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The possible mechanisms of electrical breakdown of transformer oil at low voltage values at alternating voltage were investigated. The formation of streamers from the surface of bubbles into a liquid dielectric after partial discharge in a bubble was detected. This phenomenon has been observed for bubbles in pure transformer oil, in transformer oil with the addition of carbon nano-tubes, in oil after operation. At very low voltages, streamers in transformer oil were registered if a partial discharge in the bubble was stimulated by X-rays. Three possible mechanisms of electric breakdown of a gap filled with transformer oil were revealed at low values of electric field strength in the range from 13 to 51 kV/cm. The appearance of streamers from bubbles was preceded by deformation of the bubble due to one or more partial discharges in it. Streamers always appeared in pairs from the poles of the bubble except for the case of the transformer oil with carbon nanotubes. The results obtained are important for understanding the mechanisms of breakdown of transformer oil at very low voltage and can explain the causes of a number of accidents in high-voltage installations. [ABSTRACT FROM AUTHOR]

This paper presents a novel forecasting methodology based on neural networks and second-generation wavelets, and an example of application of such methodology for forecasting the oil temperature of an actual power transformer. The proposed method consists on selecting the variables with the highest correlation concerning the magnitude to be predicted (in situations featuring multiple variables), selecting the most appropriate second-generation wavelet from a set of candidate lifting wavelet transforms to smooth each variable's data and using the values smoothed by the selected second-generation wavelet to train the considered neural network. The authors applied the proposed methodology to predict the oil temperature of an existing transformer for four days and for one month using field data collected during four years of equipment operation. The obtained results indicate that selecting the most appropriate second-generation wavelet considering different selection techniques and using them to smooth the input variables significantly reduce the forecasting errors. The application of the proposed approach to forecast the oil temperature from the field-collected data using a long short-term memory neural network considering different batch sizes, neuron numbers and second-generation wavelet selection techniques decreased the average forecasting errors presented by using just the considered long short-term memory network by up to eleven times. The innovations of this proposal about literature are the use of correlation-based variable selection techniques given improving neural network forecasting accuracy, which is unprecedented in transformer insulating oil temperature forecasting applications, and the employment of SGWs to smooth the network's inputs to increase the accuracy of the model significantly. For the application case study presented in this research, the minor average forecasting errors in percentage to predict the four-day and one-month future temperature were, respectively, 0.012 and 0.326%. Also, for one month of forecasting, the average error in degrees Celsius was less than 0.2. [ABSTRACT FROM AUTHOR]

This paper presents some attractive properties of two-dimensional (2D) nanofillers based transformer oil (TO) nanofluids. Nanofluids of 2D hexagonal boron nitride (h-BN) nanosheets in TO show stable dispersion with improved dielectric breakdown strength and superior thermophysical properties like thermal conductivity, viscosity, and stability. A substantial augmentation in AC breakdown voltage (BDV) of TO with h-BN is observed, large compared to the state-of-the-art boron nitride (BN) particles. The enhancement in BDV is clarified by the role played by the larger oil-nanofiller interfacial regions also known as Maxwell-Garnet regions of 2D nanofillers in the context of charge trapping performance. The faster rate of heating and cooling and noteworthy enhancement in thermal conductivity agrees well with Maxwells forecasts. It is explained by the enhanced phonon transfer via the large oil-nanofiller interface of 2D nano additives. Finally, the natural electrostatic repulsion between the h-BN sheets at diluted concentration exhibited enhanced stability both with time and temperature variation compared to its particle counterpart and yielded in high thermal efficiency. Hence, 2D nanofillers are better choices for next generation surfactant free transformer oil nanofluids due to their high surface area, lower filler fraction and enhanced stability. [ABSTRACT FROM AUTHOR]

