology, and the prediction model can be used to realize the analytical study of transformer oils. The model can be further applied to the monitoring field of the transformer oil characteristic parameter to realize the rapid monitoring of the transformer oil parameters based on a portable infrared spectrometer. Full article

Synthetic ester insulating oils are extensively utilized in power transformers due to their exceptional insulating properties, thermal stability, and environmental compatibility. The dissolved gas analysis (DGA) technique, which is employed to diagnose internal faults in transformers by monitoring the concentration and composition of dissolved gases in oil, is thought to be effective in detecting typical faults such as overheating and partial discharges in synthetic esters. However, owing to the significant differences in the properties of traditional mineral oil and synthetic esters, the existing DGA-based diagnostic methods developed for mineral oils cannot be directly applied to synthetic esters. A deep understanding of the microscopic processes occurring during the gas generation and diffusion of synthetic esters is an urgent necessity for DGA applications. Therefore, in this study, we systematically investigated the diffusion behavior of seven typical fault gases in synthetic ester insulating oils within a temperature range of 343–473 K using molecular dynamics simulations. The results demonstrate that H₂ exhibits the highest diffusion capability across all temperatures, with a diffusion coefficient of 33.430 × 10⁻⁶ cm²/s at 343 K, increasing to 402.763 × 10⁻⁶ cm²/s at 473 K. Additionally, this paper explores the microscopic mechanisms underlying the diffusion characteristics of these characteristic gases by integrating the Free-Volume Theory, thereby providing a theoretical foundation for refining the fault gas analysis methodology for transformer insulating oils. Full article

In power transformers, insulating liquids are essential for cooling, insulation, and condition monitoring. However, the environmental impact and biodegradability issues of traditional hydrocarbon-based liquids have spurred interest in green alternatives like natural esters. Despite their benefits, natural esters are highly prone to oxidation, limiting their broader use. This study explores a novel blend of two plant-based oils, canola oil and methyl ester derived from palm kernel oil, enhanced with two antioxidants, Tert-butylhydroquinone (TBHQ) and 2,6-Di-tert-butyl-4-methyl-phenol (BHT), to improve oxidation resistance. The performance of this antioxidant-infused oil was evaluated in terms of its interaction with Kraft paper insulation through accelerated thermal aging over periods of 10, 20, 30, and 40 days. Key properties, including the viscosity, breakdown voltage, conductivity, and FTIR spectra of oils, were analyzed before and after aging. Additionally, the degradation of the Kraft paper was investigated using scanning electron microscopy (SEM), optical microscopy, and dielectric strength tests. The results show that the antioxidant-treated oil exhibits significantly enhanced molecular stability, reduced viscosity, lower conductivity, and improved breakdown voltage (53.16 kV after 40 days). Notably, the oil mixture maintained the integrity of the Kraft paper insulation better than traditional natural esters, demonstrating superior dielectric properties and a promising potential for more sustainable and reliable power transformer applications. Full article

The aging of oil-impregnated paper (OIP) insulation is one of the key factors influencing the service life of oil-immersed current transformers. Frequency domain spectroscopy (FDS), supported by mathematical models or simulation methods, is commonly used to evaluate insulation conditions. However, traditional aging models typically ignored significant aging differences between the transformer OIP head and straight sections caused by the axial temperature gradient. To address this limitation, an accelerated thermal aging experiment was performed on a full-scale oil-immersed inverted current transformer prototype. Based on the analysis of its internal temperature field, the axial temperature gradient boundary of the main insulation was identified. By applying region-specific aging control strategies to different axial segments, a FEM model incorporating axial aging variation was developed to analyze its influence on FDS. The simulation results closely matched experimental data, with a maximum deviation below 9.22%. The model’s applicability was further confirmed through the aging prediction of an in-service transformer. The proposed model is expected to provide a more accurate basis for predicting the FDS characteristics of OIP insulation in current transformers. Full article

