Search Results (139)

Winding hot-spot temperature (HST) is a key factor affecting the insulation life of transformers. This paper proposes an optimization method based on the Kriging response surface model, which minimizes HST by adjusting the key structural parameters of the number of winding zones, vertical oil channel width, and horizontal oil channel height. First, a two-dimensional axisymmetric temperature–fluid field coupling model is established, and the finite volume method is used to solve the HST under the actual structure, which is 92.59 °C. A total of 50 sample datasets are designed using Latin hypercube sampling, and the whale optimization algorithm (WOA) is used to determine the optimal kernel parameters of Kriging with the goal of minimizing the root mean square error (RMSE) under 5-fold cross-validation. Combined with the genetic algorithm (GA) global optimization of structural parameters, the Kriging model predicts that the optimized HST is 89.77 °C, which is verified by simulation to be 89.79 °C, achieving a temperature drop of 2.80 °C, proving the effectiveness of the structural optimization method. Full article

When a transformer operates under three-phase unbalanced conditions, the location of the winding hot-spot temperature (HST) is no longer fixed on a certain phase. Taking an S13-M-100 kVA/10 kV transformer as the research object, this paper proposes a streamline inversion method for inverting the winding HST based on the analysis of oil flow morphology. The study employs the finite volume method for coupled calculations of a transformer’s thermal fluid field and combines a support vector regression (SVR) model for the HST inversion. An orthogonal experimental method is used to construct the training and testing sample sets, and the grid search method is utilized to optimize the parameters of the SVR model. In response to variations in hot-spot locations under three-phase unbalanced conditions, representative streamlines are reasonably selected, and a genetic algorithm-based dimensionality reduction optimization is performed on the feature quantities. The research results indicate that the established inversion model exhibits high inversion accuracy under three-phase unbalanced conditions, with a maximum temperature difference of 3.71 K, and the robustness check verifies the stability of the model. Full article

During drilling operations, lost circulation frequently occurs, leading to significant loss of drilling fluids which causes environmental damage and increasing drilling costs. To address the problem of fracture plugging, gel materials have emerged as an ideal solution due to stable physicochemical properties and excellent environmental compatibility. However, most existing gels exhibit poor stability and low mechanical strength under high-temperature conditions. To overcome these limitations, high-temperature-resistant phase-conversion stiffened dual-network hydrogel for oil-based drilling fluids was developed. Phase-conversion was realized by immersing synthesized double-network hydrogel in ethylene glycol (EG), polyethylene glycol (PEG), and glycerol (Gly), optimizing and enhancing its mechanical properties, followed by plugging performance evaluations. Experimental results demonstrated that the phase-conversion stiffened gels achieved significantly improved compressive strength and plugging efficiency at elevated temperature. The GC-MS results indicated that dehydration and reagent exchange occurred during immersion, with change in the solid content of the sample. After being treated by white oil at high temperature, the oil phase almost replaced the water phase in the gel. The results of ATR-IR confirmed the formation of hydrogen bonds in the gel. TGA data revealed that PEG enhanced the thermal stability of the gel, EG negatively affected thermal stability, and Gly had negligible influence. The enhancement in gel strength primarily stems from the increase in solid content caused by phase transformation. Dehydration and multiple hydrogen bonds formed between organic reagent molecules and polymer chains in the gel have a synergistic enhancement effect. Full article

Transformer health assessment enables predictive maintenance strategies that extend equipment lifespan, minimize resource consumption, and support sustainable power system operations. However, traditional methods often rely on simple health indicators, which fail to effectively capture the complex relationships within transformer health data. To address this issue, this article proposes a joint training method based on a wide and deep model, enhanced with Bayesian inference and Markov chain Monte Carlo (MCMC) techniques. The model combines a wide component, which uses linear regression to identify global patterns in transformer health parameters, and a deep neural network that learns complex nonlinear relationships, such as those in thermal aging data. Bayesian inference is integrated to quantify uncertainties in the predictions, while MCMC is employed for robust parameter estimation during training. This combination enables a more accurate, interpretable, and comprehensive assessment of transformer conditions. Experimental results on realistic datasets show that the proposed method significantly improves prediction accuracy and reliability compared to existing approaches. Specifically, the joint wide and deep model outperforms traditional methods by 6.6% in classification accuracy, demonstrating its potential for application in smart grid systems. This research contributes to sustainable power system management by enabling more efficient resource utilization and supporting the transition to sustainable energy systems. Full article

