Characterization of transformer oil is crucial for ensuring long-term reliability in the operation of power transformers. IEEE C.57.106 (2015) and IEEE C.57.637 (2015) classify fresh and in-service transformer oil based on its ageing severity with five mandatory dielectric properties, including AC breakdown voltage, water content, interfacial tension, dissipation factor, and neutralization number. However, this classification strategy allows quite a significant variation in the dielectric properties of oil samples belonging to the same oil class. It also does not directly suggest the exact reconditioning/reclamation process to pursue. Hence, the present work proposes categorizing in-service and fresh oil samples into six oil propositions: A, B, C, D, E, and F, based on the five above-mentioned tests with justified follow-up actions for each oil category. It also proposes a single partial discharge measurement as an alternative to the five tests in order to save time and resources. The prominent features of the phase-resolved partial discharge patterns are extracted to form Z marginal histograms. The oil samples are then classified using the Multinomial Naive Bayes analysis and Weibull probability density function. Finally, the oil classification, with a least error rate of 1.67%, is reported after post-processing with the Weibull analysis at 1.4(degrees) phase windows.

Natural ester nanofluids offer improved dielectric and thermal properties than non-biodegradable mineral oil. In the present work, cellulose samples impregnated with unfilled and nanofilled mineral oil and ester are subjected to thermal aging at 130 degrees C for 168, 336, 504, and 1200 h under open beaker conditions. After 1200 h of thermal stress, the AC breakdown voltage, thermal conductivity, and tensile strength of natural ester impregnated cellulose is 36.13%, 0.46%, and 1.09 times higher than that of mineral oil impregnated cellulose samples, respectively. Also, the AC Breakdown voltage, thermal conductivity, and tensile strength of 0.02 wt% natural ester nanofluid impregnated cellulose is 37.81%, 1.7%, and 1.1 times higher than that of cellulose impregnated with 0.02 wt% mineral oil nanofluid, respectively. The present measurements demonstrate an improvement in the solid insulation performance with natural ester nanofluids, suggesting a possibility of extended service life of solid insulation in liquid filled high voltage transformers.

Gas Insulated Transmission Lines (GIL) and Gas Insulated Substations (GIS) are the technologies that utilize less space than conventional methods for the transmission of high power. This high power transmission necessitates electrical insulators (spacers) with high mechanical stability, dielectric properties and thermal conductivity. In this context, a number of novel materials and preparation methods are under research for developing a suitable material for the spacer. The present work aims to characterize epoxy alumina nanocomposites filled with surface-functionalized alumina nanoparticles. Alumina nanoparticles were functionalized using silane coupling agents; (3-Aminopropyl) triethoxy silane (APTES) and (3-Glycidyloxypropyl) trimethoxy silane (GPTMS); to analyze their effects on the performance of epoxy nanocomposites. The primary investigation shows that the nanocomposites filled with APTES treated alumina nanoparticles increase the volume resistivity, tensile strength, and thermal conductivity by 11%, 17% and 11%, respectively, as compared to unfilled epoxy. These results indicate that the epoxy nanocomposites filled with APTES treated alumina nanoparticles can be a promising substitute for epoxy spacer used in GIL and GIS.

In this article, a method is proposed to classify the in-service transformer oil by assessing the level of degradation in oil. Initially, 135 oil samples are classified into Classes 1–3 as per IEEE C57.106 (2015) standard by the conventional techniques which require five tests. The proposed method attempts to classify the oil samples by a single nondestructive partial discharge (PD) test measurement. The PD data of the 135 transformer oil samples are represented as 1-D histograms and classified by statistical analysis using histogram similarity measures (HSM) including cross correlation test, Kolmogorov–Smirnov (KS) distances, and chi-square test. This classification technique achieves an accuracy of 94.9%. The results are further subjected to class likelihood measures in the postprocessing stage, and this improves the accuracy of classification to 97.5% establishing the efficiency of the proposed method.

