Transformer Oil Research Articles

Effect of Initial Temperature on Flame Spread over a Sand Bed Wetted with Transformer Oil

A series of experiments were conducted on quartz sand beds wetted with transformer oil under initial temperatures of 80–140 °C and fuel–sand mass ratios of 1:4–1:8. The flame spreading process over the fine sand bed wetted with limited liquid fuel can be divided into the development and quasi-steady stages. Experimental results reveal that the flame spread rate in the quasi-steady stage increases with the initial temperature and fuel–sand mass ratio. The effect of sand bed width on flame spread depends on the initial temperature. The flame spread rate is insensitive to the sand bed width at low initial temperatures; however, it increases with sand bed width at an initial temperature close to the flash point of liquid fuel. This discrepancy mainly results from the enhanced capillary effect due to the decreased viscosity at high initial temperatures. The capillary effect is the dominant factor determining fuel vaporization and, thus, the flame spread rate, and flame radiation plays an increasing role with increasing initial temperature. The maximum flame height is sensitive to sand bed width and fuel–sand mass ratio but changes little with initial temperature. A dimensionless model was proposed to predict the normalized flame height.

Prediction of Dissolved Gas in Transformer Oil Based on Variational Mode Decomposition Integrated with Long Short-Term Memory

To address the nonlinear and non-stationary characteristics of dissolved gas concentration data in transformer oil, this paper proposes a hybrid prediction model (VMD-SSA-LSTM-SE) that integrates Variational Mode Decomposition (VMD), the Whale Optimization Algorithm (WOA), the Sparrow Search Algorithm (SSA), Long Short-Term Memory (LSTM), and the Squeeze-and-Excitation (SE) attention mechanism. First, WOA dynamically optimizes VMD parameters (mode number k and penalty factor α to effectively separate noise and valid signals, avoiding modal aliasing). Then, SSA globally searches for optimal LSTM hyperparameters (hidden layer nodes, learning rate, etc.) to enhance feature mining for non-continuous data. The SE attention mechanism recalibrates channel-wise feature weights to capture critical time-series patterns. Experimental validation using real transformer oil data demonstrates that the model outperforms existing methods in prediction accuracy and computational efficiency. For instance, the CH4 test set achieves a Mean Absolute Error (MAE) of 0.17996 μL/L, a Mean Absolute Percentage Error (MAPE) of 1.4423%, and an average runtime of 82.7 s, making it significantly faster than CEEMDAN-based models. These results provide robust technical support for transformer fault prediction and condition-based maintenance, highlighting the model’s effectiveness in handling non-stationary time-series data.

Assessment of Polycyclic Aromatic Hydrocarbons and Polychlorinated Biphenyls from Selected Sites in Rivers State, Nigeria: Environmental and Health Implications

This study investigated the levels of Polycyclic Aromatic Hydrocarbons (PAHs) and Polychlorinated Biphenyls (PCBs) in soil from selected industrial sites in Rivers State, Nigeria. Samples obtained from a non-industrial site (control) and four industrial sites were analysed using Gas Chromatography-Mass Spectrometry (GC-MS). Results showed that the levels of PAHs and PCBs in the control sample were below detection limit (<0.01mg/kg). Additionally, PAH and PCB concentrations in industrial sites investigated ranged from 0.41mg/kg to 32.27mg/kg and 1.09mg/kg to 5.55mg/kg respectively. Indorama Petrochemical industrial site ranked the highest while PHED transformer oil storage site ranked lowest with respect to PAH and PCB contaminations. Human Health Risk evaluation showed HQ and TCR for PAH ranged from 0.00007 to 0.668 and 2.4x10-6 to 8.1x10-4 respectively. TCR for PAH exposure in Indorama Petrochemicals 8.1x10-4 (children) and 1.9x10-4 (adult)) exceeded the USEPA upper limit of 1x10-4. Similarly, HQ and TCR for PCB ranged from 0.00026 to 0.912 and 1.8x10-6 to 3.8x10-5 respectively. All HQ values obtained were below the USEPA limit of 1, thus, indicating negligible non-cancer risks. It was observed that dermal contact presented the highest risk for exposure routes from PAH and PCB contaminations in all investigated sites. Findings showed varying soil contamination from PAH and PCB across sites with different industrial activities. The Indorama Petrochemical industrial site showed the highest level of contamination and potential cancer risks from PAH exposure to both children and adults. Continuous monitoring and remediation actions are advocated to mitigate long-term PAH and PCB contaminations and protect public health.

Multiscale session-enhanced long time series modeling for power transformer oil temperature prediction

Multiscale session-enhanced long time series modeling for power transformer oil temperature prediction

UV-activated acetylene sensor based on WO3/NiO-modified ZnO heterostructures with good stability in transformer oil.

