Search Results (329)

Cellulose insulation paper used in oil-immersed power transformers is vulnerable to molecular chain loosening and aging-product transport under thermal and electrical stresses. Nanomodification is a promising route for improving insulation-paper stability, but the atomistic mechanisms by which KH550-grafted oxide nanoparticles regulate cellulose structure and aging-molecule mobility under an external electric field remain insufficiently clarified, particularly when the role of oilpaper insulation aging and oil-contact environments is considered. In this work, pristine cellulose and cellulose modified with KH550-grafted SiO₂ and Al₂O₃ nanoparticles were investigated using molecular dynamic simulations at 343 K under a uniform electric field of 0.01 V/Å (100 kV/mm) applied along the Z-axis. Based on the MSD and apparent transport-parameter results, nanomodification reduced the MSD-derived apparent coefficients of H₂O and CO₂ by 33.7–51.9%. The external field produced apparent directional transport bias, with Z/X apparent-coefficient ratios of 2.21 for H₂O and 2.02 for CO₂ in pristine cellulose. These ratios decreased to 1.62 and 1.51, respectively, in the KH550–Al₂O₃ model. Interfacial interaction energy analysis showed that the KH550–SiO₂ interface became more strongly bound under the field (−240.50 to −252.13 kcal/mol), whereas the KH550–Al₂O₃ interface remained nearly unchanged (−541.65 to −538.81 kcal/mol). Because the two nanomodified systems use different nanoparticle loadings and KH550 grafting ratios, cross-system differences are interpreted as model-specific outcomes rather than effects attributable only to nanoparticle chemistry. These results indicate that KH550-grafted nanoparticles may help maintain cellulose packing under the modeled conditions and suppress aging-molecule mobility in the simulated cellulose matrix, while the conclusions should be interpreted as atomistic simulation evidence rather than direct proof of long-term transformer reliability. Full article

Methanol is a critical molecular marker for the early aging of oil-paper insulation, and its rapid detection is highly valuable for insulation condition assessment and the fault warning of power transformers. Widely used chromatographic methods require sophisticated pretreatment workflow and are not suitable for in situ monitoring. Non-destructive spectroscopic methods remain challenging due to the intrinsically small cross section of trace molecules in complex liquid environments. The rapid, direct detection of trace methanol in an oil mixture has yet to be demonstrated. In this study, a high-performance Raman-enhancing substrate was developed through hierarchical microstructure regulation, combining microscale light-trapping structures and nanoscale field-confinement sites to sense the weak Raman response of methanol in transformer oil. Direct detection of ppm-level methanol in the oil matrix was achieved, without additional adsorption enrichment or other complicated pretreatment procedures. The characteristic Raman band of methanol in transformer oil was identified, and a quantitative sensing method was established. Furthermore, the intrinsic temperature-dependent Raman response of methanol was investigated to evaluate the stability of its characteristic fingerprint bands over a broad temperature range. This work demonstrates a rapid, sensitive, and pretreatment-free spectroscopic strategy for trace methanol detection in complex oil matrices, and also sheds light on the high-sensitivity detection of small molecular markers in complex liquid environments. Full article

This paper investigates the multifaceted macroeconomic drivers of vehicle electrification in India and correlates them with micro-level corporate financial returns using a rigorous dual-stage econometric framework. Stage 1 employs a Newey–West time-series estimator on monthly observations to evaluate aggregate consumer demand elasticities across the automotive sector. Stage 2 utilizes a fixed effects panel specification with clustered standard errors across 10 major Indian automotive manufacturers over a four-year fiscal horizon. Stage 1 results demonstrate that short-run variations in Brent crude prices lack joint predictive power over domestic retail metrics ($F=0.89,p=0.4166$), supporting the thesis that state-owned OMC price-smoothing insulates short-term market dynamics from global oil shocks. Conversely, Stage 2 panel estimations prove that annual global Brent crude fluctuations yield no significant contemporaneous margin shocks. However, expanding annual EV market penetration exerts a substantive negative impact ($\beta =-2.49,p=0.107$) bordering statistical significance on corporate operating profit margins. This operational decoupling reflects a prominent industry ‘J-curve’, where accelerating consumer adoption cycles are countered by heavy front-loaded capital expenditures, asset re-tooling, and unoptimized economies of scale. These findings provide critical direct and indirect strategic insights for organizational stakeholders and policymakers navigating transitional capital cycles in emerging markets. Full article