As the popularity of electric vehicles (EVs) continues to increase, the need for effective and efficient driveline lubricants and dielectric coolants has become crucial. Commercially used mineral oils or synthetic ester-based coolants, despite performing satisfactorily, are not environmentally friendly. The fatty esters of vegetable oils, after overcoming their shortcomings (like poor oxidative stability, higher viscosity, and pour point) through chemical modification, have recently been used as potential dielectric coolants in transformers. The benefits of natural esters, including a higher flash point, breakdown voltage, dielectric character, thermal conductivity, and most importantly, readily biodegradable nature, have made them a suitable and sustainable substitute for traditional coolants in electric transformers. Based on their excellent performance in transformers, research on their application as dielectric immersion coolants in modern EVs has been emerging in recent years. This review primarily highlights the beneficial aspects of natural esters performing dual functions—cooling as well as lubricating, which is necessary for “wet” e-motors in EVs—through a comparative study with the commercially used mineral and synthetic coolants. The adoption of natural fatty esters of vegetable oils as an immersion cooling fluid is a significant sustainable step for the battery thermal management system (BTMS) of modern EVs considering environmental safety protocols. Continued research and development are necessary to overcome the ongoing challenges and optimize esters for widespread use in the rapidly expanding electric vehicle market. Full article

Sustainable deepwater drilling for oil and gas offers significant potential. In this work, we synthesized a nanoscale collapse-prevention agent by grafting didecyldimethylammonium chloride onto spherical nano-silica and characterized it using Fourier-transform infrared spectroscopy, thermogravimetric analysis, zeta-potential, and particle-size measurements, as well as SEM and TEM. Adding 1 wt% of this agent to a bentonite slurry only marginally alters its rheology and maintains acceptable low-temperature flow properties. Microporous-membrane tests show filtrate passing through 200 nm pores drops to 55 mL, demonstrating excellent plugging. Core-immersion studies reveal that shale cores retain integrity with minimal spalling after prolonged exposure. Rolling recovery assays increase shale-cutting recovery to 68%. Wettability tests indicate the water contact angle rises from 17.1° to 90.1°, and capillary rise height falls by roughly 50%, reversing suction to repulsion. Together, these findings support a synergistic plugging–adsorption–hydrophobization mechanism that significantly enhances wellbore stability without compromising low-temperature rheology. This work may guide the design of high-performance collapse-prevention additives for safe, efficient deepwater drilling. Full article

As the key non-electric protection equipment of an oil-immersed transformer, the gas relay plays an important role in ensuring the safe operation of the transformer. To further enhance the sensitivity of gas relays for the heavy gas alarm, this paper takes the BF type double float gas relay as the research object and proposes a new method for heavy gas setting, which is based on the internal oil flow acceleration characteristics of the gas relay. Firstly, the analytical derivation of the force acting on the gas relay baffle is carried out, and through theoretical analysis, the internal mechanism of heavy gas action under transient oil flow excitation is revealed. Then, the numerical simulation and experimental research on the variation of oil flow velocity and acceleration under different fault energies are carried out. The results show that with the increase of fault energy, the oil flow velocity fluctuates up and down during heavy gas action, but the oil flow acceleration shows a linear correlation. The oil flow acceleration can be set as the threshold of heavy gas action, and the severity of the fault can be judged. At the same time, the alarm time of the heavy gas setting method based on the oil flow acceleration characteristics is greatly shortened, which can reflect the internal fault of the transformer in time and significantly improve the sensitivity of the heavy gas alarm. Full article

During the manufacturing process of oil-immersed current transformers, metallic particles may become embedded in the insulation wrapping, and the resulting electric field distortion is one of the primary causes of failure. Historically, the shape of metallic particles has often been simplified to a standard sphere, whereas in practice, these particles are predominantly irregular. In this study, ellipsoidal and flaky particles were selected to represent smooth and angular surfaces, respectively. Using COMSOL Multiphysics^® (version 6.2) software, a three-dimensional simulation model of an oil-immersed inverted current transformer was developed, and the influence of defect position and size on electric field characteristics was analyzed. The results indicate that both types of defects cause electric field distortion, with longer particles exerting a greater influence on the electric field distribution. Under the voltage of a 220 kV system, elliptical particles (9 mm half shaft) lead to the maximum electric field intensity of main insulation of up to 45.1 × 10⁶ V/m, while the maximum field strength of flaky particles (length 30 mm) is 28.9 × 10⁶ V/m. Additionally, the closer the particles are to the inner side of the main insulation, the more significant their influence on the electric field distribution becomes. The findings provide a foundation for fault analysis and propagation studies related to the main insulation of current transformers. Full article

