Search Results (66)

This study designed and validated a dual-component beat-frequency quartz-enhanced photoacoustic spectroscopy (BF-QEPAS) gas detection system utilizing time-division multiplexing (TDM). By applying TDM to drive distributed feedback lasers, the system achieved the simultaneous detection of acetylene and methane. Its key innovation lies in exploiting the transient response of the quartz tuning fork (QTF) to acquire gas concentrations while concurrently capturing the QTF resonant frequency and quality factor in real-time. Owing to the short beat period and rapid system response, this approach significantly reduces time-delay constraints in time-division measurements, eliminating the need for periodic calibration inherent in conventional methods and preventing detection interruptions. The experimental results demonstrate minimum detection limits of 5.69 ppm for methane and 0.60 ppm for acetylene. Both gases exhibited excellent linear responses over the concentration range of 200 ppm to 4000 ppm, with the R² value for methane being 0.996 and for acetylene being 0.997. The system presents a viable solution for the real-time, calibration-free monitoring of dissolved gases in transformer oil. Full article

To enable the highly sensitive detection of acetylene (C₂H₂) dissolved in transformer oil, a high-power quartz-enhanced photoacoustic spectroscopy (QEPAS) sensing system is proposed. A standard 32.7 kHz quartz tuning fork (QTF) was employed as an acoustic transducer, coupled with an optimized acoustic resonator to enhance the acoustic signal. The laser power was boosted to 150 mW using a C-band erbium-doped fiber amplifier (EDFA), achieving a detection limit of 469 ppb for C₂H₂ with an integration time of 1 s. The headspace degassing method was utilized to extract dissolved gases from the transformer oil, and the equilibrium process for the release of dissolved C₂H₂ was successfully monitored using the developed high-power QEPAS system. This approach provides reliable technical support for the real-time monitoring of the operational safety of power transformers. Full article

Penta-graphene, a novel two-dimensional carbon allotrope entirely composed of pentagonal carbon rings, has attracted increasing attention due to its unique geometric structure, mechanical robustness, and intrinsic semiconducting nature. In this study, we systematically investigate the adsorption behavior of three typical dissolved gases in transformer oil (CO, C₂H₂, and C₂H₄) on penta-graphene using first-principles calculations based on density functional theory. The optimized adsorption configuration, adsorption energy, charge transfer, adsorption distance, band structure, density of states, charge density difference, and desorption time are analyzed to evaluate the sensing capability of penta-graphene. Results reveal that penta-graphene exhibits moderate chemical interactions with CO and C₂H₂, accompanied by noticeable charge transfer and band structure changes, whereas C₂H₄ shows weaker physisorption characteristics. The projected density of states analysis further confirms the orbital hybridization between gas molecules and the substrate. Additionally, the desorption time calculations suggest that penta-graphene possesses good sensing and recovery potential, especially under elevated temperatures. These findings indicate that penta-graphene is a promising candidate for use in gas sensing applications related to the monitoring of dissolved gases in transformer oils. Full article

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

Dissolved Gas Analysis (DGA) of transformer oil is a critical technique for transformer fault diagnosis that involves analyzing the concentration of gases to detect potential transformer faults in a timely manner. Given the issues of large model parameters and high computational resource demands in transformer DGA diagnostics, this study proposes a lightweight convolutional neural network (CNN) model for improving gas ratio methods, combining Knowledge Distillation (KD) and recursive plots. The approach begins by extracting features from DGA data using the ratio method and Multiscale sample entropy (MSE), then reconstructs the state space of the feature data using recursive plots to generate interpretable two-dimensional image features. A deep feature extraction process is performed using the ResNet50 model, integrated with the Convolutional Block Attention Module (CBAM). Subsequently, the Sparrow Optimization Algorithm (SSA) is applied to optimize the hyperparameters of the ResNet50 model, which is trained on DGA data as the teacher model. Finally, a dual-path distillation mechanism is introduced to transfer the efficient features and knowledge from the teacher model to the student model, MobileNetV3-Large. The experimental results show that the distilled model reduces memory usage by 83.5% and computation time by 73.2%, significantly lowering computational complexity while achieving favorable performance across various evaluation metrics. This provides a novel technical solution for the improvement of gas ratio methods. Full article