Moisture and temperature are the main factors that affect the life expectancy of liquid-immersed power transformers. These factors speed up the aging process of cellulose insulation, worsening its physio-chemical properties and increasing the risk of faults in the transformer. Recently, a multi-physical modeling approach was reported aimed at estimating the temperature and moisture dynamics of transformer insulation for different loading conditions. The model objective was to evaluate how the moisture dynamics between oil and paper insulation might affect the loading capability of the transformer. Although the model is based on the representation and analysis of concrete regions of the oil–paper insulation, it could also be useful for the estimation of the moisture content of the solid insulation in moisture monitoring systems; however, the modeling had not been experimentally validated due to the difficulty of measuring the moisture in cellulose insulation during transformer operation. In this work, an experiment was designed and developed to carry out the experimental validation of the model and to evaluate the possibility of applying it for moisture monitoring purposes. Moisture dynamics experiments were carried out in an experimental test plant; the evolution of the moisture content throughout the experiment was assessed by direct measure and with dielectric response measurements. The experimental conditions were simulated with the model, and the estimated and measured moisture contents were compared. A detailed analysis was carried out to evaluate the precision of the model and to identify possible sources of error and improvements that may be implemented to improve its accuracy. [ABSTRACT FROM AUTHOR]

The present article covers simultaneous measurements of current, emitted light and voltage drop associated with DC discharges in power transformer mineral oil. These measurements are analyzed in both time and frequency domains. The study focuses on the nanosecond scale for electrical signals and on the megahertz scale for spectral analysis. All of these measurements are characterized by high-frequency activity between 20 and 125 MHz and are highly correlated. By using fast response sensors, the signals have been acquired and represented in a digital format. This representation has been used to differentiate between oil samples with different water content based on FFT light signals analysis. For in service aged oils, it was difficult to differentiate between their aging when based only on the latter approach. Then, a feature extraction of light emission signals and their classification based on SVM have been successfully performed. In the experimental section, it has been shown that the proposed approach is capable of distinguishing between two different classes of oil (new or aged). Hence, by the use of simple SVM protocol, aged oils are well identified with an accuracy of 99% by analyzing light signals provided from low energy discharges. Such techniques can be useful for sealed power equipments diagnosis. [ABSTRACT FROM AUTHOR]

The disposal of aged transformer insulating oils presents a significant environmental challenge due to the presence of diverse contaminants. This study proposes a novel biorefinery approach for reclaiming three aged transformer insulating oils, each with service periods ranging from 20 to 25 years, utilizing magnesium oxide (MgO) as a potent absorbent. The reclamation process involves a synergy of centrifugation, dehydration, and absorption with MgO, resulting in the substantial restoration of aged transformer insulating oils, imbuing them with desired insulating characteristics. Additionally, an adaptive neuro-fuzzy inference system (ANFIS) algorithm is developed to estimate the remaining life of transformers based on the properties of the reclaimed oil. This entails analyzing input variables, selecting the optimal combination based on their correlation with the degree of polymerization, and identifying optimal membership functions for the ANFIS model. The performance of the proposed ANFIS model is evaluated, demonstrating training and testing accuracies of 91.2% and 88.6%, respectively. Subsequently, the model is applied to assess transformer insulation and estimate expected life, offering valuable assistance to experts in transformer condition assessment. Overall, this algorithm presents a sustainable alternative to traditional methods for assessing transformer conditions and estimating expected life, thereby enhancing transformer maintenance practices and promoting environmental sustainability. [ABSTRACT FROM AUTHOR]

Exploration of sustainable insulation solutions for HVDC converter transformers has prompted the investigation of palm methyl ester (PME) as a viable alternative to traditional mineral oil. This study presents a detailed thermal ageing analysis of PME-impregnated thermally upgraded kraft (TUK) paper, focusing on critical elements of the oil–paper insulation system. The research emphasizes the importance of interactions between the oil and paper components, particularly in influencing long-term insulation performance and reliability under HVDC stress. Acidity measurements confirmed PME’s chemical stability over time, while scanning electron microscopy (SEM) provided insights into ageing-induced morphological changes in TUK paper, affecting its mechanical integrity and dielectric strength. The degree of polymerization was evaluated to assess the extent of chemical degradation, crucial for predicting the insulation system's lifespan. Furthermore, the study examined key electrical properties, including DC breakdown voltage, quasi-DC conductivity using the polarization depolarization current method, and space charge dynamics analysed with pulsed electro-acoustic techniques. Results showed that PME-impregnated paper offered superior performance over mineral oil-impregnated paper, with a higher DC breakdown voltage, lower quasi-DC conductivity, more stable charge distribution, and a significantly reduced electric field distortion rate (28% vs. 16%). These findings underscore PME’s potential as a viable, eco-friendly insulation solution for HVDC applications. The study advocates for further investigation into PME in oil–paper systems, emphasizing the pivotal role of these specific interactions and properties in enhancing the development and efficiency of HVDC technologies. [ABSTRACT FROM AUTHOR]