UV-activated acetylene sensor based on WO3/NiO-modified ZnO heterostructures with good stability in transformer oil.

Cooling Fiber Laser Power Converter Systems by Immersion in Oil

We demonstrate the use of Laser Power Converters (LPCs) driven by fiber laser light while immersed in transformer oil for heat management purposes. Reliability tests performed via extended continuous operation using 6–7 W of input power from 808 nm and 976 nm light propagating through oil show no degradation of components nor transmission losses from the oil for up to 1000 h. The operation of a bare die designed for use with 1040–1080 nm light and in direct contact with oil is also shown to be feasible. We discuss how the use of transformer oil can be beneficial to transfer excess heat away from LPCs in special applications.

Практические методы оценки степени деградации систем трансформаторов после длительного хранения

Backup transformers are stored at power plants and grid companies to replace those that have failed. Transformers are on stand-by for 12–15 years or more. During storage, degradation of transformer nodes occurs despite the lack of connection, since water dissolved in oil affects the package plates, oxidizes the contacts of the on-load tap changing, low voltage (LV) terminals, etc. Due to current shortage of transformers, particular attention to the choice of informative methods for monitoring their condition is reasonable. The practical relevance of this study is the fact of disability of emergency failure of a new transformer after long-term storage. To analyze changes of transformer parameters after long-term storage, the following methods have been used: dependence of Rh(U), tg(U); dependence of breakdown voltage in oil Upr(N); vibration phenomena on the turns of coils; transformer housing vibration tests. A study of the parameters of backup transformers has been conducted. The authors have studied the influence of temperature (–10 and +20 °C) and aging products of solid insulation on furan compounds during long-term storage on the parameters of the insulation system. Deterioration of the insulation due to degradation of transformer oil and paper insulation due to excess moisture content in the oil has been detected. The most informative method to analyze the condition of backup transformers has been determined; it is oil analysis for moisture content. The authors have been established that for periodic monitoring during long-term storage it is sufficient to monitor the oil for moisture content. Other methods should be used only when preparing the transformer for voltage supply, since they are uninformative and quite expensive.

Thermal Management in 500 kV Oil-Immersed Converter Transformers: Synergistic Investigation of Critical Parameters Through Simulation and Experiment

Aimed at solving the problem of insulation failure caused by the local overheating of the oil-immersed converter transformer, this paper investigates the heat transfer characteristics of the 500 kV converter transformer based on the electromagnetic-flow-heat coupling model. Firstly, this paper used the finite element method to calculate the core and winding loss. Then, a two-dimensional fluid-heat coupling model was used to investigate the effects of the inlet flow rate and the radius of the oil pipe on the heat transfer characteristics. The results show that the larger the inlet flow rate, the smaller the specific gravity of high-temperature transformer oil at the upper end of the tank. Increasing the pipe radius can reduce the temperature of the heat dissipation of the transformer in relative equilibrium. Still, the pipe radius is too large to lead to the reflux of the transformer oil in the oil outlet. Increasing the central and sub-winding turn distance, the oil flow diffusion area and flow velocity increase. Thus, the temperature near the winding is reduced by about 9%, and the upper and lower wall temperature is also reduced by about 4%. Based on the analysis of the sensitivity weight indicators of the above indicators, it is found that the oil flow rate has the largest share of influence on the hot spot temperature of the transformer. Finally, the surface temperature of the oil tank when the converter transformer is at full load is measured. In the paper, the heat transfer characteristics of the converter transformer are investigated through simulation and measurement, which can provide a certain reference value for the study of the insulation performance of the converter transformer.

Fault Diagnosis of Transformer Dissolved Gas Based on Random Forest

To improve the accuracy of transformer fault diagnosis, this paper proposes a transformer fault diagnosis model based on the Random Forest (RF) algorithm. First, the dissolved gas analysis (DGA) method is used to preprocess the concentration data of characteristic gases dissolved in transformer oil. The Random Forest model is then constructed using Bootstrap sampling and random feature selection, achieving high-precision classification of transformer fault types through the integration of multiple decision trees. To validate the effectiveness of the model, actual transformer fault operation data are selected for testing. Additionally, the Random Forest model is compared with five other models: GNB, LDA, KNN, SVM, and GBDT. The results show that the Random Forest model significantly outperforms the other models in terms of both training and testing accuracy, while also demonstrating higher efficiency in training time. The comprehensive performance of the Random Forest model is superior to the comparison models. This study demonstrates that the Random Forest-based transformer fault diagnosis method can effectively handle complex dissolved gas data, accurately identify multiple fault types, and exhibit high generalization ability and robustness, making it suitable for practical transformer fault diagnosis in power systems.

Adsorption and Sensing Performance of Pt(1-3)-Modified TiSe2 for Dissolved Gas (CH4, C2H2, and CO) in Transformer Oil: A DFT Study.