Aiming at improving the prediction accuracy of the gas dissolved in transformer oil which occurs with strong nonlinearity, this paper presents a method named CEEMDAN-PWOA-VMD-BIGRU for gas content prediction. First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is performed to decompose the original gas sequence. To solve the problem of the strong nonlinear characteristic of the decomposed high-frequency components leads to a large error in prediction, this paper uses Variational Mode Decomposition (VMD) for secondary decomposition. Though VMD can decompose high-frequency modes well, the selection of the optimal decomposition number and the quadratic penalty factors often depends on subjective judgment, which may affect the accuracy of decomposition results. Therefore, Whale Optimization Algorithm (WOA) is applied to optimize the parameter setting of VMD. However, the search of WOA in the optimization process is random, which leads to the limitations of the optimization efficiency. To solve this problem, this paper further uses Proximal Policy Optimization (PPO) to improve WOA (PWOA). With the optimized parameters of PWOA, VMD obtains more accurate secondary decomposition results. Then, the trained Bidirectional Gated Recurrent Unit (BiGRU) model is used to predict each decomposed component, and finally these predicted components are reconstructed to obtain more accurate prediction results. The experimental results demonstrate that the mean absolute error (MAE) of the proposed model is reduced by 6.88%, 7.45%, and 5.69%, compared with the traditional algorithms of Long Short-term Memory network (LSTM), Gated Recurrent Unit (GRU), and Temporal Convolution Network (TCN), respectively. Full article

Water-capped tailings technology (WCTT) is a strategy where oil sand tailings are sequestered within a mined-out pit and overlayed with a layer of water in order to sequester tailings with the aim that the resulting pit lake will support aquatic plants and organisms over time. The Base Mine Lake Demonstration (BML) is the first full-scale demonstration of a pit lake in the Athabasca Oil Sands Region (AOSR). In the BML, the release of methane from the fluid tailings influences several key processes, including the flux of greenhouse gases, microbial oxygen consumption in the water column, and ebullition-facilitated transport of organics from the fluid tailings to the lake surface. It is hypothesized that the residual low molecular weight hydrocarbons (LMWHCs) derived from diluent naphtha used during bitumen extraction processes are the carbon sources fueling ongoing microbial methanogenesis within the BML. The aims of this study were to identify the LMWHCs in the BML fluid tailings, to elucidate their sources, and to assess the extent of biogeochemical cycling affecting them. A headspace GC/MS analysis identified 84, 44, and 56 LMWHCs (C4–C10) present in naphtha, unprocessed bitumen ore, and fluid tailings, respectively. Equilibrium mass balance assessment indicated that the vast majority (>95%) of LMWHCs were absorbed within residual bitumen rather than dissolving into tailings pore water. Such absorbed compounds would not be readily available to in situ microbial communities but would represent a long-term source for methanogenesis. Chromatographic analysis revealed that most biodegradable compounds (n-alkanes and BTEX) were present in the naphtha but not in fluid tailings or bitumen ore, implying they are sourced from the naphtha and have been preferentially biodegraded after being deposited. Among the LMWHCs observed in bitumen ore, naphtha, and fluid tailings, C2-cyclohexanes had the highest relative abundance in tailings samples, implying their relatively high recalcitrance to in situ biodegradation. Full article

One of the key problems in the billet and shaped casting of aluminum alloys is the presence of various undesirable inclusions and impurities in the melt, which can serve as stress concentrators in the finished product, as well as dissolved hydrogen, which contributes to the formation of porosity. The interaction of aluminum with other gases produced by the combustion of fuel particles, oil, and paint materials brought into the furnace together with charge and scrap increases the amount of nitrides, oxides, carbides, and sulfides in the melt. Flux treatment is widely used as protection of aluminum alloys from oxidation and removal of impurities. The present paper reports the data of a comparative analysis of five widely used flux compositions based on sodium, potassium, and magnesium chlorides. The study covers the following aspects: chemical composition, moisture content, melting temperature and melting range, particle size distribution, and refining ability as measured by the change in Na, Ca, and H₂ content after melt treatment. Full article