Palm kernel and castor oils are two vegetable oils available in tropical climates which have been proposed as an alternative to mineral oils, and their methyl esters, also called monoesters, have been previously elaborated. As the insulation system of many power transformers is made up of oil and paper, it is essential to study the interaction between these methyl esters and insulating paper. In this paper, we present the results of thermal aging of insulating paper immersed in methyl esters of palm kernel (MEPKO) and castor (MECO) oils. A comparison is made with insulating paper immersed in mineral oil (MO) based on the total acid number, the kinematic viscosity, the quantity of dissolved decay products through Ultraviolet–Visible (UV–visible) spectroscopy, the paper breakdown voltage, and the physical appearance of the insulating oils. UV–visible spectroscopy and total acid number show that the quantity of decay dissolved products of methyl esters is greater than the quantity of decay dissolved products generated by MO during initial thermal aging. However, the viscosity of MEPKO is smaller than the viscosity of MO. In addition, the paper breakdown voltage when dipped in methyl esters is greater than when dipped in MO. Therefore, methyl esters better preserve the insulating paper than mineral oils during initial thermal aging. [ABSTRACT FROM AUTHOR]

The temperature of windings hot spot is a factor affecting the load capacity and useful life of power and distribution transformers. It is the hottest point of transformer in terms of design and measurement in all components of the transformer. The temperature of oil and windings of transformers, especially windings hot spots, can accelerate destruction of cellulose paper such that the most common cause of transformer failure is collapse of insulation. In this study, we performed the temperature rise test on a copper distribution transformer with a power of 200 kVA in accordance with the IEC standard in several steps. The accurate sensors recorded the top, bottom oil and ambient temperature. In addition, the ohmic resistance of the windings was measured in the first stage and at the end of the test to calculate the hot spot temperature of windings. Finally, the results were compared according to the Computational fluid dynamics (CFD) method via the three-dimensional drawing of the same transformer by ANSYS Design modeler and its thermal modeling including windings, core, and active part display using ANSYS fluent software. Evaluation of the two analyses revealed that the percentage of relative errors of results was negligible; thus, transformer designers can use the CFD method in their research. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis (DGA) is a powerful tool to monitor the condition of a power transformer. Several interpretation methods have been proposed, one of the most reliable of which is the graphical Duval triangle method (DTM). The method consists of several triangles, which still requires expertise for fault identification. The use of computer-based technology has been implemented in recent years to support transformer fault identification. However, no study has done thorough investigation on the use of suitable machine learning algorithm for the ML-based implementation of this matter. This study examines six commonly used machine learning algorithms to support DGA fault identification of power transformer: decision tree, support vector machine, random forest (RF), neural network, Naïve Bayes, and AdaBoost. Three DGA fault identification methods for mineral oil insulated transformer were studied, namely DTM1, DTM4, and DTM5. The training and testing datasets were generated for each DGA method, and trained to each ML algorithm. The tenfold cross validation was used to evaluate the results using five criteria, namely classification accuracy, area under curve, F1, Precision, and Recall. RF models demonstrated the best performance in classifying fault codes of most DGA methods. A validation was carried out using the validation dataset, comparing the selected RF-based models to the graphical DGA fault identification. The combination method was also implemented in the developed model. The results show that the proposed model is reliable, and especially useful to be used for fault identification of a large number of transformer populations. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) detection is essential for evaluating the cross-linked polyethylene (XLPE) cable insulation. The acoustic waves originating from PD carry important characteristic information, which can be used for PD detection. In this paper, a fiber-optic Mach–Zehnder interferometer (MZI) has been constructed to detect PD of an air gap sample of simulated XLPE cable terminal. The results indicated that the MZI could effectively detect the acoustic signals of 88 pC PD in the insulating oil and 499 pC internal discharge in the sample. The pulse signal in the MZI sensing signal contains the acoustic information of PD. Its phase distribution map has good phase and amplitude correspondence with the phase-resolved partial discharge map of the HFCT sensing signal to identify PD. The application of fiber-optic sensing technology is beneficial to the construction of smart grid. [ABSTRACT FROM AUTHOR]