The results of the experiments on the transport process of fluid flow through a pipeline under temperature gradient conditions between the internal and external environments, and on continuous gas generation at the contact boundary of the transported media, are presented in this paper. The test results showed that under non-isothermal flow conditions, a slippage effect will impact flow velocity and pressure, as well as the temperature distributions in variable cross-section pipes. Laboratory experiments were conducted in order to study the effects of the gas nucleus at the pipe walls on the hydrodynamic characteristics of the fluid flow. It is shown that the throughput capacity of the pipe is affected by the temperature difference between the oil and the pipe walls. The test results also demonstrated that at certain temperature gradients on the border layer, the pipe’s capacity reaches its maximum value. Quantitatively, the hydroconductivity of Q/ΔP increased from about 1.45 × 10⁻⁵ m³/(s·MPa) under relatively isothermal conditions to a maximum value of approximately 2.04 × 10⁻⁵ m³/(s·MPa) with a temperature difference in the oil–pipe-wall zone of about 3–5 K, which corresponds to an increase of about 41%. With a further increase in the temperature difference, the hydroconductivity decreased to about 1.64 × 10⁻⁵ m³/(s·MPa) at 10 K and then stabilized in the range of (1.60–1.64) × 10⁻⁵ m³/(s·MPa). This non-monotonic behavior is explained by the temperature-induced release of gas and the formation of a gas-saturated wall zone, which initially reduces the effective resistance of the wall and creates an apparent sliding effect. At high temperature differences, gas accumulation, thermal insulation of the wall area and two-phase flow disturbances limit this effect, which leads to the decrease and subsequent stabilization of the pipe capacity. Full article

Dissolved gas analysis (DGA) remains a cornerstone technique for the early detection of internal faults in power transformers. However, the interpretation of gas signatures is inherently complex due to coupled thermochemical processes, operating variability, and measurement uncertainties. Over the past three decades, artificial intelligence (AI) techniques have been widely applied to DGA-based diagnostics, evolving from conventional machine learning to advanced deep learning and hybrid models. Although many studies report diagnostic accuracies exceeding 95%, these results often rely on limited datasets and heterogeneous validation practices, raising concerns about their robustness in real-world operating conditions. This review provides a comprehensive and critical analysis of the evolution of AI-based approaches, with emphasis on methodological foundations rather than algorithmic performance alone. Key aspects examined include dataset construction, validation protocols, class imbalance handling, and experimental design. The analysis highlights a structural Accuracy-Robustness Gap, in which high reported accuracies often coexist with limited evidence of generalisation under realistic industrial variability. To address this issue, a conceptual framework is proposed linking the physical complexity of the oil–paper insulation system, dataset characteristics, and algorithmic modeling strategies. The review further identifies several key research gaps in benchmarking practices, dataset availability, robustness evaluation, and model interpretability. Based on these findings, a standardized evaluation framework is proposed to support more rigorous and comparable assessments of AI-based DGA diagnostic models. Future research directions are discussed, highlighting the need for physics-informed learning, distribution-aware validation, and shared datasets to enable reliable deployment of AI-based transformer diagnostics. Full article

