Transformer Oil Research Articles

Adsorption of dissolved gases (CO, H2, CO2) produced by partial discharge in converter transformer oil by CuO(1, 2) and Ag2O(1, 2) clusters doped with MoTe2: A DFT study

Density functional theory (DFT) was used to analyze the effect of (CuO)n, (Ag2O)n (n=1, 2) cluster-doped MoTe2 on the three gases (CO, H2 and CO2) adsorption performance and characteristics. MoTe2 has large bond length and low binding energy, which can simultaneously achieve high sensitivity to target molecules and excellent reversibility at room temperature. By analyzing the modified MoTe2, compared with the initial MoTe2, the reduced energy band structure of band gap, the increased adsorption energy, more charge transfer, and the increased conductivity of DOS, all reflect that the doping of metal oxides enhances the adsorption and sensing performance of MoTe2 for the three gases. The adsorption capacity of CuO-MoTe2 and (Ag2O)2-MoTe2 for the three gases is: CO>H2>CO2, while the adsorption capacity of (CuO)2-MoTe2 and Ag2O-MoTe2 for the three gases is: CO>CO2>H2. Finally, the adsorption effects of the four modifications were compared and feasibility analyzed through molecular orbitals, recovery time, sensitivity and work function, and corresponding conclusions were drawn. The results of this study provide a theoretical basis for detecting dissolved gases in oil generated by partial discharge of converter transformers.

Research on the online monitoring technique for transformer oil level based on ultrasonic sensors

AbstractIn order to solve the problem of remote oil level measurement for unmanned transformers, this paper proposes an online monitoring technology for transformer oil level based on ultrasonic sensors. Subsequently, a finite element model and experimental testing platform were constructed to analyse and verify the influencing factors of the transformer oil level sensors. The results indicated that there is a lower measurement error at 140 kHz in the ultrasonic frequency range of 20–320 kHz, which can be selected as the recommended frequency. The impurities in the oil and the thickness of the tank wall have a slight impact on the accuracy of oil level measurement, and can lead to a decrease in the signal‐to‐noise ratio of the echo. Furthermore, as the speed of sound increases by about 4 m/s, for every 1°C increase in oil temperature, it is necessary to calibrate the measurement results based on the oil temperature. The test results of actual experimental transformers showed that the designed online monitoring device can achieve high‐precision monitoring of transformer oil level, with a relative error generally less than 3%.

BiPLS-RF: A hybrid wavelength selection strategy for laser induced fluorescence spectroscopy of power transformer oil

In this paper, a method for indirect diagnosis of transformer faults based on the fluorescence spectrum and characteristic wavelength screening of transformer oil has been proposed. Specifically, a hybrid strategy (BiPLS-RF) for establishing the fluorescence spectrum feature screening of transformer oil using backward interval partial least squares (BiPLS) and random forest (RF) has been proposed. Aiming at the problem of transformer fault diagnosis, the laser induced fluorescence (LIF) spectroscopy of transformer oil in different states was first collected, and it is found that the fluorescence spectrum intensity of normal transformer oil was stronger than that of faulty transformer oil. Then the characteristic bands of the original fluorescence spectra were screened by BiPLS. It is found that when the original fluorescence spectra were divided into 15 sub-intervals, the minimum root mean squares error of cross-validation can be obtained by selecting 3 sub-intervals (including 411 wavelengths). On this basis, RF was employed to further screen the characteristic wavelengths and realized the identification of the fluorescence spectrum. It is found that in the RF model composed of 54 trees, the selected 196 characteristic wavelengths of the fluorescence spectrum can minimize the analysis error (0.56%). In addition, the selected characteristic wavelength information was fed into other common classifiers to construct a fluorescence spectrum identification model, which further proved the effectiveness of BiPLS-RF for wavelength selection for LIF spectroscopy of power transformer oil. The results show that it is feasible to use BiPLS-RF to screen the characteristic wavelength of LIF spectroscopy and apply it to transformer fault diagnosis, which provides a new solution for transformer fault diagnosis.