This paper describes the recognition of low-temperature overheating in power transformers by the results of the analysis of dissolved gases in the oil. In order to improve the reliability of recognition, the results of a complex analysis of the gas content in oil for 344 transformers and shunt reactors with low-temperature overheating are presented. Values of gas ratios and values of percentage content of gases for overheating in a range of temperatures 150–300 °C are analysed. Comparison of gas ratios values and their percentage content with the most characteristic damages is carried out. 11 nomograms and graphical areas of defects, which correspond to overheating in a range of low-temperatures, are offered. A comparative analysis of overheating recognition reliability results in the temperature range of 150–300 °C with the use of the most known standards and the author's methods was made. The results obtained increase the reliability of defect type recognition based on the results of dissolved gas analysis in oil. [ABSTRACT FROM AUTHOR]

This paper deals with a comparative study of the electrostatic charging tendency (ECT) of a synthetic oil namely mono/dibenzyltoluene (M/DBT known as Jarylec, a product widely used for impregnating and filling power capacitors; and a mineral oil largely used in high-voltage oil-filled electrical equipment and which is Nynas Nytro Gemini X (NGx). The ECT of insulating oils generated by the charge concentration gradient at the oil-pressboard interface is investigated on a spinning disk system (CIGRE Cell) covered on its both sides with pressboard of different thicknesses versus the rotating speed of the disk and thickness of pressboard. Experimental outcomes show that the ECT of the two investigated liquids is very close at low rotating speeds of the disk that is for 200 and 400 rpm/min. While the ECT of Jarylec is higher than that of mineral oil NGx for higher velocity (600 and 700 rpm/min); the difference increases with the rotating speed of the disk and the thickness of pressboard. However, the observed difference is not so alarming and cannot be exclusive of this synthetic oil for possible use in power transformers provided that it fulfills the other required criteria. [ABSTRACT FROM AUTHOR]

Acetylene is one of the main fault gases for oil transformers. The rapid and highly sensitive detection of dissolved acetylene in the insulating oil plays an extremely important role in the diagnosis of transformer faults, as it is produced by the decomposition of hydrocarbons due to discharge and overheating. This work describes a rapid real-time online monitoring system for dissolved acetylene in oil, which integrates a highly efficient Teflon-AF2400/ceramic composite degassing module and a high-sensitive laser photoacoustic detector. Real-time online monitoring is feasible as the detection period of the device is as short as 1.5 min, and the equilibrium concentration of acetylene in the oil can be accurately determined from test data at a degassing time of 15 min. When the concentration of acetylene in the oil changes suddenly, the device can report more than 90% of the change within 30 min. The detection accuracy is improved from 0.9 to 0.3 μL L−1 after corrections are made to account for the influence of temperature on the oil–gas separation membrane. [ABSTRACT FROM AUTHOR]

By scaling down scientific inquiry from a macroscopic level to a nanoscopic one, various existing problems in the disciplines of electricity, electronics, and mechanics were effectively overcome. Transformers are distinctive devices that have a reduced lifespan because of the oil they use having a low dielectric strength. This problem may be resolved by incorporating nanoparticles into the standard oil. Nevertheless, extensive research is always being conducted to achieve the economical production of nanoparticles for optimal usage. The main aim of this study is to explore the efficient and economical production of silica nanoparticles by sol–gel synthesis. The silica nanoparticles were analysed using X-ray diffraction, which revealed a prominent peak at Bragg angle 22°. This peak provides evidence for the presence of a hexagonal crystal structure in the nanoparticles. The Fourier transform infrared spectroscopy (FTIR) study of the synthesised nanoparticles reveals the existence of O–H, Si–O–Si, and Si–OH functional groups at wavenumbers of 2956 cm−1, 1126 cm−1, and 976 cm−1, respectively. The morphology and particle size of silica nanoparticles were analysed using the field emission scanning electron microscopy method. The research revealed that the nanoparticles had a spherical morphology, with an average particle size of around 36 nm, and exhibited little aggregation. The optical band gap of silica nanoparticles was calculated by using the Kubelka–Munk method, which included evaluating reflectance data acquired from a UV–VIS–NIR spectrophotometer. The calculated value for the optical band gap is 5.1 electron volts (eV). Furthermore, the incorporation of nanoparticles into the host Naphthenic mineral oil resulted in an increase in the breakdown voltage from 58.3 to 69.3 kV, as determined by the Jiantong 6801 Automatic 50 Hz Electrical Breakdown Tester in accordance with the IEC60156 standard. The rise in numbers may be attributed to the silica nanoparticles capacity to collect unbound electrons. [ABSTRACT FROM AUTHOR]