Based on density functional calculations, the adsorption and gas sensing properties of transition metal Pt(1-3)-modified TiSe2 for dissolved gas (CH4, C2H2, CO) in transformer oil were studied in this paper. Firstly, the stable structures, density of states, and energy bands of Pt(1-3)-modified TiSe2 were calculated. Then, the structure parameters, density of states, electrostatic potential distribution, and desorption time of Pt(1-3)-modified TiSe2 after adsorbing CH4, C2H2, and CO gas were calculated. The results show that the large binding energy between the transition metal Pt(1-3) modification and the TiSe2 substrate indicates that the modification systems have good structural stability. On the one hand, Pt(1-3) modification improves the conductivity of TiSe2. On the other hand, the transition metal Pt(1-3), which acts as the active site for gas adsorption, obviously enhances the gas adsorption effect, resulting in the significant charge transfer and a change in material conductivity. In summary, Pt(1-3)-modified TiSe2 significantly improves the adsorption and gas sensing performance of gas sensing materials for CH4, C2H2, and CO, which provides a new idea for the study of gas sensing materials for online monitoring of transformer working conditions.

Forecasting Dissolved Gas Concentration in Transformer Oil Using the AdaSTDM

AbstractAccurately forecasting dissolved gas concentration (DGC) in transformer oil is crucial for ensuring the safety and reliability of power transformers and facilitating early anomaly warning. Current methods for forecasting DGC demonstrate limited effectiveness in non‐stationary characteristics with data‐distribution shifts. To address this, this paper presents a novel adaptive segmented temporal distribution matching (AdaSTDM) model, consisting of the Toeplitz inverse covariance‐based clustering (TICC) algorithm and time distribution matching (TDM) algorithm. To effectively adapt to the different state distribution of the DGC data, the TICC algorithm is used to segment the state domain of the DGC sequence, and the Jensen‐Shannon (JS) divergence is used as an indicator to evaluate the segmentation results. The TDM module is designed to mitigate data‐distribution mismatches by learning common knowledge among different gas states. Experimental results across two real‐world cases illustrate that the proposed AdaSTDM outperforms various advanced methods in predicting both stationary and non‐stationary DGC data. © 2025 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Accurate and efficient image stitching for inspection robots working in complex transformer oil environments

Accurate and efficient image stitching for inspection robots working in complex transformer oil environments

Environmental Pollution Mitigation: The Chemical Transformation of Residual Frying Oil into Biodiesel

Currently, residual frying oil has three purposes: it is used again in the preparation of fried foods, mixed with new vegetable oil, which can cause cardiovascular disease in the consumer; it is collected by government institutions, without having an exclusive use; or it is thrown into the drain, causing serious pollution problems to water resources. An alternative is to transform it into biodiesel, through transesterification with methanol, to be used in internal combustion engines, biodiesel-diesel mixtures of 10:90, 15:85 and 20:80 (v/v), according to international regulations in such a way that, in the combustion process, less CO2 and greenhouse gas emissions are generated. Residual frying oil served as raw material, which was collected, mixed and homogenized to evaluate physicochemical properties before transformation. The biodiesel generated had a density of 0.886 g L−1, an acidity of 0.516%, a viscosity of 7.535 mm2 s−1, a flash point of 166.8 °C and an oxidative stability of 49 days at 25 °C. Additionally, the content of functional groups characteristic of biodiesel formation was evaluated by Infrared Spectroscopy. The Biodiesel obtained is of good quality for use in internal combustion engines and agricultural machinery, thus validating its continuous production and complying with the standard values of international regulations.

Comprehensive Assessment of Transformer Oil After Thermal Aging: Modeling for Simultaneous Evaluation of Electrical and Chemical Characteristics

This paper reports the results of an experimental study that examines the impact of thermal aging on the electrical and chemical properties of insulating oil used in power transformers. Transformer-oil samples were thermally aged over a 5000 h period at different temperatures varying between 80 °C and 140 °C, replicating both normal and extreme operating conditions. Measurements of breakdown voltage, dielectric dissipation factor, acidity, and water content were taken at 500 h intervals. A novel approach of this research is the integration of these electrical and chemical characteristics into a comprehensive exponential regression analysis model. The results indicate that breakdown voltage and resistivity decrease with aging time, whereas the dielectric dissipation factor, acidity, and water content increase with aging time. The degradation trends computed by the proposed model show close correlation with both electrical and chemical properties, with correlation coefficients generally equal to or exceeding 90%, which demonstrates its reliability in predicting aging behavior of transformer oil. This integrated approach offers valuable insights into the combined electrical and chemical degradation processes due to thermal aging and assists in the condition assessment of power transformers.