New Short-Circuited Coaxial Method Implementation for Complex Permittivity Measurement of Power Transformer Oils

The investigation into the dielectric properties of transformer oils has been a focal point in both historical and contemporary electric insulation technology as high-voltage applications have greatly benefited from the continuous research efforts in this field. The dielectric permittivity is one of the factors to be negotiated when discussing the electric insulation of any material. This work aims to showcase a new short-circuited coaxial cable method to evaluate the complex dielectric permittivity of palm, sunflower and rapeseed oil and review other previously used measurement techniques. Mainly, the test was performed using a sample holder that represents a short-ended coaxial cable filled with the test material which is connected to one port of a vector network analyser. The measurements of the input impedance for various oil samples were conducted at frequencies ranging from 1 MHz to 10 MHz. The technique uses the value of the input impedance which depends on the reflected signals from the test sample to the network analyser, consequently, the value of the dielectric permittivity and dielectric loss have been calculated using the short circuit impedance formula which involves numerical methods to get the results which shows that this method can be used as an alternative to investigate the oils insulation parameters as it provides smooth results for permittivity without any divergence, ensuring more reliable and consistent measurements as well as providing high accuracy in determining the permittivity of materials.

Ti3C2Tx (T = F, O, OH) as a sensor for dissolved gas in transformer oil: A theoretical study

Power transformer is the hub of power grid energy transmission, and its safe and reliable operation is very important to power grid. In this paper, based on density functional theory, Ti3C2Tx (T = F, O, OH) material was used as sensing material. The adsorption models of Ti3C2Tx and dissolved characteristic gases (H2, CH4, C2H4, C2H6, C2H2, CO, and CO2) in transformer oil are constructed. The adsorption distance, bond length change, adsorption energy, charge transfer and density of states (DOS) of the adsorption system were analyzed. The results showed that the gases can adsorb on the surface of Ti3C2Tx spontaneously. Specifically, CO2, CO, C2H2 and C2H4 exhibit a strong adsorption effect on Ti3C2(OH)2, forming hydrogen bonds or chemical bonds, accompanied by large charge transfer and adsorption energy, indicating Ti3C2(OH)2 shows a good sensing performance for these gases.

Investigation on the combined effect of the hydrogen premixing and nanoparticle mixed pyrolysed oil of transformer oil waste in engine characteristics

In the world, the waste of one situation is the perfect requirement for the other situation. The value addition of the waste can produce new products which contribute to different applications. The electrical grid system produced the transformer oil wastes based on their use and capacity. This waste can be converted into useful applications in the CI engine by pyrolysis at 480 °C. This Pyrolysed oil of transformer oil waste (POTOW) is blended with diesel as a 1:3 contribution by volume for improved fuel properties. But it produced lesser performance. Therefore, the nanoparticles of Titanium dioxide and Silicon dioxide are added to the blend to generate the nano fuel. Enhanced performance and emission reduction are tried with 2–6 lpm hydrogen induction with the nano fuel in a 5.2 kW CI engine. Even though nanofuel produced better results, hydrogen induction was used to increase performance with reduced engine emissions further. At full load, 6 lpm hydrogen induction with the nanofuel produced 10.4% higher brake thermal efficiency, nearly 40% drop in CO, 39% drop in the UHC, and 22.22% drop in the smoke emission but 28% higher NOx emission compared to the petroleum diesel. Therefore, this combination is recommended for the CI engine operation with higher performance with reduced emissions and the transformer oil waste reduction in the environment of this application.

New Insights into Gas-in-Oil-Based Fault Diagnosis of Power Transformers

The dissolved gas analysis of insulating oil in power transformers can provide valuable information about fault diagnosis. Power transformer datasets are often imbalanced, worsening the performance of machine learning-based fault classifiers. A critical step is choosing the proper evaluation metric to select features, models, and oversampling techniques. However, no clear-cut, thorough guidance on that choice is available to date. In this work, we shed light on this subject by introducing new tailored evaluation metrics. Our results and discussions bring fresh insights into which learning setups are more effective for imbalanced datasets.

