Search Results (318)

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

This paper systematically investigates the failure characteristics and mechanisms of insulating materials in DC voltage dividers under combined high-frequency voltage and high-temperature conditions via simulations and experiments. The results showed that high-frequency harmonics severely degrade the insulation strength of polypropylene/paper/polypropylene (PPLP) at 10 kHz, in which the bulk breakdown strength of PPLP decreases by over 50%. Furthermore, the surface flashover voltage in oil is reduced by 17.7% under high-frequency voltage alone, and by as much as 51% when white flocculent substances are present in the oil. The dielectric properties of PPLP strongly depend on frequency and temperature, which aggravate the heat accumulation of the divider under high-frequency voltage. Furthermore, the multilayer structure of PPLP introduces deeper trap levels due to interfacial states, which reduce the breakdown strength and flashover voltage of PPLP. Electro-thermal coupling induces a rapid temperature rising to 98 °C at 25 kHz caused by dielectric loss, leading to oil turbidity and white precipitation, consistent with finite element simulations. Consequently, a failure mechanism is proposed as follows: prolonged electro-thermal stress causes chain scission in styrene-containing materials, releasing monomers that repolymerize into white polystyrene deposits. Their porous structure and dielectric mismatch distort the interfacial field, trigger partial discharge, and aggravate surface flashover. Full article

Nanofluids have emerged as promising candidates for enhancing the dielectric and thermal performance of insulating liquids used in power transformers. While numerous studies report significant improvements in breakdown voltage (up to +10–40%) and thermal conductivity, the underlying mechanisms remain only partially understood and often contradictory, particularly with respect to long-term stability and ageing behavior. This paper presents a comprehensive and critical review of nanofluids applied to transformer insulation, adopting a system-level approach focused on the oil–paper insulation system. The analysis reveals that the reported performance strongly depends on key parameters such as nanoparticle concentration, dispersion quality, and experimental conditions, leading to significant inter-study variability. Dielectric improvements are shown to be maximized within narrow concentration ranges and may deteriorate due to nanoparticle aggregation, while thermal enhancements are often accompanied by increased viscosity, resulting in a thermo-hydraulic trade-off. Furthermore, this review highlights major contradictions in the literature, including the paradoxical relationship between electrical conductivity and dielectric strength, as well as the unclear impact of nanofluids on cellulose ageing. The findings demonstrate that performance observed at the fluid level cannot be directly extrapolated to real transformer conditions without considering the complex interactions between nanoparticles, oil, cellulose, and moisture. To address these limitations, a conceptual framework termed Nano-Modified Composite Insulation (NMCI) is proposed. This model provides a unified description of multiphase interactions and offers a basis for a more realistic evaluation of nanofluids under operational conditions. This work emphasizes the need for standardized experimental methodologies and long-term studies and provides clear research directions toward the development of reliable and industrially applicable nanofluid-based insulation systems. Full article

In order to achieve more accurate and efficient oil-paper insulation aging assessment, and to improve the operation and maintenance level of oil-paper insulated power equipment, this paper proposes an aging evaluation method of oil-paper insulation based on Raman spectrum and frequency-domain spectroscopy. First, oil-paper insulation samples with different aging degrees were prepared by an accelerated thermal aging test in this experiment. Then, Raman spectroscopy and frequency-domain dielectric spectroscopy were used to examine the samples and analyze the aging characteristics of the samples by LightGBM R2019b. Finally, the gray neural network is used to establish a prediction model for the degree of polymerization of insulating paper based on frequency-domain dielectric features and Raman spectral features. The results of this study showed that there is a certain correlation between the Raman characteristics of insulating oil and the FDS characteristics of insulating paper. The average absolute error of the prediction of the R-F-PGNN model developed in this paper is 20.4. The research in this paper provides a strong support for the development of Raman spectroscopy diagnosis technology for oil-paper insulation aging in the power industry, which has certain academic value and engineering application significance. Full article

Partial discharge detection is a key technology for maintaining the normal operation of industrial power equipment. Oil-impregnated paper bushings are crucial components connecting transformers to the power grid. Insulation degradation leads to partial discharge, posing a significant threat to power system operation. Developing on-line diagnostics for partial discharge in transformer bushings and automatic identification of insulation defects can effectively protect system and personnel safety. Due to limitations of small sample sizes and lightweight networks, this study combines interpolation techniques with a lightweight convolutional neural network to improve identification accuracy. This network uses interpolation to maintain the undistorted sample signal from the initial input and reduces training defects from a small sample size. The neural network extracts partial discharge features to determine the defect type and its cause. This study uses a publicly available dataset with discharge signals from generators. Although from a different source from the discharge signals generated by oil-impregnated paper bushings, the signal distribution is similar, allowing for a fair analysis and providing a reference for evaluating discharge signals obtained from oil-impregnated paper bushings or other discharge devices. The experimental results show that the accuracy of this network improved from 97% to over 99% while maintaining low computational complexity and excellent real-time performance. Furthermore, this network was implemented and validated on existing industrial equipment. 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