The representative dissolved gases analysis (DGA) method for transformer fault detection faces many shortcomings in early fault diagnosis, which restricts the application and development of fault detection technology in the field of transformers. In order to diagnose early failure in time, fluorescence analysis technology has recently been used for the research of transformer failure diagnosis, which makes up for the shortcomings of DGA. However, most of the existing fluorescence analyses of insulating oil studies combined with intelligent algorithms are a qualitative diagnosis of fault types; the quantitative fault diagnosis of the same oil sample has not been reported. In this study, a typical fault simulation experiment of the interval discharge of insulating oil was carried out with the new Xinjiang Karamay oil, and the fluorescence spectroscopy data of insulating oil under different discharge durations were collected. In order to eliminate the influence of noise factors on the spectral analysis and boost the accuracy of the diagnosis, a variety of spectral preprocessing algorithms, such as Savitzky–Golay (SG), moving median, moving mean, gaussian, locally weighted linear regression smoothing (Lowess), locally weighted quadratic regression smoothing (Loess), and robust (RLowess) and (Rloess), are used to smooth denoise the collected spectral data. Then, the dimensionality reduction techniques of principal component analysis (PCA), kernel principal component analysis (KPCA), and multi-dimensional scale (MDS) are used for further processing. Based on various preprocessed and dimensionally reduced data, transformer failure diagnosis models based on the particle swarm optimization algorithm (PSO) and the secretary bird optimization algorithm (SBOA) optimized BPNN are established to quantitatively analyze the state of insulating oil and predict the durations of transformer failure. By using the mathematical evaluation methods to comprehensively evaluate and compare the effects of various algorithm models, it was found that the Loess-MDS-SBOA-BP model has the best performance, with its determination coefficient (R²) increasing to 99.711%, the root mean square error (RMSE) being only 0.27144, and the other evaluation indicators also being optimal. The experimental results show that the failure diagnosis model finally proposed in this paper can perform an accurate diagnosis of the failure time; the predicted time is closest to the true value, which lays a foundation for the further development of the field of transformer failure diagnosis. Full article

As a critical component in voltage–current conversion and power transmission within HVDC systems, the risk assessment of converter transformers plays a significant role in ensuring their operational safety and enhancing the reliability of the power supply. To address the issues of the incomplete characteristic parameters and limited fault data used for model training in existing transformer evaluation models, this paper develops a risk assessment model for converter transformers based on the entropy-weighted analytic hierarchy process (AHP). Firstly, in accordance with relevant standards in the power industry and existing experimental research, 14 ‘electrical–thermal–mechanical’ multi-dimensional characteristic parameters, including partial discharge, dissolved gases in oil, and hot spot temperature rise, are selected to effectively reflect the risk state of converter transformers. The risk state is then categorized into four levels. Next, the AHP, which uses a subjective weighting method, is combined with the entropy-weight method, an objective weighting approach, to construct the risk assessment model for converter transformers based on the entropy-weighted AHP. Finally, the effectiveness of the model is validated through four case studies of converter transformers. The results indicate that the risk assessment model proposed in this paper can accurately and effectively reflect the risk state of transformers at different levels, providing valuable guidance for the development of maintenance strategies for converter transformers. Full article

The prediction of dissolved gas concentrations in oil can provide crucial data for the assessment of power transformer conditions and early fault diagnosis. Current simulations mainly focus on the generation and accumulation of characteristic gases, lacking a global perspective on gas diffusion and dissolution. This study simulates the characteristic gases produced by typical faults at different flow rates. Using ANSYS 2022 R1 simulation software, a gas–liquid two-phase model is established to simulate the flow and diffusion of characteristic gases under fault conditions. Additionally, a fault-simulation gas production test platform was built based on a ±400 kV actual converter transformer. The experimental data show good consistency with the simulation trends. The results indicate that the diffusion of dissolved gases in oil is significantly affected by the oil flow velocity. At higher flow rates, the characteristic gases primarily move within the oil tank along with the oil circulation, leading to a faster rate of gas dissolution in oil and a shorter time to reach equilibrium within the tank. At lower flow rates, the diffusion of characteristic gases depends not only on oil flow circulation but also on self-diffusion driven by concentration gradients, resulting in a nonlinear change in gas concentration across various monitoring points. Full article

Complex geological, gas geochemical and hydro meteorological studies were conducted to investigate the methane fields present in the bottom sediments and seawater of the Red River and Phu Khanh sedimentary basins. We demonstrate that the system of tectonic faults that formed the sedimentary basins of the Red River and the Phu Khanh (the eastern shelf and slope of Vietnam) created the necessary conditions for the generation and migration of endogenous methane into the bottom sediments and seawater. It is shown that dissolved methane in seawater can be transported by marine currents, which in turn can be influenced by seasonal and irregular synoptic processes. The research shows that part of the dissolved methane contained in the waters above the Ken Bau gas field can be transported to the south by the coastal Vietnamese current, which adapts to the conditions of the winter northeast monsoon. It is concluded that there could be at least two deep sources of hydrocarbon gas emissions in the Phu Khanh basin. The impact of Typhoon Nakri on the transport of dissolved methane in the water column of the Phu Khanh sedimentary basin has been investigated. The typhoon could create favorable hydrodynamic conditions for the movement of dissolved gases from oil and gas deposits near the coasts of the islands of Kalimantan and Palawan to the Phu Khanh basin. A possible route for this transfer has been identified. Full article