Adsorption of dissolved gases on the TM (Pt, Pd) doped XP(X=In, Ga): A DFT study

Using 2D materials to detect dissolved gases in transformer oil can effectively evaluate the operating status of power systems. In this study, the Pt and Pd atoms are used to modify InP and GaP monolayers for detecting the target gases (C2H2, C2H4, CH4). The band gaps of modified structures are reduced and became more compact compared to the original substrates. The adsorption characteristics of the target gases on the modified substrates are systematically analyzed through adsorption parameters, DCD and DOS. The adsorption effect of the modified InP monolayers on the target gases is ranked as: C2H2>C2H4>CH4. The adsorption capacity of the modified GaP monolayers to the target gases is: C2H4>C2H2>CH4, however the adsorption capacity of the Pd-GaP/gases system is consistent with that of the modified InP monolayers. The sensitivity and recovery time of every gas adsorption systems are further analyzed. In addition, this study provides a theoretical direction for exploring the application prospects of InP and GaP monolayers in the evaluation of operating conditions of oil-immersed transformers.

Study on the partial discharge characteristics induced by the motion of cellulose particles in transformer oil

AbstractCellulose particles present a significant concern within the oil‐paper insulation of transformers, posing potential risks to insulation performance. Under the influence of the electric field, the movement of cellulose particles can compromise the transformer's insulation, leading to potential failure. An experimental platform was established to synchronously record particle motion images, partial discharge (PD) pulses, and electric voltage waveforms in oil, aiming to observe the PD characteristics resulting from particle motion under alternating current (AC) voltage and investigate the relationship between different particle motion modes, motion positions, and PD signals. The findings reveal that the phase distribution of PD signals is correlated with the particle motion mode. Specifically, the phase distribution of PD pulses during the back‐and‐forth motion mode is between 4°–94° and 182°–275°. In the suspended oscillation motion mode, the PD pulses phase is concentrated between 20°–84° and 203°–268°. The generation of PD pulses is closely linked to the particle's motion position. PD pulses occur when the particle remains on the electrode during the back‐and‐forth motion mode, generally, PD pulses rarely occur during the jumping process between the two electrodes. In the suspended oscillation motion mode, PD pulses occur when the particle moves upward, but generally do not occur during downward movement. Furthermore, the Pulse Sequence Analysis technique was used to employ the PD characteristics caused by particle motion in transformer oil. The simulation calculations of the electric field distribution for two different particle motion modes show that the particle's motion can cause distortion of the electric field distribution, leading to the generation of PD. The study of the PD characteristics at different particle motion modes and positions obtained contributes to a deeper understanding of the PD induced by cellulose particle motion under AC voltage and provides a reference for the insulation evaluation of transformers.

Effect of Silica and Alumina Nanoparticles Addition on the Dielectric and Rheological Properties of Shea Oil Methyl Ester

Study’s Excerpt/Novelty This study introduces a novel green nanofluid by incorporating shea methyl ester (SME) with surface-modified Al2O3 and SiO2 nanoparticles, addressing the environmental concerns associated with mineral-based transformer oils. The research highlights that SiO2 nanoparticles significantly enhance the dielectric properties of the base oil while minimally impacting its viscosity, suggesting improved insulation performance. This innovative approach offers a sustainable alternative with superior electrical characteristics, potentially transforming the application of transformer oils in the industry. Full Abstract The increasing environmental and utility concerns of mineral-based transformer oils have impelled research into alternative insulation liquids. This study developed a green nanofluid using a two-step approach, incorporating shea methyl ester (SME) derived from shea butter oil and surface-modified Al2O3 and SiO2 nanoparticles. The dynamic viscosity at 25 °C and dielectric properties of the developed nanofluid were investigated in the frequency range of 0.1–15 kHz. Adding nanoparticles into the SME noticeably increased the oil's viscosity, with SiO2 nanoparticles having the least effect on the base liquid's viscosity. The addition of 0.6 wt% of Al2O3 nanoparticles increased the dielectric loss of the SME at all frequencies (0.1–15 kHz), while the addition of 0.6 wt% SiO2 filler nanoparticles reduced the loss of the base oil at frequencies beyond 4 kHz indicative of an improved electrical property. Adding Al2O3 and SiO2 to the base SME oil obtained higher relative permittivity values. This study successfully developed an SME and single nanoparticle-based nanofluid, incorporating a 0.6 wt% weight fraction of surface-modified Al2O3 and SiO2 nanoparticles. The addition of these nanoparticles increased the dynamic viscosity of the base methyl ester oil, with approximately 43% and 36% increases, respectively. The nanofluid developed by incorporating SiO2 nanoparticles showed promising dielectric performance for its application as an insulation liquid.