The metallic shell of converter transformer oil conservators prevents direct visual monitoring of the internal capsule, creating a long-standing monitoring blind spot that may leave hidden defects such as top gas accumulation undetected. To address this issue, this paper proposes an endoscopic video monitoring device installed inside the oil conservator capsule, enabling continuous visual monitoring without compromising insulation performance. On this basis, an anomaly detection method is established: region-of-interest (ROI) templates are defined in areas where the capsule normally adheres to the metallic inner wall of the oil conservator; normalized cross-correlation (NCC) is applied for template matching, and kernel density estimation (KDE) statistically analyzes the distribution of matching scores from normal samples to determine per-template thresholds. For valid template localizations, Euclidean distances among template centroids are extracted as geometric features and fed into an Isolation Forest for unsupervised anomaly scoring. Validation on field data from a 500 kV converter station demonstrates that the method achieves a precision of 98.78%, a recall of 96.43%, and an F1-score of 97.59%. The results indicate that the proposed method can distinguish normal capsule breathing from capsule morphological abnormalities caused by top gas accumulation, providing a basis for online monitoring of oil conservator capsules. Full article

Oil-immersed power transformers and high voltage reactors may experience abnormal mechanical vibration during operation, especially under complex electromagnetic and load conditions. Such vibration can induce periodic pressure fluctuations in narrow oil–paper gaps, promoting bubble formation, collapse, and associated characteristic gas production. Since characteristic gases are important indicators for insulation condition assessment, vibration-induced gas generation may affect the interpretation of dissolved gas analysis and fault diagnosis. However, the gas production behavior and underlying mechanism of oil–paper insulation under combined mechanical vibration and electric field stress remain insufficiently understood. In this work, an equivalent oil–paper gap model was developed to experimentally investigate the effects of vibration parameters and electric field strength on gas generation under vibration–electric field coupling. The bubble collapse dynamics under vibration were further analyzed using a modified Rayleigh–Plesset (R-P) equation. Results indicate that the localized high-temperature region produced during bubble collapse in the positive-pressure phase of vibration initiates pyrolysis of insulating oil and paper, generating characteristic gases including H₂, CO, CO₂, CH₄, C₂H₄, C₂H₆, and C₂H₂, among which CO₂, CO, H₂, C₂H₄, and CH₄ are the dominant components under test conditions. At low electric field strength (before partial discharge inception), the additional pressure contributed by electrostatic forces intensifies bubble collapse, increasing the concentrations of H₂, CO_x, and THC by 15.9%, 7.6%, and 29.8%, respectively. At high electric field strength (after partial discharge inception), discharge-induced decomposition of oil and paper further increases the concentrations of H₂, CO_x, and THC by approximately 47.7%, 30.0%, and 44.9%, respectively. These findings provide theoretical and data support for evaluating insulation conditions and understanding failure mechanisms in oil-immersed power equipment subjected to vibration. Full article

The presence of bubbles severely threatens the insulation safety of power transformers. Understanding bubble accumulation characteristics is essential for predicting potential insulation weaknesses. In this study, we propose a Euler–Lagrange two-phase flow model, combined with an experimental platform, to investigate the accumulation and growth characteristics of bubbles in typical oil channels. The continuous phase flow field is solved within the Eulerian framework, while discrete bubble trajectories are tracked using the Lagrangian approach, simultaneously accounting for drag, buoyancy, and friction forces. The results indicate that bubbles primarily accumulate on the top insulating paper surfaces within horizontal and corner oil channels. In horizontal channels, the accumulation rate correlates positively with flow velocity and bubble concentration; furthermore, paper overlapping gaps intercept sliding bubbles, promoting their coalescence into larger ones. Conversely, at channel corners, the accumulation length and rate decrease as flow velocity increases, with accumulation weakening significantly when the velocity exceeds 0.6 m/s. Additionally, bubble growth is highly sensitive to flow velocity and temperature: higher velocities inhibit bubble coalescence, whereas elevated temperatures accelerate both coalescence and growth. Ultimately, this research reveals the fundamental accumulation laws of bubbles in transformer oil channels, providing a reliable analytical tool and theoretical reference for assessing bubble-induced insulation risks. Full article