The presence of partial discharges (PD) in high voltage insulation is one of the prime factors responsible for equipment failure. High-Frequency Current Transformer (HFCT) is a simple and relatively cheap instrument that can be used for the detection of partial discharges. However, it is very important to characterize or identify the transfer function of HFCT for the identification of PD. PD current pulses have a wide frequency band and hence the measurement of the wideband transfer function will be advantageous in providing better resolution for the PD current pulses. This paper focuses on the characterization of the transfer function of the HFCT using an innovative method. In this work, an HFCT sensor has been excited with two different pulses with two different ranges of frequency content (20 kHz- 10 MHz and 1 MHz – 500 MHz) and the response signals are measured. Combining these responses, the transfer function is estimated, covering a much wider range than the frequency range of individual excitation pulses. The identified transfer function shows very good matching with experimental measurements.

One of the major insulation degradation processes that enhances the risk of equipment failure is partial discharges (PD). The severity of the damage caused by PD will depend on the type of PD source present in the insulation system, ie. Corona discharges, voids, surface discharges, etc. Therefore, early detection and identification of PD sources are very useful for the proper assessment of insulation health. The insulation material investigated in the present case is an oil-impregnated pressboard. Three types of PD sources, void, corona, and surface discharges, were identified from PD signals measured using a high-frequency current transformer (HFCT). It was observed that Time-Frequency mapping is an efficient technique to discriminate corona, surface discharges, and void discharges when they are present standalone in the insulation.

Statistical analysis of the Partial Discharge (PD) data is proposed for the quality assessment of the transformer oil. Initially, the PD data is represented as X and Y marginal histograms and the Histogram Similarity Measures (HSM) are adopted for the supervised classification of the transformer oil. Three different HSM techniques: cross-correlation, Chi-square and the Kolmogorov-Smirnov tests are employed. 45 transformer oil samples collected from the State Electricity Board are used for the classification of the oil samples to Class 1, Class 2 and Class 3 as per IEEE C.57.106-2006 standard. The proposed statistical method using the Chi-square test classifies the oil samples with an accuracy of 100%, proving its superior performance over any other physical, chemical or electrical tests suggested in the literature.

The increasing demand for clean and reliable power has directed the research toward biodegradable green insulating oils or natural esters for transformers. Efforts to enhance the ester oil properties by nanoparticle addition have resulted in nanofluids with improved dielectric and thermal properties. This chapter deals with the development and characterization of natural ester nanofluids. The preparation of nanofluids, issues related to stability, and methods to improve stability are explained in detail. A review of the physical, electrical, and thermal properties of natural ester nanofluids and an interpretation of the possible mechanisms behind the property variations are presented. Comparison of natural ester nanofluids is conducted based on the property enhancement attained with the addition of different types of nanoparticles. A study of the long-term performance of natural ester nanofluids is carried out based on the available literature. The potential areas for future research, related to the feasibility of natural ester nanofluids in transformers, are discussed.

Dissolved Gas Analysis (DGA) is a worldwide accepted diagnostic technique for detecting transformer incipient faults. Among the DGA interpretation methods, Duval triangle is the most frequently used technique. Even though Duval triangle interprets the DGA data and relates them to the transformer fault condition, classifying the faults that fall in the overlap region is still a tedious task. To overcome this, an attempt is made to adopt statistical learning techniques to identify an underlining relationship between the input and output data. In this paper, Maximum Likelihood Classification (MLC) and conditional probability techniques are proposed to predict the class of the incipient fault that maximizes the likelihood of the observed DGA data. In the overlapping or the boundary regions, MLC assigns DGA data to the corresponding fault class that has the highest probability of occurrence. The proposed method with Duval triangle has increased the efficiency of classifying the DGA data and an accuracy of 91.14% is achieved.

In this contribution, a novel technique to accurately sense the thermal aging of the epoxy alumina nano-composites employing complex electric modulus is proposed. To sense the thermal ageing of epoxy nano-composites accurately, knowledge of relaxation behavior is necessary. However, relaxation behavior cannot be understood completely due to electrode polarization and charge transport effects at high temperatures and at low frequencies. To overcome this problem, electric modulus which is defined as the inverse of the complex permittivity, is being proposed in this study to analyze the aging behavior of the epoxy alumina nano-composites quantitatively. For this purpose, three epoxy resin samples mixed with alumina (Al 2 O 3 ) nano-fillers with different filler concentrations were prepared and thermal aging of the same samples was done for 100 hours, 200 hours, 300 hours and 400 hours, respectively. For each sample, the complex dielectric modulus (M*( ω)) was computed using frequency domain spectroscopy measurement to observe their frequency dependent relaxation behaviors. The variation of the real (M ' ( ω)) and imaginary (M '' ( ω)) part of ( M*( ω)) over the frequency range from 1 mHz to 10 kHz was further fitted using Cole-Cole (C-C) model. From the nature of variation of M '' ( ω) spectrum and the fitting parameters of the C-C model, two characteristic parameters were extracted to quantitatively describe the thermal aging of the epoxy nano-composite samples. Investigations revealed that the extracted parameters can be accurately used to sense the aging condition of the epoxy nano-composites.