Due to increase in the number of transformers, the need for transformer dielectric coolant (TDC) is gradually rising. Transformers are continuously under stress, which shortens the TDC's shelf life. In this experimental investigation, the antioxidants (Axes), butylated hydroxytoluene, and citric acid, as well as the emulsifier lecithin (Ln), were blended with TDC to increase the dielectric performance of the TDC. Using the Box–Behnken and response surface methodology, the ideal quantity of the Ax and Ln is determined. In this experimental design, dielectric breakdown voltage (DBV), kinematic viscosity (KVIS), fire point (FRPT), and electrical conductivity (ELC) are the four dependent response variables, while the Ln, PriAx and PreAx are the three independent process variables. Based on the experimental design, the optimal values for process and response variables are examined using the response and contour plots. The optimal quantity of the process variables is used for blending the TDC with Ax and Ln. After optimizing the process conditions, Ln = 0.295 g, PriAx = 0.377 g, PreAx = 0.248 g, the experimental response was DBV = 66.092 kV, KVIS = 15.465 cSt, FRPT = 189.734° C, ELC = 0.411 µS / m. These actual values deviate slightly from the expected response values, DBV = 65.69 kV, KVIS = 15.49 cSt, FRPT = 189.75° C, ELC = 0.404 µS / m. This demonstrates how well the mathematical model influences the dielectric response of the TDC. They also show that treating TDC with optimal amounts of Ln and Ax improves dielectric response and durability. Lecithin helps emulsify immiscible liquid mixtures by lowering surface tension. Lecithin's innate antioxidant properties provide protection against product degradation, wherever it is used. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis is the most common and well-established method used in identifying internal faults of power transformers. However, an onsite analysis using this method accuracy still need to be improved while the laboratories analysis process takes longer time. New alternative onsite and online method are most urgently need to be studied. Power transformer faults identification based on extended application of polarization and depolarization current (PDC) measurement data on oil sample from in-service power transformer was investigated in this research. The changes in the molecular properties of the insulating oil due to different fault conditions found had influenced the initial time response of the PDC measurement (from 0 to 100 s). In this research, the unique characteristic pattern of each condition was determined by a single unique numerical dimensionless quantity known as depolarization ratio index (DRI). DRI is used to express the changes of depolarization current shape with different fault conditions. The ratio of depolarization current between 5/100 and 10/100 s was found to have a better correlation at least more than 90% on the incipient fault in power transformer. Indeed, the DRI at 5/100 also was observed has higher accuracy especially on units with normal, overheating and arcing condition. In addition, a graphical representation of DRI value at 5/100 and 10/100 s was proposed for fault recognition in power transformer. Both DRI and graphical representation are well validated, and the proposed interpretation techniques have accuracy of more than 90% and able to identify and classify the normal, overheating and arcing condition of the in-service power transformer. [ABSTRACT FROM AUTHOR]

In this paper, maintenance process for on-load tap changer (OLTC) would be enhanced using nanofluid that developed by adding nanoparticles (titanium dioxide, TiO2) to the insulating oil. The occurrence of the internal arcs inside OLTC resulted in the formation of carbon atoms on the inner bonding points during its operation. Over time, these points became unusable since the contact resistance value was high. To increase the efficiency of the maintenance process, infrared radiation was used to excite the additive TiO2 atoms to attract carbon atoms. Electrical tests were performed before and after the proposed maintenance process according to IEC standards. The results showed significant differences in the maintenance process with and without nanoparticles. A prediction model to identify the OLTC state was constructed based on support vector machine classifier. The results revealed the ability of the predicted model to predict the OLTC state with accuracy 100%. [ABSTRACT FROM AUTHOR]