Research on infrared spectroscopy detection of furfural content in transformer oil based on acetonitrile extraction

Abstract The stable operation of power transformers depends on the oil-paper insulation system, in which the aging degree of insulating paper is the key to evaluate the remaining life of the transformer. Currently, methanol extraction is widely used to detect the furfural content in oil to evaluate the aging state of insulating paper, but it ignores the influence of methanol produced during the operation of the transformer on the extraction results. To address this issue, this paper proposes a new method that can replace methanol extraction for detecting furfural in oil through simulation and experiment. Firstly, through simulation and experiment, it is proved that the strong vibration absorption peaks at 1677 cm-1 for furfural carbonyl and 2240 cm-1 for acetonitrile cyano can be used to establish a new method for extracting furfural content in oil using acetonitrile. A quantitative model between the concentration x of furfural in the furfural-acetonitrile mixed solution and the area y of the infrared absorption peak at 1677 cm-1 is established, with a goodness of fit of 0.9974. Secondly, a comparison between direct detection and acetonitrile extraction methods is conducted. The results show that direct detection is simple to operate, but the minimum detection concentration is 40mg/L, which is difficult to meet practical requirements. Acetonitrile extraction can reduce the minimum detection concentration to 0.1mg/L. At the same time, the extraction conditions are analyzed to determine the extraction ratio of 30, extraction times of 5, and extraction rate of 60%. Finally, the proposed detection method is applied to thermal aging tests on insulating paper of different types. The experimental results show that the proposed method has good repeatability and improves the detection resolution of furfural content. This paper combines infrared spectroscopy with acetonitrile extraction technology to open up a more efficient and practical new method for detecting furfural content in transformer oil.

Development of Generalized Correlations for Predicting Density and Specific Heat of Nanofluids for Enhanced Heat Transfer

Objective: Energy-generating devices such as automotive vehicles, power reactors, computing machines etc. require effective cooling for safe and efficient operation. Additionally, at the same time, heat losses must be minimized. In industry, pure fluids such as water, air etc. are usually used for these purposes. Nanofluids have recently been explored as an alternative to traditional coolants. Nanofluids have attracted attention due to their superior thermophysical properties as compared to conventional fluids. Nanofluids are nanosized particles of metallic, non-metallic, or organic origin suspended in base fluids. Some examples of these nanoparticles include silver, copper, metal oxides, metal nitrides, carbon materials (such as carbon nanotubes, metal carbides) etc. Several options for base fluids exist, such as ethylene glycol, water, transformer oil, etc. Methods: The thermophysical properties of nanofluids of interest include thermal conductivity, viscosity, density, and specific heat. Predicting these physical properties, which depend upon particle concentration, temperatures, particle sizes, etc., is a significant challenge. Researchers have developed correlations for predicting these properties based on available experimental data. However, a thorough literature review suggests that generalized correlations do not exist, especially for the density and specific heat. Most researchers have done work on thermal conductivity and viscosity. The present work has developed generalized correlations to predict nanofluids' thermophysical properties density, and specific heat. Experimental data was collected for the various operating and geometric parameters affecting nanofluids' density and specific heat. Results: By performing regression analysis, four dimensionless correlations are proposed for each of predicting two important thermophysical properties, i.e., heat capacity and density of nanofluids. The developed correlations were compared with available experimental data. Conclusion: It was found that the correlations generated were fitted the experimental data for density and specific heat with an accuracy of ±10% band of actual value.

Miniaturized quantitative detection of particles in transformer oil based on lensless holographic microscopy

The insulation effectiveness of transformer oil has a substantial impact on the safety of transformers. In the presence of particles within the transformer oil, they are able to adversely affect its performance, potentially originating from electrical arcs or severe carbonization processes. These particles are commonly capable of reducing the insulating capability of the transformer oil, thereby enhancing the risk of transformer failure. To overcome this dilemma, we propose a miniaturized quantitative particle detection approach in transformer oil based on a lensless digital holographic microscope. In general, methods based on traditional high-resolution optical microscopy are essentially limited by their fairly huge cost and importability. However, the proposed approach utilizes a homemade miniaturized model combined with a CMOS image sensor to achieve microscopic imaging. The deployed device here weighs ∼ 50 g, making it portable and affordable for on-the-go detection. By simplifying the microscope system, we can quickly and accurately detect tiny particles in transformer oils. This enables us to efficiently assess the status of impurities in power transformers and to identify potential issues in advance, thereby enhancing the reliability of transformer oil. This detection method not only features simplicity of operation, convenient on-the-go detection, low cost, and high precision but also provides a convenient approach for accurately assessing the condition of impurity particles in power transformers and preemptively identifying potential issues.