Accurate monitoring of internal winding temperature is essential for assessing the thermal state and operational reliability of oil-immersed transformers. However, direct deployment of distributed temperature sensors inside transformer windings is difficult because of insulation constraints, structural complexity, and potential reliability risks. To address this problem, this paper proposes a non-invasive internal winding temperature estimation method based on surface temperature sensing and a hybrid deep learning model. In the proposed framework, external surface temperature measurements are used as sensor inputs to infer the internal transient thermal state of the transformer. First, an extreme gradient boosting (XGBoost) model is employed to evaluate the contribution of different surface temperature measurement points and select the sensing locations that are most strongly correlated with internal winding temperature variations. Then, the selected surface temperature time-series data are used to train a Long Short-Term Memory (LSTM) network, which captures the temporal evolution of the transformer temperature field under different operating conditions. The proposed method is verified through both numerical simulation and experimental testing on a scaled single-phase oil-immersed converter transformer model (D-800/35) developed in this study. The results show that the proposed XGBoost-LSTM model can estimate internal winding temperature with an error of less than 1.5 K. Compared with direct internal sensing, the proposed method provides a non-invasive and sensor-efficient solution for internal temperature monitoring. The results demonstrate its potential for real-time thermal state estimation, condition monitoring, and fault diagnosis of oil-immersed converter transformers. Full article

To address the issues of thermal non-uniformity and insulation aging of converter transformers operating under long-term high electric field and high-temperature conditions in ultra-high-voltage direct current (UHVDC) transmission systems, this paper investigates the temperature field distribution characteristics of converter transformers based on electro–thermal–mechanical multiphysics coupling. By establishing a full-scale multiphysics simulation model of a ±800 kV converter transformer, the interactions among the electric field, temperature field, and mechanical stress field are comprehensively considered. The temperature gradient distribution and hotspot formation mechanisms within the valve-side winding and the lead-out structure are revealed. The results show that the internal temperature distribution of the converter transformer is non-uniform, resulting in a nonlinear distribution of material parameters in oil-paper insulation, which significantly affects the insulation performance. The research findings provide a theoretical basis and engineering reference for the structural optimization and thermal stability improvement of the main insulation system of converter transformers. Full article

This paper presents an investigation into the effect of dissolved gases (DGs) on the insulating properties, such as breakdown strength, of Midel EN 1204 natural ester oil and Poweroil TO 1020 60U mineral oils. The gasses were generated by simulating thermal fault/s at 130 °C, 210 °C, 340 °C, 400 °C, and 450 °C. Dissolved gas analysis (DGA) was conducted according to IEC 60567 to determine the concentrations of H₂, CH₄, C₂H₆, C₂H₄, C₂H₂, and CO in each of the twelve oil samples. Moisture was measured using the Karl Fischer Method according to IEC 60814. The breakdown voltage (BDV) was measured according to IEC 60156. The results show that total dissolved gas concentration and rate of rise increased with fault temperature in both oils. For this relatively short time experiment, the rise in concentration of DGs had minimal effect. The overall BDV 73.7 kV (virgin ester oil BDV) increased to 76.3 kV at 450 °C for natural ester oil, whereas the BDV of mineral oil decreased from 68.7 kV to 63.1 kV. These findings showed that natural ester oil has better insulation stability under thermal stress. Full article

Two bio-based insulating oils (BHOs) with average carbon chain lengths of approximately 18 and 22 were investigated as short- and long-chain BHOs. By constructing an oil-paper composite insulation system, the generation law of characteristic gases in the two systems was studied by partial discharge experiments. Based on the ReaxFF reaction molecular dynamics simulation under electrothermal coupling stress, the cracking path, cracking rate, evolution of oxygen-containing small molecules, and generation path of characteristic gases of cellulose polymer were revealed. Both systems produced H₂, CH₄, C₂H₂, C₂H₄, C₂H₆, CO, and CO₂, with CO₂ dominant and C₂H₆ least abundant. The short-chain BHO generated markedly higher amounts of H₂, CO, C₂H₂, and C₂H₄ than the long-chain BHO; after 15 min, its H₂ and CO concentrations were about 3.4- and 2.1-times those in the long-chain system, respectively. ReaxFF simulations showed that cellulose degradation in the short-chain BHO followed stepwise chain scission and continuous decarbonylation, favoring CO and unsaturated gas precursors. In contrast, cellulose chains disappeared faster in the long-chain BHO, producing more oxygen-containing organic fragments and C₁-C₅ oxygenated molecules and a higher small-molecule conversion ratio. Characteristic gas pathway analysis revealed that all seven gases could be generated from cellulose pyrolysis intermediates, and different oil environments primarily influenced gas generation behavior by altering the evolution pathways of these intermediates. These findings, at the molecular scale, elucidate the impact of BHO environments on the degradation mechanism of cellulose polymers, providing a theoretical basis for the condition assessment and design of environmentally friendly oil-paper insulation systems. Full article

