Search Results (166)

The oil-immersed environment within power transformers is characterized by high temperatures, strong electric fields, and severe electromagnetic interference, posing significant challenges for simultaneous multi-parameter monitoring. Conventional electrical sensors are susceptible to electromagnetic interference, whereas typical integrated fiber Bragg grating (FBG) sensors exhibit cross-sensitivity and reliability issues under such harsh operating conditions. To address these challenges, this paper proposes an integrated FBG-based sensor. Through specialized material and structural design, each sensing element is engineered to respond predominantly to its target parameter at the physical level. This approach effectively mitigates cross-sensitivity, enabling high-precision simultaneous measurement of oil temperature, pressure, and moisture content. Under simulated transformer oil conditions, the sensor achieved a temperature sensitivity of 17.1 pm/°C, a pressure sensitivity of approximately 4 nm/MPa, and a moisture sensitivity of 7.775 × 10⁻⁴ nm/%RS (equivalent to 6.37 × 10⁻⁴ nm/ppm at 40 °C). The results also confirmed excellent linearity, repeatability, and resistance to cross-sensitivity. These findings demonstrate that the proposed integrated FBG sensor can achieve stable multi-parameter measurement and effective decoupling under the tested transformer-oil conditions, indicating its potential for engineering application in transformer online monitoring. Full article

Accurately locating and reliably diagnosing insulation defects in oil-immersed transformers remains challenging. To overcome this, a micro-robot is employed to autonomously identify partial discharge (PD)-induced carbon traces on the insulation surface of the core components. Accurately capturing the multi-scale complex features of surface-discharge carbon traces under low-illumination conditions is critical for effective defect detection. Therefore, to address the obscurity of carbon trace features caused by insufficient illumination inside oil-immersed transformers, a Retinex-based image enhancement algorithm with superimposed illumination estimation is proposed. By transforming the original image into the HSI color space and integrating negative-image illumination fusion, this algorithm decouples brightness from chromaticity and preserves dark-region details, thereby reducing color distortion and enhancing carbon trace features. Furthermore, to handle the significant scale variations in carbon traces, a C2f module integrated with spatial and channel synergistic attention (SCSA) is designed. This module employs multi-scale depthwise separable convolutions and wide-channel self-attention to enhance cross-scale feature representation and reduce redundancy. Moreover, to address the feature resolution degradation in the fast spatial pyramid pooling module, which hinders the accurate perception of tiny carbon traces, a poly kernel inception atrous spatial pyramid pooling module (PKI-ASPP) is adopted. This preserves precise morphological details and minimizes the missed and false detection rates for tiny carbon traces. Finally, to tackle the difficulties in fusing complex morphological features, a deformable large kernel attention (DLKA) module is introduced into the neck network. This adapts to irregular carbon trace shapes, significantly improving the localization and learning of complex morphologies. Experiments on a transformer PD carbon trace dataset demonstrate that the proposed model significantly improves perceptual capabilities for carbon traces with massive scale variation. The improved model outperforms the baseline across all evaluation metrics, with mAP50 improved by 2.7% and mAP50-95 improved by 7.9%. These results indicate that the proposed method is highly reliable, providing solid technical support for internal surface discharge intensity detection and insulation condition assessment in oil-immersed transformer maintenance. 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

Dissolved gas analysis (DGA) is an essential technique for the fault diagnosis and condition monitoring of oil-immersed power transformers. Among various characteristic gases, acetylene (C₂H₂) is a key indicator of high-energy discharge and arc faults. In this work, a high-sensitivity dissolved acetylene detection system is developed based on clamp-type quartz-enhanced photoacoustic spectroscopy (QEPAS). A specially designed clamp-type quartz tuning fork (Clamp-type QTF) is employed as the acoustic transducer to improve acoustic coupling efficiency and optical alignment tolerance. Compared with conventional standard quartz tuning forks, the clamp-type structure exhibits enlarged acoustic interaction volume, lower damping loss, and higher signal collection capability. A near-infrared distributed feedback (DFB) laser operating at 1531.6 nm is used as the excitation source. The dissolved gas is extracted from transformer oil using a headspace degassing module and introduced into the QEPAS cell for real-time measurement. Experimental results showed that the developed system achieves a 1σ-based SNR-estimated detection limit of 17 ppb at a 50 s integration time, derived from the continuous measurement of 0.75 ppm C₂H₂, with excellent linearity in the concentration range from 100 ppm to 500 ppm. The measured concentration of dissolved acetylene in transformer oil is in good agreement with gas chromatography (GC), validating the effectiveness and practical applicability of the proposed system. Full article