For large oil-immersed transformers, their metal-enclosed structure poses significant challenges for direct visual inspection of internal defects. To ensure the effective detection of internal insulation defects, this study employs a self-developed micro-robot for internal visual inspection. Given the substantial morphological and dimensional variations of target defects (e.g., carbon traces produced by surface discharge inside the transformer), the intelligent and efficient extraction of carbon trace features from complex backgrounds becomes critical for robotic inspection. To address these challenges, we propose the DCMC-UNet, a semantic segmentation model for carbon traces containing adaptive illumination enhancement and dynamic feature fusion. For blurred carbon trace images caused by unstable light reflection and illumination in transformer oil, an improved CLAHE algorithm is developed, incorporating learnable parameters to balance luminance and contrast while enhancing edge features of carbon traces. To handle the morphological diversity and edge complexity of carbon traces, a dynamic deformable encoder (DDE) was integrated into the encoder, leveraging deformable convolutional kernels to improve carbon trace feature extraction. An edge-aware decoder (EAD) was integrated into the decoder, which extracts edge details from predicted segmentation maps and fuses them with encoded features to enrich edge features. To mitigate the semantic gap between the encoder and the decoder, we replace the standard skip connection with a cross-level attention connection fusion layer (CLFC), enhancing the multi-scale fusion of morphological and edge features. Furthermore, a multi-scale atrous feature aggregation module (MAFA) is designed in the neck to enhance the integration of deep semantic and shallow visual features, improving multi-dimensional feature fusion. Experimental results demonstrate that DCMC-UNet outperforms U-Net, U-Net++, and other benchmarks in carbon trace segmentation. For the transformer carbon trace dataset, it achieves better segmentation than the baseline U-Net, with an improved mIoU of 14.04%, Dice of 10.87%, pixel accuracy (P) of 10.97%, and overall accuracy (Acc) of 5.77%. The proposed model provides reliable technical support for surface discharge intensity assessment and insulation condition evaluation in oil-immersed transformers. 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

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

We demonstrate the use of Laser Power Converters (LPCs) driven by fiber laser light while immersed in transformer oil for heat management purposes. Reliability tests performed via extended continuous operation using 6–7 W of input power from 808 nm and 976 nm light propagating through oil show no degradation of components nor transmission losses from the oil for up to 1000 h. The operation of a bare die designed for use with 1040–1080 nm light and in direct contact with oil is also shown to be feasible. We discuss how the use of transformer oil can be beneficial to transfer excess heat away from LPCs in special applications. 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

To enhance the efficiency and accuracy of detecting insulation faults such as discharge carbon traces in large oil-immersed transformers, this study employs an inspection micro-robot to replace manual inspection for image acquisition and fault identification. While the micro-robot exhibits compactness and agility, its limited battery capacity necessitates the critical optimization of its 3D inspection path within the transformer. To address this challenge, we propose a hybrid algorithmic framework. First, the task of visiting inspection points is formulated as a Constrained Traveling Salesman Problem (CTSP) and solved using the Ant Colony Optimization (ACO) algorithm to generate an initial sequence of inspection nodes. Once the optimal node sequence is determined, detailed path planning between adjacent points is executed through a synergistic combination of the A algorithm*, Rapidly exploring Random Tree (RRT), and Particle Swarm Optimization (PSO). This integrated strategy ensures robust circumvention of complex 3D obstacles while maintaining path efficiency. Simulation results demonstrate that the hybrid algorithm achieves a 52.6% reduction in path length compared to the unoptimized A* algorithm, with the A*-ACO combination exhibiting exceptional stability. Additionally, post-processing via B-spline interpolation yields smooth trajectories, limiting path curvature and torsion to <0.033 and <0.026, respectively. These advancements not only enhance planning efficiency but also provide substantial practical value and robust theoretical support for advancing key technologies in micro-robot inspection systems for oil-immersed transformer maintenance. Full article