Gas Concentration In Oil Research Articles

Diffusion Characteristics of Dissolved Gases in Oil Under Different Oil Flow Circulations

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.

Prediction of dissolved gas concentration in transformer oil considering data loss scenarios in power system

The trend prediction of dissolved gas concentration in transformer oil can provide basis for transformer fault diagnosis, which is of vital significance to the safe operation of power system. However, due to the inevitable malfunction of monitoring equipment, it is difficult to collect all needed data in actual operation scenarios. Therefore, a method for predicting dissolved gas concentration in transformer oil for data loss scenarios is proposed based on Bayesian probabilistic matrix factorization (BPMF) and gated recurrent unit (GRU) neural network. Firstly, aiming at the problem of data loss in actual monitoring of dissolved gas in oil, BPMF is used to fill in the missing data. Then, a GRU neural network model is established to predict the trend of dissolved gas concentration in oil. Finally, the hyperparameters of the prediction model are selected and optimized by Bayesian theory. The examples show that this method can effectively fill in the missing part of the measured data. Compared with traditional prediction methods, the proposed method has higher prediction accuracy.

Classification of Cellulosic Insulation State Based on Smart Life Prediction Approach (SLPA)

The state of cellulosic solid kraft paper (CSKP) insulation, to a large extent, is an indication of a transformer’s health. It not only reflects the condition of transformer but also diagnose its residual life. The quantity of 2-furfuraldehyde (2-FAL), carbon dioxide (CO2), and carbon monoxide (CO) dissolved in the transformer oil are useful diagnostic indicators to predict the state of the CSKP insulation. In this work, the current physical state of the CSKP is determined with the help of easily measurable parameters, like temperature, moisture, and the aging time. Here, the degree of deterioration of CSKP insulation has been determined using an integrated insulation health assessment system. This technique integrates a two-stage system comprising of a neural network (NN) model followed by a Smart Life Prediction Approach (SLPA). A thermo-moisture-aging multi-layer feed-forward NN model has been developed to predict the concentrations of 2-FAL, CO2, and CO, which are further correlated to estimate the Degree of Polymerization (DP) values adopting an SLPA. The advantage of the proposed integrated system is that it provides an alternative means of paper health assessment based on Dissolved Gas Analysis (DGA) without estimating dissolved gas concentrations in oil, thereby avoiding the use of sophisticated measuring instruments. The optimal configuration of the NN model has been achieved at minimum iterations with an average cross-validation mean square error of 3.78 × 10−7. The proposed system thereby avoids destructive and offline measurement of DP and facilitates real-time condition monitoring of oil-immersed transformers. The test results of the developed system show considerable reliability in determining insulation health using easily measurable parameters. Furthermore, the system’s performance is compared with reported work and has been found to provide encouraging outcomes.

Optical fiber evanescent-wave sensing technology of hydrogen sulfide gas concentration in oil and gas fields

For the problems of light source fluctuation noises, zero drift and other problems of H2S gas concentration sensor in optic-fiber evanescent-wave absorption spectroscopy, double-light-path absorption system was designed to theoretically analyze the relationship between evanescent-wave power and gas concentration as well as detection sensitivity. Moreover, the relationship between evanescent-wave power and optical-fiber residual diameter in the optic fiber corrosion process was also studied. The sensing probes with different evanescent-wave power ratios were used to detect experiments of H2S gas concentration. The experimental results show that the sensitivity is up to 0.169μw/ppm when the evanescent-wave power ratio of sensing probe is 10%, detected minimum H2S gas concentration is 5.1ppm, the detection repeatability is good and the response time is only 2.03s. Due to low production cost, high sensitivity and short response time, the optic-fiber evanescent-wave sensor can be used for real-time on-line measurement of H2S gas in the area of the hydrocarbon exploration and development.

Experimental Simulation on Oil Gas Spreading in the Complex Confined Spaces

Abstract In the process of mass production, usage, transport and storage of petroleum products, it would be occurred any time that the oil products leak to generate oil gas spreading which leads to serious accidents in fire and explosion safety. The cause and damage extent of oil gas explosion hazards in a confined space is not only limited to ambient temperature, humidity and other environmental conditions, but also depends on the speed, extent and concentration distribution of the oil gas spreading. In this paper, the special phenomenon, spreading law, the characteristic parameter and the main influencing factors of oil gas spreading in the complex confined spaces are studied and analyzed from the simulation experiments. The results show that the oil gas spreading process in the complex confined spaces can be divided into three stages, namely, the gravity settling stage, adherent diffusion stage and passive mass transfer stage. Many factors, such as the cross–section shape, wall roughness, ratio between length and diameter and so on, will directly affect the oil gas spreading in the complex confined space. The oil gas concentration in the outlet can reach up to about 51.28% of that in the inlet. Meanwhile, the oil gas concentration of the leakage source has great impact on the spreading process, and higher concentration will accelerate the spreading process.

Diagnostics of transformer oil by elastic waves

In the diagnostics of oil-immersed transformers, a chemical analysis of generated gases in the oil is generally performed, evaluating the kinds of gases and their quantities. This method, however, involves numerous procedures and is difficult to use in the field to diagnose the deterioration of oil. Considering the current state of the advanced information society, no deterioration in power apparatus should be permitted and the diagnosis of deterioration must be performed reliably while the equipment is energized. Therefore, we attempted to diagnose insulation oil based on analyses of elastic waves produced by a pulse discharge in the oil. This technique uses the behavior of elastic waves in liquid, in which an incident wave causes a high-frequency oscillation component with increasing gas content. In this investigation, elastic waves were detected on the outside surface of an oil tank at gas concentrations of 0 to 10%. The detected waves were analyzed by the fast Fourier transform. It was found that frequency spectra of 400 to 1000 kHz were increasingly evident when the gas concentration in oil was higher than ∼5%. This indicates that our new technique is useful for the diagnostics of oil-immersed transformers using the oscillation behavior of elastic waves produced by bubbles in oil. © 2001 Scripta Technica, Electr Eng Jpn, 135(1): 1–7, 2001