The hot-spot temperature in oil-immersed 3D wound core transformer has a significant impact on its performance. The complexity of the winding structure and the characteristics of oil flow increase the difficulty of temperature field analysis. To address this challenge, this study aims to propose a comprehensive thermal network model for oil-immersed 3D wound core transformers to accurately calculate the winding average temperature rise and local hot-spot temperature rise with high efficiency. First, based on the principle of constant thermal resistance, a detailed model of high- and low-voltage winding is calculated using 2D finite element simulation technology. An equivalent model is established to obtain the equivalent thermal conductivity. This model considers various variables, including wire diameter, external insulation dimensions, and the vertical and longitudinal spacing of the windings. Next, multiple types of thermal resistance are defined using the thermoelectric analogy method, and a global thermal network model of the oil-immersed 3D wound core transformer is constructed. Using the Gauss–Seidel method and relevant heat transfer theory, factors such as the flow of transformer cooling oil are taken into account, which allows for the calculation of the average temperature rise and local hot-spot temperature rise in the windings. This approach effectively reduces calculation time while ensuring accuracy. Finally, a 50 kVA oil-immersed amorphous alloy 3D wound core transformer is used as a case study, and temperature field experimental tests are conducted to verify the accuracy of the proposed analytical model. Full article

Transformers are core equipment in power systems, and their failure may cause severe accidents. Dissolved gas analysis (DGA) is one of the dominant techniques for fault diagnosis of oil-immersed transformers. To achieve lightweight design and high identification efficiency, this paper proposes an early fault identification method for transformers based on the integration of the Convolutional Block Attention Module-enhanced ShuffleNetV2 (CBAM-SV2) model and Gramian Angular Field (GAF). First, hybrid oversampling is used for data preprocessing. Then, the preprocessed one-dimensional gas data are converted into dual-channel two-dimensional images via GAF as the input of the classification network. Finally, a CBAM-SV2 model integrating deep convolutional networks and attention mechanisms is constructed, which combines the lightweight advantage of ShuffleNetV2 and the powerful feature representation ability of the Convolutional Block Attention Module (CBAM). Feature extraction and classification are performed by the CBAM-SV2 model to output the identification results. Additionally, t-distributed Stochastic Neighbor Embedding (t-SNE) and a confusion matrix are used to visualize classification performance for intuitive evaluation of the network’s effectiveness. The experimental results show that, compared with other mainstream algorithms, the proposed method achieves higher recognition accuracy in transformer early fault classification under imbalanced data conditions. Full article

In the context of new-type power system construction, digital twin has become the core technology for power transformers, supporting their full-life cycle intelligent operation and maintenance. The real-time, high-precision calculation of the internal temperature field serves as the core supporting element for realizing the real-time mapping between the physical transformer entity and its virtual twin. Aiming at the inherent defects of traditional temperature rise calculation methods, such as insufficient accuracy and an excessively long computation time, this paper proposes a simplified calculation model for the transformer temperature field. In this model, the transformer oil tank is simplified into a two-dimensional axisymmetric thermal–fluid coupled field model solved by the finite volume method (FVM). The Proper Orthogonal Decomposition (POD) technique is adopted to perform order reduction on the matrices involved in the governing equations, so as to reduce the computational degrees of freedom. Meanwhile, the radiator is equivalent to a one-dimensional thermal circuit model, and the field–circuit coupled solution is achieved through bidirectional data mapping. Temperature field calculation is carried out for a 220 kV oil-immersed transformer based on the proposed model. The results show that the average relative error between the calculated results and the experimental data is around 0.86%, while the computation time is merely 0.04% of that of the traditional three-dimensional full-scale model. Furthermore, taking the real-time overload capacity evaluation of the transformer as a case, it is verified that the proposed model can successfully support the requirements of practical engineering applications. Full article

This paper presents a comprehensive life-cycle analysis of distribution transformers, based on realized measurements of the increased power losses as a result of their long-term service under real-world conditions. The study is based on aggregated measured data from extensive fleets of oil-immersed distribution transformers characterized by diverse designs, manufacturing vintages, and service lives. The evolution of no-load losses and short-circuit losses is analyzed as a function of operational duration, structural characteristics, and the specific technologies employed for windings and magnetic core construction. Statistical models describing the variation in these losses are presented, highlighting the limitations of the static assumptions commonly utilized in power distribution network planning. On this basis, an approximation of the time evolution of the transformer’s total power and energy losses is proposed as appropriate for implementation in a life-cycle analysis model. Furthermore, the impacts of thermal loading and abnormal operating conditions—such as unbalanced loads, frequent short circuits, and repeated overheating of the transformer oil—are analyzed as drivers of accelerated transformer aging. These effects are integrated into a unified life-cycle framework, enabling the quantitative assessment of loss variations and their associated operational expenditures (OPEX). A numerical example is provided to evaluate the cost-effectiveness of “repair vs. replacement” scenarios, utilizing a discounted cash flow analysis that incorporates a carbon component. The findings establish a methodological foundation for a broader assessment of technical condition and energy performance, identifying the optimal intervention point for repair or replacement to support decision-making for Distribution System Operators (DSOs) amidst increasing requirements for efficiency and decarbonization. Full article