Dissolved gas analysis (DGA) is a vital method for the online detection of transformer operation state. The adsorption performance of a SnP₃ monolayer modified by transition metal Cr regarding six characteristic gases (CO, C₂H₄, C₂H₂, CH₄, H₂, C₂H₆) dissolved in oil was studied. The study reveals the relevant adsorption and gas-sensing response mechanisms through calculations of the adsorption energy, density of states, differential charge density, energy gap, and recovery time. The results display a considerable increase in the adsorption effect of the Cr-SnP₃ monolayer on six gases. The CO, C₂H₂, and C₂H₄ gases lead to chemical adsorption, and the CH₄, H₂, and C₂H₆ gases lead to physical adsorption. Combined with the recovery time, the Cr-SnP₃ monolayer has a strong adsorption effect on CO and C₂H₂ gases at normal temperatures and even high temperatures, and the adsorption is stable. C₂H₄ gas can be rapidly desorbed from the Cr-SnP₃ monolayer at 398 K. Therefore, the Cr-SnP₃ monolayer can be expected to serve as a CO and C₂H₂ gas adsorbent and a resistive gas sensor for C₂H₄ gas. This research offers a theoretical foundation for the development of the Cr-SnP₃ monolayer in gas-sensitive materials. Full article

Multi-component thermal luid technology optimizes development effects and has a strong adaptability, providing a new choice for the efficient development of heavy oil reservoirs. However, due to the significant differences between the phase behavior of multi-component thermal-fluid-heavy oil systems and conventional systems, and the lack of targeted and large-scale research, key issues such as the phase behavior of these systems are unclear. This research studies the phase behavior and influencing factors of emulsions and foamy oil in a multi-component thermal-fluid-heavy oil system through high-temperature and high-pressure PVT experiments, revealing the characteristics of the system’s special phase behavior. In the heavy oil emulsion system, the water content directly affects changes in the system’s phase state. The higher the temperature, the larger the phase transition point, and the two are positively correlated. As the stirring speed increases, the phase transition point first increases and then decreases. The amount of dissolved gas is negatively correlated with the size of the phase transition point, and dissolution can form foamy oil. In the heavy oil–foamy oil system, the dissolution capacity of CO₂ is greater than that of multi-component gases, which is greater than that of N₂. A high water content and high temperature are not conducive to the dissolution of multi-component gases. While an increase in stirring speed is beneficial for the dissolution of gases, there are limitations to its enhancement ability. Therefore, the development of multi-component thermal fluids should avoid the phase transition point of emulsions and promote the dissolution of multi-component gases. Full article

Using first-principles theory, this work purposes Ru-doped Janus WSSe (Ru-WSSe) monolayer as a potential gas sensor for detection of three typical gas species (CO, C₂H₂, and C₂H₄), in order to evaluate the operation status of the oil-immersed transformers. The Ru-doping behavior on the WSSe surface is analyzed, giving rise to the preferred doping site by the replacement of a Se atom with the formation energy of 0.01 eV. The gas adsorption of three gas species onto the Ru-WSSe monolayer is conducted, and chemisorption is identified for all three gas systems with the adsorption energy following the order: CO (−2.22 eV) > C₂H₂ (−2.01 eV) > C₂H₄ (−1.70 eV). Also, the modulated electronic properties and the frontier molecular orbital are investigated to uncover the sensing mechanism of Ru-WSSe monolayer upon three typical gases. Results reveal that the sensing responses of the Ru-WSSe monolayer, based on the variation of energy gap, to CO, C₂H₂, and C₂H₄ molecules are calculated to be 1.67 × 10⁶, 2.10 × 10⁵, and 9.61 × 10³, respectively. Finally, the impact of the existence of O₂ molecule for gas adsorption and sensing is also analyzed to uncover the potential of Ru-WSSe monolayer for practical application in the air atmosphere. The obtained high electrical responses manifest strong potential as a resistive sensor for detection of three gases. The findings hold practical implications for the development of novel gas sensing materials based on Janus WSSe monolayer. We anticipate that our results will inspire further research in this domain, particularly for applications in electrical engineering where the reliable detection of fault gases is paramount for maintaining the integrity and safety of power systems. Full article