Research on anomaly detection in oil chromatography based on convolutional neural network and grey relational analysis algorithm

Abstract The power transformer is a crucial voltage conversion node and a core device in substations. Currently, dissolved gas analysis (DGA) in oil is a practical and validated method for assessing the operational status of transformers. In this study, addressing issues such as insufficient data mining from single dissolved gas measurements, we employed a combination of Convolutional Neural Network and Grey Relational Analysis algorithm to enhance the reliability of identifying abnormal dissolved gas data in transformer oil. This approach improves the sensitivity and accuracy of transformer fault detection. The study includes data processing and result analysis, indicating that the proposed algorithm effectively identifies abnormal dissolved gas data in transformer oil.

Thermodynamic Computational Method of Thermal Decomposition Gas Content in Transformer Oil Considering Temperature and Competitive Reaction

Thermodynamic Computational Method of Thermal Decomposition Gas Content in Transformer Oil Considering Temperature and Competitive Reaction

Simulation of the Streamer Propagation in the Transformer Oil With Bubbles

Simulation of the Streamer Propagation in the Transformer Oil With Bubbles

Chemically engineered essential oils prepared through thiocyanation under solvent-free conditions: chemical and bioactivity alteration

The generation of chemically engineered essential oils (CEEOs) prepared from bi-heteroatomic reactions using ammonium thiocyanate as a source of bioactive compounds is described. The impact of the reaction on the chemical composition of the mixtures was qualitatively demonstrated through GC–MS, utilizing univariate and multivariate analysis. The reaction transformed most of the components in the natural mixtures, thereby expanding the chemical diversity of the mixtures. Changes in inhibition properties between natural and CEEOs were demonstrated through acetylcholinesterase TLC autography, resulting in a threefold increase in the number of positive events due to the modification process. The chemically engineered Origanum vulgare L. essential oil was subjected to bioguided fractionation, leading to the discovery of four new active compounds with similar or higher potency than eserine against the enzyme. The results suggest that the directed chemical transformation of essential oils can be a valuable strategy for discovering new acetylcholinesterase (AChE) inhibitors.Graphical

Influence of the Flowing Speed of Transformer Oil Contaminated by Dry Cellulosic Particles on Its Breakdown Characteristics

Influence of the Flowing Speed of Transformer Oil Contaminated by Dry Cellulosic Particles on Its Breakdown Characteristics

Sawtooth-Based FDS for Rapid and Accurate Testing of Transformer Oil–Paper Insulation

Sawtooth-Based FDS for Rapid and Accurate Testing of Transformer Oil–Paper Insulation

Evaluation of the Electrical Power Transformer Fins Design Technology: Numerical Analysis and Experimental Validation

The study includes numerical analysis and experimental verification on an electrical power distribution transformer (250 kVA, 11 kW, Oil Natural Air Natural). ANSYS Fluent R3 2019 software was used to develop the numerical simulation model. The validity of the numerical model was confirmed by comparing the results of the numerical model and experimental data. The study aims to improve the efficiency and performance of electrical power distribution transformers by proposing a design that reduces the temperature of the transformer while maintaining its traditional size. Numerically, the effect of fin geometry on the temperature and density of transformer oil was studied. Four different fin designs were proposed and compared with the traditional design. According to the results, all proposed designs contributed to improving the cooling performance of the transformer compared to the traditional design. Design A is similar to the traditional transformer design, with the only modification being manipulation of the fin length, and achieves an average oil temperature reduction of 4 K. Design B showed the smallest temperature drop of the four designs, with a 3 K drop. Designs C and D include ventilation channels that match the shape of the fin, providing distinct design differences. The difference between both designs relied on the fact that for design C, the orthogonally of fin plates was retained. On the other hand, in design D, skewing of fin plates was introduced. Design D proved to be the most effective in reducing the average oil temperature, being reduced by 10 K. On the other hand, Design C reduced the average oil temperature by 7 K.