Cellulose paper, a natural polymeric dielectric, determines the lifetime of oil–paper insulation systems in transformers, yet its molecular degradation behavior in ester-based insulating media remains insufficiently clarified. This study investigates the electro–thermal aging of cellulose polymer immersed in soybean-based natural ester (SBNE) and palm fatty acid ester (PFAE), with emphasis on depolymerization and its relationship with partial discharge (PD) activity. Accelerated aging experiments were conducted under combined electrical and thermal stress, and the evolution of the degree of polymerization (DP) was measured to quantify polymer chain scission. Phase-resolved PD (PRPD) patterns were recorded during aging, and multi-dimensional statistical features were extracted and reduced using principal component analysis to characterize degradation-sensitive electrical responses. The results show a progressive decrease in DP with aging time in both ester media, accompanied by distinct PD evolution characteristics, indicating different influences of the two esters on cellulose polymer stability. An ensemble learning model integrating multiple classifiers was further employed to identify aging stages based on PD features, achieving reliable discrimination performance. These findings establish a correlation between cellulose depolymerization and dielectric discharge behavior, providing a polymer-centered interpretation of aging mechanisms in ester-based oil–paper insulation systems. Full article

Oil leakage in oil-immersed power transformers poses a significant threat to grid reliability, potentially causing severe electrical accidents and environmental pollution if not detected in time. Detecting oil leakage outdoors, however, remains challenging due to the impact of weather conditions such as fog, humidity, and rain, which obscure the leakage signs and complicate real-time detection. To address these challenges, we propose a solution that integrates infrared thermal imaging with a CNN-SVM hybrid architecture. The core of this approach lies in shifting from traditional Softmax-cross-entropy-based empirical risk minimization (ERM) to maximum-margin-based structural risk minimization (SRM). A fully fine-tuned MobileNetV3 transforms low-contrast, boundary-softened infrared thermal images—often affected by fog and moisture—into a more discriminative high-dimensional feature space, where positive and negative samples become linearly separable. This is followed by replacing Softmax with a linear SVM and using hinge loss to enforce a margin constraint, which maximizes the classification margin and improves robustness to input perturbations. Experimental results show that our proposed method outperforms all compared models, achieving an accuracy of 0.990, significantly higher than ResNet50_BCE (0.908), EfficientNetB0 (0.925), YOLOv11n-CLS (0.930), and ViT (0.929). In terms of F1-Score (0.989) and AUC (0.995), MobileNetV3-SVM also demonstrates excellent performance, ensuring outstanding classification capability. Additionally, the model achieves an inference latency of only 6.3 ms, demonstrating excellent real-time inference performance, highlighting its potential for transformer oil monitoring applications. This research contributes to SDG 6 by preventing industrial water pollution resulting from transformer oil runoff, thereby protecting vital water sources in remote environments. Full article

In response to the key issues of complex internal structure, significant attenuation of partial discharge (PD) ultrasound signal propagation, and low sensor sensitivity in large oil–immersed power transformers, this paper analyzes the multi–order resonant mode vibration characteristics of the shaft–type fiber optic ultrasound sensor core structure. The displacement distribution patterns of the core structure in both transverse and longitudinal resonant modes are clarified. A strategy using oblique fiber winding rings is proposed to eliminate the problems of strain cancellation and non–accumulation of displacement in transverse and longitudinal resonant modes, which are common in traditional fiber optic ultrasound sensors with parallel fiber windings. Furthermore, design principles are provided to enhance the coverage of the free end and the high–strain regions with semi–high symmetry, as well as the vector–integrated response suitable for multi–order modes. Experimental results show that, in typical PD model detection, the oblique winding sensor exhibits a more prominent response near the high–order resonances of the core, with a detection sensitivity approximately 2.5 times higher than that of the parallel winding structure, and an overall sensitivity at least 7.4 times greater than that of traditional Piezoelectric (PZT) sensors. This demonstrates that the fiber winding method is a key design parameter determining the acoustic–solid coupling efficiency and high sensitivity performance of shaft–type fiber optic interferometric PD sensors, providing a feasible path for high–reliability fiber optic sensing solutions for online monitoring of transformer partial discharges. Full article