Micro-electro-mechanical systems (MEMS) sensors offer the benefits of compact size, lightweight design, and low cost, which has led to widespread use in consumer electronics, vehicles, healthcare, defense, and communications. As their performance has improved, MEMS sensors have also found applications in oil exploration and geophysical studies. Pressure and temperature measurements during hydraulic fracturing have long been employed to improve downhole conductivity during oil and gas extraction. Nevertheless, the development of high-precision MEMS sensors for oil exploration remains an active area of research. This paper presents the design, fabrication, packaging, and characterization of a silicon-on-insulator (SOI) MEMS piezoresistive pressure sensor integrated with a temperature sensor. It also describes the design of a chamber intended to emulate conditions at the bottom of oil exploration wells. The sensors were successfully designed and fabricated on the basis of physics-based simulations, deep reactive ion etching and anodic bonding. The pressure sensors, together with the signal-conditioning system, exhibited a linear response with a sensitivity of 0.0268 mV/V/MPa and maximum hysteresis of 5.3%. Full article

The reliability of power transmission and distribution depends on the proper functioning of power transformers, which use conventional mineral oil as an insulating fluid. The lower fire class and biodegradability of mineral oil have led to a shift towards second-generation oils from vegetable and plant crops. Ester fluids provide a better performance in combination with solid pressboard/paper insulation, increasing the lifetime of power transformers compared to those using mineral oil. Considering the need for sustainability in the near future, second-generation oils are no longer feasible, and hence, third-generation oils derived from microalgae species are suitable alternative fuels for the energy sector. The fatty acid methyl ester (FAME) content of algae is similar to that of biodiesel, making it a suitable fluid for power transformers. A detailed overview of third-generation feedstock (algae) for power transformer applications is provided, focusing on the extraction of algal oil, in conjunction with safety precautions and its fatty acid content, and a comparison with conventional vegetable and plant-based oils is presented. Various properties of algal oil (fatty acid composition, kinematic viscosity, oxidation stability, breakdown voltage, etc.) are analyzed to assess its suitability as a transformer fluid. This review article comprehensively analyzes the current research landscape surrounding the use of algal oil as an insulating fluid in transformers. It critically evaluates both the potential advantages and the unique challenges associated with this alternative to conventional mineral oil and second-generation vegetable and plant-based oils. Full article

The aging of oil-paper insulated transformer components significantly impacts their service life. Accurate health assessment is crucial for predicting failure rates and residual life, which is vital for ensuring operational safety. This paper employs the bathtub curve concept and Weibull distribution to fit collected oil-paper insulated transformer failure rate data, obtaining the failure rate curve. Considering operational environment and load factors, a health index model is established for residual life prediction. By optimizing the weight and bias parameters of the backpropagation (BP) neural network using an adaptive chaotic sequence strategy, a multi-parameter correlated transformer life prediction model is constructed. A cross-validation mechanism is introduced to enhance the model’s generalization ability. Experimental results from training and testing demonstrate that the proposed method achieves higher prediction accuracy, with average errors of 5.36% for annual failure rate and 3.32% for residual life, confirming its effectiveness and applicability in transformer life prediction. Full article

To address the stringent insulation safety requirements of modern high-voltage transformers, accurately characterizing the transient electric field is critical. However, a significant problem remains: current engineering models typically rely on static capacitive distributions, failing to capture the dynamic electric field distortion induced by rapid space charge injection under lightning impulses. Therefore, a non-contact spatial electric field measurement method based on the optical Kerr effect was employed to analyze the influence of electrode material, voltage amplitude, and wavefront time. Unlike traditional simulation models that often assume constant mobility and focus solely on the shielding effect, this study reveals a non-monotonic electric field evolution driven by a ‘Static-Dynamic’ mode transition. The proposed model highlights two critical breakthroughs: (1) Mechanism Innovation: It experimentally verifies that charge injection is governed by the ion charge-to-mass ratio rather than just the work function, leading to a newly identified field enhancement phase during the wavefront that overcomes the limitations of capacitive models that underestimate transient stress. (2) Parameter Quantification: Precise spatiotemporal thresholds are established—negative charges traverse the gap within ~200 ns, while positive charges require ~10 μs to reach equilibrium. These findings provide experimentally calibrated time constants for simulation correction and offer new criteria for optimizing electrode materials in UHV transformers to mitigate transient field distortion. 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

An increasing number of different types of dielectric liquids are appearing on the market. This is undoubtedly related to sustainable development goals. This paper presents comparative studies of the lightning impulse breakdown voltage (LIBV) of six dielectric liquids with different chemical compositions: naphthenic uninhibited mineral oil (UMO), naphthenic inhibited mineral oil (IMO), natural ester (NE), synthetic ester (SE), bio-based hydrocarbon (BIO), and an inhibited liquid produced using gas-to-liquids technology (GTL). Tests were conducted in a point-to-sphere electrode configuration with a 5 mm thick pressboard barrier placed between them. This configuration was designed to more closely replicate the actual configuration found in transformers, where the oil channels are separated by pressboard barriers. Tests were performed for two inter-electrode gap distances of 25 mm and 40 mm, and for both lightning impulse voltage polarities. The pressboard barrier was placed so that the distance between point electrode and the barrier was always the same (10 mm). Measurements were performed using the step method. Before measurements began, the pressboard barrier was impregnated with the dielectric liquid being tested. The obtained measurement results were compared with previous studies conducted by the authors, which used a similar electrode system but without the pressboard barrier. The results confirmed that inserting the pressboard barrier between the electrodes effectively inhibits development of discharges and significantly increases the electrical strength of the entire insulation system. Full article