Conventionally, mineral oil impregnated with solid insulating materials has been used as an insulating medium in the transformers. On the other hand, mineral oil is less biodegradable than ester oils and it has poor fire-resistant characteristics. Therefore, the conventionally used mineral oils are not satisfying the new ecological requirements. Besides that the availability of fossil fuels is also going to run out. Consequently, several investigators have tried with different kinds of edible as well as non-edible oils for using them as liquid dielectrics in the transformers. This paper attempts to prove the feasibility of Pongamia pinnata oil (PPO) as an alternate liquid dielectric for transformer. At first, solid insulating material's deterioration study was carried out using DGA, SEM, XRD and UV–Vis spectroscopy. Alongside, thermal stability of PPO and mineral oil was analyzed through FTIR spectroscopy. DGA results show that gas-generating phenomenon was found on both the oil samples. However, the generation of combustible gas in PPO impregnated with solid insulating material is found to be lower when compared with other oils. From the FTIR analysis, it is found that the structural difference between PPO and mineral oil is negligible. However, from XRD and UV–Vis spectroscopy, it is evident that depending on the type of material, the thus emitted spectra have different bandwidth and there is variation in the absorbance intensity. When the contamination rate is higher, the absorbance intensity of the insulating oil increased. SEM analysis shows that cellulose fiber's width has drastically decreased in case of the solid insulation material immersed in mineral oil; also, there is evidence for bond breaking. [ABSTRACT FROM AUTHOR]

Electrical insulation materials are highly exposed to electrical network-based electric and magnetic fields in power systems. In electrical fields, electrical insulation materials are prone to breakdown and cause failure. A transformer failure which is related to pressboard insulation may lead to total breakdown and hence system malfunction in a total manner. In this study, a test setup is used to conduct discharge tests for pressboards in different thicknesses where main interest is originated magnetic fields on the pressboards. These tests are fulfilled with spherical and rod electrodes in transformer oil where magnetic field sensors are used to acquire discharge-based magnetic field data. By investigating high-voltage stresses with different levels, possible breakdown voltage of a pressboard is predicted and statistically analyzed. In addition to magnetic field measurements, discharge current measurements are taken; however, contrary to conventional studies, this study assesses magnetic field data which are dependent on the thickness of pressboard insulation. For different voltage levels (13 kV and 22 kV for different stress levels), magnetic field measurements and current waveforms are obtained by using magnetic field sensors and high-speed oscilloscope. Magnetic field time series signals are subjected to wavelet analysis, and wavelet coefficients are obtained. Rather than utilizing time series current signals or time series magnetic field signals, wavelet coefficients of magnetic field signals are taken into consideration as a novel approach. These coefficients are processed by multifractal analysis, and finally, the integrity of the pressboard is determined as in proper mode (no failure) or in breakdown mode. [ABSTRACT FROM AUTHOR]

In this contribution, the influence of free radical concentration on the electrostatic charging tendency (ECT) of aged mineral oils is reported. A free radical reagent, 2,2 diphenyl-1-picrylhydrazyl, is used to assess the relative concentration of free radicals. Prior to the ECT measurement, the oil samples were subjected to various stresses (corona discharges, multiple arcs or local overheating). Different ASTM standards were considered to assess the physicochemical properties before and after stress applications. It is found that free radicals affect the ECT. The theoretical premises that, free radicals may contribute to the ECT, are experimentally confirmed under laboratory conditions. The reason why this information is important is that ECT can significantly affect partial discharge inception in in-service transformers. [ABSTRACT FROM AUTHOR]

The mineral oil (MO) in conjunction with paper is the main insulation components in oil-immersed transformers; their insulation properties play a significant role in the safe and stable operation of power transformers. To strengthen the insulation level of ultra-high voltage transformer and to reduce its size and weight, it is imminent to enhance the insulating performance of transformer oil and oil-impregnated cellulose. Recently, a unique novel effort of suspension of nanorods (NRs) into MO has been carried out and the results have exhibited improved insulation characteristics of transformer oil. The Al2O3 NRs with favorable features were prepared in our laboratory to develop transformer oil/paper insulation system with better insulation performance. The transformer oil-based nanofluids (NFs) were prepared with nanorod shape, oleic acid surface modification and 0.8 g/L concentration of NRs. The impregnated pressboards were prepared by impregnating them into dried oil and NF under vacuum beneath 1 kPa at 80 °C for almost 48 h to obtain the oil-impregnated pressboard (OIP) and nanofluid-impregnated pressboard (NIP), respectively. The interface at solid/liquid is considered weak link and is the main reason of oil-filled transformer collapse. The effect of Al2O3 NRs on creeping discharge and flashover traits of oil/pressboard (OP) interface under AC and impulse voltages were studied. Partial discharge and creeping flashover test of OIPs and NIPs were conducted. TSDC and PEA tests were applied to examine the space charge properties of OP interface before and after suspension of Al2O3 NRs. [ABSTRACT FROM AUTHOR]