Winding damage is one of the most common and highly destructive faults in power transformers. To analyze the winding force and deformation under short-circuit conditions, this paper establishes a three-dimensional simulation model of a 220 kV oil-immersed power transformer. The force distribution of the windings under different short-circuit scenarios is investigated, and the vulnerable locations in different simulation model configurations are identified. The effects of variations in spacer blocks and tie bar quantities, as well as differences in material parameters of each component, on the evolution of weak-force regions are summarized. Finally, the influence of short-circuit cumulative effects on the maximum winding deformation is studied, providing a theoretical basis for transformer condition-based maintenance and fault prediction. Full article

Rising global electricity demand and the expansion of distribution networks require a critical assessment of component-level greenhouse gas contributions. Distribution transformers, although indispensable, have significant life-cycle carbon impacts due to the use of materials, manufacturing, and in-service losses. This study conducts a life-cycle assessment of a single-phase, 75 kVA oil-immersed distribution transformer manufactured in Newfoundland, one of the provinces with the cleanest, hydro-dominated grids in Canada, and evaluates it over a 40-year lifespan. Using a cradle-to-use boundary, the analysis quantifies embodied emissions from raw material extraction, manufacturing, and transportation, alongside operational emissions derived from empirically measured no-load and load losses. All the data are collected directly during the manufacturing process, ensuring high analytical fidelity. The energy efficiency of the transformer is analyzed in MATLAB version R2023b using measured no-load and load losses to generate efficiency, load characteristics under various operating conditions. Under varying load factor scenarios and based on Newfoundland’s 2025 grid intensity of 18 g CO₂e/kWh, the lifetime operational emissions are estimated to range from 0.19 t CO₂e under no-load operation to 4.4 t CO₂e under full-load conditions. A linear regression-based decarbonization model using Microsoft Excel projects grid intensity to reach net-zero around 2037, two years beyond the provincial target, indicating that post-2037 transformer losses will remain energetically relevant but carbon-neutral. Sensitivity analysis reveals that temporary overloading can substantially elevate lifetime emissions, emphasizing the value of smart-grid-enabled load management and optimal transformer sizing. Comparative assessment with fossil fuel-intensive provinces across Canada demonstrates the dominant influence of grid generation mix on life-cycle emissions. Additionally, refurbishment scenarios indicate up to 50% reduction in cradle-to-gate emissions through material reuse and oil reclamation. The findings establish a scalable framework for integrating grid decarbonization trajectories, life-cycle carbon modelling, and circular-economy strategies into sustainable distribution network planning and transformer asset management. Full article

Large oil-immersed power transformers are core equipment in power grids, and the use of robots for internal inspection can significantly enhance efficiency. However, existing research has primarily focused on the development of robotic bodies, neglecting the potential impact of their operation on the transformer’s oil–paper insulation system. This paper addresses this issue, evaluates the risk of underwater inspection robots colliding with internal structures, and finds that the maximum elongation rate of insulation paperboard at a speed of 0.1 m/s is far below the damage limit. Simultaneously, it analyzes the process by which propellers induce bubbles in oil, pointing out the need to optimize propeller design to ensure insulation safety. The study also extends the classical cavitation theory in water to the oil medium, reveals the conditions for gas generation by the propeller and the variation in the patterns of gas components (such as C₂H₂, H₂, etc.) through experiments, and discusses the gas source issue of cavitation in oil. Full article

Tip discharge in oil-immersed transformers poses a significant threat to insulation integrity. Conventional detection methods, such as gas and electrical analysis, are limited by slow response times or susceptibility to interference. Additionally, the lack of systematic comparisons between aged and fresh oil using multi-modal signal correlations hinders the development of accurate diagnostic strategies. To address this, a multi-modal sensing platform employing optical, UHF, and HFCT sensors, complemented by visual observation, was developed to investigate the evolution characteristics and mechanisms of tip discharge and to compare the detection effectiveness of these methods. Experimental results reveal that aged oil undergoes a novel four-stage evolution, where discharge signals first rise to a local peak, then experience suppression, followed by a dramatic surge, and finally decline slightly before breakdown. This process is governed by an “Impurity-Assisted Cumulative Breakdown Mechanism,” driven by impurity bridge growth and space charge effects, with signal transitions from ‘decoupling’ to synchronization. The optical sensor demonstrated superior sensitivity in early discharge stages compared to electrical methods. In contrast, fresh oil exhibited a “High-Field-Driven Stochastic Breakdown Mechanism,” with isolated pulses from micro-bubble discharges maintaining a metastable state until a critical threshold triggers instantaneous failure. This study enhances the understanding of how oil condition alters discharge mechanisms and underscores the value of multi-modal sensing for insulation condition assessment. Full article