Due to its advantages, such as its corrosive sulfur-free property and high purity, gas-to-liquid (GTL) oil is regarded as an excellent alternative to conventional naphthenic mineral oil in the oil/paper composite insulation of UHV converter transformers. In such application scenarios, under the condition of voltage polarity reversal, charge accumulation is likely to occur along the liquid/solid interface, which leads to the distortion of the electric field, consequently reducing the breakdown voltage of the insulating material, and leading to flashover in the worst case. Therefore, understanding such space charge characteristics under polarity-reversed voltage is key for the insulation optimization of GTL oil-filled converter transformers. In this paper, a typical GTL oil is taken as the research object with naphthenic oil as the benchmark. Electroacoustic pulse measurement technology is used to study the space charge accumulation characteristics and electric field distribution of different oil-impregnated paper insulations under polarity-reversed conditions. The experimental results show that under positive–negative–positive polarity reversal voltage, the gas-impregnated pressboard exhibits significantly higher rates of space charge density variation and electric field distortion compared with mineral oil-impregnated paper. In stage B, the dissipation rate of negative charges at the grounded electrode in GTL oil-impregnated paper is 140% faster than that in mineral oil-impregnated paper. In stage C, the electric field distortion rate near the electrode of GTL oil-impregnated paper reaches 54.15%. Finally, based on the bipolar charge transport model, the microscopic processes responsible for the differences in two types of oil-immersed papers are discussed. Full article

There is growing interest in measuring the temperature-dependent dielectric properties of bio-tissues using dual-mode techniques (scattering measurements and thermal treatment). Uncooled coaxial antennas are preferred for their direct contact with the measured medium and reduced complexity; however, they exhibit structural changes during ablation due to the thermal expansion of polytetrafluoroethylene (PTFE). This paper presents an experimental study on PTFE expansion in an uncooled coaxial insulated monopole antenna in response to changes in the tissue’s thermal environment. Furthermore, it presents a methodology to mitigate these effects through coaxial annealing. The investigation consists of two distinct experiments: characterising PTFE expansion and assessing the effects of annealing through microwave ablation. This was achieved by simulating the thermal effects experienced during ablation by immersing the test antenna in heated peanut oil. PTFE expansion was measured through camera monitoring and using a toolmaker’s microscope, revealing two expansion modalities: linear PTFE expansion and non-linear plastic deformation from manufacturing processes. The return loss during ablation and consequential changes in the ablated lesion were also assessed. Antenna pre-annealing increased resilience against structural changes in the antenna, improving lesion ellipticity. Therefore, this study establishes a fabrication method for achieving an uncooled thermally stable antenna, leading to an optimised dual-mode ablation procedure, enabling quasi-real-time permittivity measurement of the surrounding tissue. Full article

This article continues the authors’ research on NOMEX^® 910 cellulose–aramid insulation saturated with modern electrical insulating liquids, which is increasingly used in the construction of high-power transformers The increase in technical requirements and environmental awareness influences, nowadays, shows that, during the overhaul and modernization of power transformers, petroleum-based mineral oils are increasingly being replaced by biodegradable synthetic esters (oil retrofilling). As a result of this process, the solid insulation of the windings are saturated with an oil–ester liquid mixture with a percentage composition that is difficult to predict. The purpose of the research described in this paper was to test the effect of the degree of mixing of synthetic ester with mineral oil on the diagnostic measurements of NOMEX^® 910 cellulose–aramid insulation realized via the polarization PDC method. Thus, the research conducted included determining the influence of such factors as the degree of mixing of synthetic ester with mineral oil and the measurement temperature on the value of the recorded time courses of the polarization and depolarization current. The final stage of the research involved analyzing the extent to which the aforementioned factors affect parameters characterizing polarization processes in the dielectric, i.e., the dominant dielectric relaxation time constants τ₁ and τ₂, and the activation energy E_A. The test and analysis results described in the paper will allow better interpretation of the results of diagnostic tests of transformers with solid insulation built on NOMEX^® 910 paper, in which mineral oil was replaced with synthetic ester as a result of the upgrade. Full article