Oil level prediction of power transformer based on BP neural network

Transformer oil plays the role of heat dissipation and insulation in the operation of transformer, and any excessive high (low) transformer oil level will affect the safe and stable operation of the transformer; the transformer oil level will change with the change of ambient temperature and load rate, so it is necessary to predict the oil level of the transformer, and then it can be governed in advance. First, according to the shape of the transformer oil storage cabinet, oil surface characteristics and the working principle of the oil level meter, the relationship between the oil volume in the transformer oil storage cabinet and the oil level indication is established; second, according to the transformer operating parameters, the BP neural network oil level prediction model is constructed, and the model is trained and tested to determine the optimal number of training times; finally, by comparing the predicted curve of the transformer oil level with the actual operating curve, the model is verified for its accuracy of the model.

Theoretical Exploration of a Noble Metal-Decorated Black Arsenic-Phosphorus Monolayer as a Highly Sensitive Gas Sensor for Dissolved Gas Detection in Power Transformers.

The aging or failure of power transformers poses a significant threat to both production and daily life, and employing characteristic dissolved gas detection offers an effective early warning system to mitigate such risks. In this study, we conducted first-principles simulations to investigate the adsorption and sensing characteristics of two-dimensional intrinsic and noble metal-decorated AsP monolayers toward four characteristic gases (C2H4, H2, C2H2, and CO). Additionally, we conducted an in-depth analysis of the electronic structure and its impact on the sensing performance within the adsorption systems. The study demonstrates that the introduction of a noble metal improves gas behaviors, with the Ru-decorated AsP monolayer exhibiting particularly superior adsorption and sensing performances. At different temperatures, the H2/Ru-AsP adsorption system in transformer oil demonstrates excellent adsorption behavior. During the initial and later stages (from 350 to 900 K) of power transformer failure, the adsorption energy change is approximately -0.8 eV, and its desorption time is less than 0.5 s. Even in scenarios involving severe faults that result in equipment overheating and the subsequent generation of gases such as C2H2 and C2H4, the sensor demonstrates a consistent response time of less than 0.3 s. This work provides valuable research support for characteristic gas detection in the safe operation of power transformers.

Analysing the correlation between polychlorinated biphenyl (PCB) content in transformer oil to ensure compliance with environmental regulations

Abstract Polychlorinated Biphenyls (PCBs) are widely used because they are a non-flammable material with good thermal stability. Transformer oils were manufactured between 1980 and 1997. Due to environmental and health risks, Indonesia was committed to phasing out PCBs by 2025 and 2028. Referring to the results of the Stockholm Convention which aims to phase out PCBs by 2028. PT PLN (Persero) was concerned about compliance with PCBs waste management regulations. This analysis focuses on finding a correlation between the PCBs content in transformer oil samples and the year of production on transformers at several electrical distribution substation locations. Normality and homogeneity tests were conducted as the initial stage of the Statistical Test. The Spearman Correlation Test yielded a p-value of 0.022 and a correlation value (r) of - 0.161. The results of the statistical tests show that there is a weak negative relationship between the year of transformer production and the PCBs test results. Statistical analysis suggests newer transformers tend to have lower PCBs content, but further research is needed to confirm this. The resulting mitigation was to create a long-term service agreement with a third party for transformer oil sampling services, parameter testing, oil retro filling & destruction of oil containing PCBs with a competent partner specifically related to PCBs. It is carried out to ensure compliance with environmental regulations and enhancing safety and operational efficiency.

Design and Fabrication an optical sensor devices base on graphene oxide

Contamination of oil, particularly by dissolved water, is a very common problem in the failure of step-down transformers used by electricity providers as this degrades the insulating property of the oil. In this paper, the use of D-shaped optical fibers functionalized with Graphene Oxide is presented to detect the water content in transformer oil. The synthesis of graphene oxide was achieved by a modified version of Hummer's method. Subsequently, the drop-casting process was used to apply the graphene oxide onto the D-shaped fibre. The coating thickness attained in the samples was around 200 nm. Side polishing in a single-mode fiber engages an evanescent field that increases its sensitivity as an optical sensor. A few layers of graphene oxide coating on D-fiber exhibit a quick response time and high sensitivity to moisture content present in transformer oil, which proves to be a hopeful solution in real-time monitoring and maintenance of transformer insulation systems. It manifested that the experimental results had a high sensitivity to different water contents in transformer oil for the D-shaped fiber coated with GO. The GO-coated fibers exhibited a sensitivity of 0.5677 dB/ppm, which is relatively high compared with the sensitivity in the case of uncoated D-shaped fibers.

Investigating the Magnetic Field Dominated Streamer Dynamics in Transformer Oil

Investigating the Magnetic Field Dominated Streamer Dynamics in Transformer Oil

Dechlorination of polychlorinated biphenyls in used transformer oil by accelerated electron beam irradiation

Dechlorination of polychlorinated biphenyls in used transformer oil by accelerated electron beam irradiation