This paper reviews emerging dielectric fluids for power transformers, including natural and synthetic esters, silicone oils, gas-to-liquid oils, and nanofluids, driven by environmental regulations, fire safety concerns, and the need for extended asset life. The review synthesizes technical data from standards and field experience, including a case study of an Eskom transformer energized in 2016 with natural ester fluid. Analysis confirms these fluids offer significant benefits in fire safety, biodegradability, and dielectric performance, with the case study demonstrating natural esters’ effectiveness in preserving solid insulation. However, trade-offs involving cost, material compatibility, and operational protocols require careful management. Full article

Oil-filled current transformers are crucial in high-voltage substations, directly affecting grid safety and reliability. Traditional defect diagnosis methods often show low accuracy and limited monitoring coverage, failing to meet operation and maintenance requirements. This paper proposes an AI-empowered digital twin-based defect diagnosis method that addresses typical issues like oil leakage, insulation damage, and moisture ingress by extracting relevant characteristic parameters to create an evaluation index system. A digital twin model integrates winding, core, and thermal flow characteristics, enabling real-time acquisition of operation parameters and precise mapping between physical and virtual transformers. A dual-model AI framework using Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM) is introduced for intelligent defect identification and early defect prediction through multi-source data fusion. Finally, a corresponding diagnostic system is developed and verified using actual operation data from a 220 kV substation in Liaoning Province. The results show that the proposed method enables the online monitoring of multiple operating parameters, and the dual-model framework exhibits higher diagnostic accuracy and faster computation speed compared with Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), providing effective support for intelligent condition-based maintenance of current transformers. Full article

To address the challenges of low detection accuracy and high missed detection rates in insulating oil leakage detection for power equipment—arising from small and densely distributed oil stains, structural occlusion, and complex background interference—this paper proposes a detection method based on an enhanced YOLOv11s (You Only Look Once version 11 small) architecture. First, a dedicated dataset is constructed, encompassing four representative scenarios—small object detection, complex background, multi-object detection and equipment occlusion—to evaluate detection performance. Second, in terms of network design, a proposed attention module, SimAMWS (Simple Attention Module With Slicing), is introduced. This module enhances the model’s sensitivity to subtle and irregular oil stains by utilizing slicing operations and localized energy-based weighting. For bounding box regression, a U-IoU (Unified Intersection over Union) loss is adopted, which incorporates a dynamic scaling mechanism during training to enable the model to focus more effectively on high-quality candidate boxes—leading to improved localization accuracy, particularly suited to the characteristics of oil leakage. Finally, comparative experiments are conducted against mainstream object detectors including SSD (Single Shot MultiBox Detector), Faster R-CNN (Region-based Convolutional Neural Network), YOLOv5s, YOLOv8s, and the baseline YOLOv11s. The proposed method achieves an mAP@0.5 (mean Average Precision at IoU = 0.5) of 97.7% and an mAP@0.5:0.95 of 66.9%, with an inference speed of 96.4 FPS. These results demonstrate that the proposed model delivers higher detection accuracy while maintaining high inference efficiency, making it well-suited for real-time oil leak detection in power equipment and supporting the development of intelligent operation and maintenance systems in the power industry. Full article