Polydimethylsiloxane (PDMS) with good hydrophobicity and nano-SiO₂ with excellent thermal stability and mechanical properties are used as a composite coating for cellulose insulating paper in oil-immersed transformers, which effectively reduces the moisture generated by the thermal aging process, thus prolonging each transformer’s service life. This study employed molecular dynamics simulations to investigate the effects of surface-modified nano-SiO₂ with different silane coupling agents (KH570 and KH151) on the thermodynamic properties and hydrophobicity of PDMS. Four groups of anhydrous models were constructed, namely, PDMS, P-SiO₂, P-570, and P-151, as well as four corresponding groups of water-containing models: PDMS/H₂O, P-SiO₂/H₂O, P-570/H₂O, and P-151/H₂O. The results demonstrate that incorporating silane-coupled nano-SiO₂ into PDMS enhances mechanical properties, FFV, CED, MSD, diffusion coefficient, interaction energy, and hydrogen bond count, with KH570-grafted composites exhibiting optimal thermomechanical performance and hydrophobicity. At a temperature of 343 K, KH570 modification increased the bulk modulus and CED by 26.5% and 31.0%, respectively, while reducing the water molecular diffusion coefficient by 24.7% compared to that of unmodified PDMS/SiO₂ composites. The extended KH570 chains occupy additional free volume, forming a larger steric hindrance layer, restricting molecular chain mobility, suppressing hydrogen bond formation, and establishing a low energy surface. Full article

In this paper, results of comparative studies on the positive lightning impulse breakdown voltage (LIBV) and accelerating voltage (V_a) of six insulating liquids of different chemical composition are presented. This paper discusses the behavior of uninhibited naphthenic mineral oil (UMO), inhibited naphthenic mineral oil (IMO), natural ester (NE), synthetic ester (SE), and two modern dielectric fluids: bio-based hydrocarbon (BIO) and inhibited liquid produced using Gas-to-Liquids (GTL) technology. Measurements are taken in a point-to-sphere electrode system for two selected gap distances: 25 mm (which is suggested by the IEC 60897 standard) and 40 mm. After analyzing the obtained results, it is noted that positive LIBV does not differ significantly between the tested liquids. Noticeable differences are observed, however, for V_a. The lowest values of this parameter characterize ester liquids, which is consistent with the common knowledge in this field. In addition, the obtained values of LIBV and V_a are used to evaluate the maximum values of electric field intensity through the application of simulations for each specific case based on the finite element method. These simulations confirm that, for a given parameter, maximum electric field stress is on similar level, regardless of the gap distance. This proves that the breakdown and appearance of fast discharges are determined by specific field conditions. Full article

Aimed at solving the problem of insulation failure caused by the local overheating of the oil-immersed converter transformer, this paper investigates the heat transfer characteristics of the 500 kV converter transformer based on the electromagnetic-flow-heat coupling model. Firstly, this paper used the finite element method to calculate the core and winding loss. Then, a two-dimensional fluid-heat coupling model was used to investigate the effects of the inlet flow rate and the radius of the oil pipe on the heat transfer characteristics. The results show that the larger the inlet flow rate, the smaller the specific gravity of high-temperature transformer oil at the upper end of the tank. Increasing the pipe radius can reduce the temperature of the heat dissipation of the transformer in relative equilibrium. Still, the pipe radius is too large to lead to the reflux of the transformer oil in the oil outlet. Increasing the central and sub-winding turn distance, the oil flow diffusion area and flow velocity increase. Thus, the temperature near the winding is reduced by about 9%, and the upper and lower wall temperature is also reduced by about 4%. Based on the analysis of the sensitivity weight indicators of the above indicators, it is found that the oil flow rate has the largest share of influence on the hot spot temperature of the transformer. Finally, the surface temperature of the oil tank when the converter transformer is at full load is measured. In the paper, the heat transfer characteristics of the converter transformer are investigated through simulation and measurement, which can provide a certain reference value for the study of the insulation performance of the converter transformer. Full article