To alleviate the adverse impact due to the deterioration of electrical insulation, this paper presents an electromagnetic-thermal-fluid analysis for the prediction of temperature distribution in transformers with oil-based nanofluids or pure oil. The core loss and copper loss are taken as the heat sources for the temperature analysis using computational fluid dynamics. To strive for computing the temperature distribution accurately in the nano-oil, an effective numerical method using finite volume method and improved physical parameter model are employed. Numerical simulation of the thermal performance of the nanofluid with different volumetric fractions with Al2O3 nanoparticles is compared with those using pure transformer oil in a 500 VA single-phase transformer. From the comparisons of the simulation results, it is found that the volumetric fraction 0.01% is an optimum concentration in reducing the transformer size for the same power rating. The observation is served as useful guidelines and detailed process for the design of oil-based power transformers. [ABSTRACT FROM AUTHOR]

In this paper, the dielectric characteristics of the power transformer oil are enhanced by dispersing newly synthesized nanoparticles. Two nanocrystalline powders, barium titanate (BaTiO3) and nickel ferrite (NiFe2O4), are synthesized via the tartaric acid precursor method. The change in the crystal structure, crystalline size, and microstructure of the synthesized nanoparticles as a function of annealing temperatures are characterized using X-ray diffraction and transmission electron microscope. These nanoparticles are mixed with the transformer oil considering different concentrations such as 25, 50, and 75 mg/l where the sonicator is used to disperse the nanoparticles in the oil samples. The dielectric breakdown strength is tested based on IEC standard. The results confirm the efficiency of the NiFe2O4 than BaTiO3 for enhancing the dielectric characteristics of the power transformer oils. [ABSTRACT FROM AUTHOR]

This paper presents the experimental results of physicochemical properties studies of mixtures based on mineral oil carried out in the laboratory, under thermal stress at fixed temperature with different intervals for long period. The stability study of thermal aging is an essential tool to estimate the lifetime of insulating oil and therefore for the transformer. The tests results allow to make a comparison between the different mixtures proposed and to select the best. It is shown that the degradation rate of oils represented by kinematic viscosity is changed according to ester rate under thermal stress. We can consider that mixtures are less affected than mineral oil, depending on the ester content in the mixture. Results show the strong correlation between viscosity and dissolved decay product, under thermal stress. The increase in the soluble products in the oils, generated by degradation of oil, affects the physical parameter by increasing the kinematic viscosity. These measures are the main criterion to evaluate the stability of physical parameter of oil in-service. [ABSTRACT FROM AUTHOR]

The insulating oil performance could be enhanced in high-voltage apparatus using barriers. The importance of the barrier in increasing the dielectric strength of the insulating oils in order to reduce the oil failure stresses had not been sufficiently studied. In this paper, the effects of the barrier variables on the insulation performance of the transformer oil for point-plate and plate-plate gaps were demonstrated. These variables are: gap space (d), the barrier location relative to the high-voltage electrode (a / d) %, barrier diameter (D), barrier thickness (e), electrode configurations (EC), the presence of contaminating particles, the weight of the contaminating particles (W) and the temperature of the insulating oil (T). The statistical t test was used to explain whether the effect of these parameters was significant or not. Furthermore, the above-mentioned variables were used as training variables to construct the prediction model of oil breakdown voltage considering barrier effect based on the artificial neural networks (ANN). The ANN model was developed based on the results from experimental works. Therefore, 784 samples were used as training data set and other 25 samples were taken as testing and validating samples. The results explained that the prediction ANN model had a high ability to expect the breakdown voltage for other different experiment cases. The average errors of the training and testing samples were 1.6%, and 2.66%, respectively. Therefore, the prediction accuracy could be considered as 98.4% for training and 97.34% for testing. [ABSTRACT FROM AUTHOR]

The original transient thermal model of transformers with ONAN cooling was developed in a previous work by the authors. The parameters of the model are determined from inexpensive measurements in the short-circuit heating experiment. Two problems exist in the on-line application of the model: unknown starting hot-spot temperature and variation in the thermal parameters in a long-term transformer operation. Both are discussed in this paper, and the solution through the application of a thermal observer is also discussed. [ABSTRACT FROM AUTHOR]