The degradation occurring in epoxy nanocomposites due to electrical treeing has been studied through a simulation using cellular automata models. Epoxy nanocomposites containing alumina, zinc oxide and titania nanofillers, each with weight proportions of 1%, 2% and 5% are subjected to study along with pure epoxy. The simulation has been carried out by inserting the dielectric material between need...

The operating life and efficiency of a transformer depends on the electrical and thermal performance of its insulation system. The present work involves the preparation and thermal characterization of mineral oil and natural ester nanofluids. An electro-thermal analogous model is developed to determine the thermal resistance and thermal capacitance of the unfilled and nanofilled oils. Simulation model of unfilled and nanofilled oils using Comsol Multphysics is developed to obtain the temperature time plot, from which the values of the thermal resistance and thermal capacitance of the unfilled and nanofilled oils are calculated. The simulation results are validated experimentally. It is observed that the thermal resistance and thermal capacitance values of both mineral oil and natural ester obtained from simulation and experiment decrease with the addition of nanoparticles. This indicates an improvement in the heat distribution and dissipation in nanofluids when compared to the unfilled counterparts, rendering them better insulating oils for transformers.

Classification of transformer oil as per IEC C.57.106-2006 is performed using Partial Discharge (PD) measurements. Fresh and used oil samples procured from the State Electricity Board are used for the classification. Histogram Similarity Measures (HSM) like Kolmogorov Smirnov (KS) test, Chi-square test and Cross-correlation are used to find the similarity between the histograms of the PD data and classify them. Subsequently, to predict the accuracy of the decision made while classifying, a probability is associated with each classification. The test statistics of HSM are fitted using Beta, KS and Chi-square distributions and their class likelihood probabilities are evaluated. Eventually, the class assignment and related probabilities from different PD measurements are added up to get a final class assignment and probability value for the test oil sample.

Surface discharge phenomenon in epoxy alumina nanocomposites have been investigated here, by way of preparing epoxy nanocomposites containing alumina nanoparticles in different weight proportions of 1%, 2% and 5% and subjecting them to electrical ageing for a predetermined duration. The partial discharge patterns at intervals of 4 hours have been recorded for those insulator samples and the pure e...

The present work aims at the preparation and characterization of a stable soybean natural ester nanofluid using alumina nanofiller, as an alternative to the conventional mineral oil. Stability using UV-Vis spectrophotometer and turbidity meter for various concentrations of nanofluids is reported and analyzed. Natural ester nanofluids with 0.02 wt% nanofiller concentration shows the highest stability. AC breakdown voltage shows a 21.5 and 27.9% increase for unfilled and 0.02 wt% nanofilled natural ester, respectively, when compared to the unfilled mineral oil. An improvement of 5.4 and 14.6% in the thermal conductivity can be observed for unfilled and 0.1 wt% nanofilled natural ester, respectively, when compared to that of unfilled mineral oil.

Transformers are critical components of electric power transmission and distribution system. Mineral oil (MO) based multi-particle nanofluid (MPNF) were prepared with an intention to enhance electrical properties of MO by incorporating Al 2 O 3 and TiO 2 nanoparticles. Filler loading concentration and mixing ratio, which is the ratio between Al 2 O 3 and TiO 2 nanoparticle content is optimized by analyzing the simulation results. AC breakdown strength of the prepared samples were measured. It is found that, MPNF sample having a filler loading concentration of 0.1weight percentage (wt%) and mixing ratio of 9:1 shows highest AC breakdown strength. This sample shows an enhancement of 38.4%, 15.86%, and 17.41%, w.r.t pure oil, Al 2 O 3 and TiO 2 NFs having same filler loading concentration.

This paper proposes a method to analyze electric field distribution in the composite structure quantitatively based on statistical parameters through electric field simulations. A geometry of many nanoparticles each surrounded by interphase region in a random arrangement is modeled for simulation ensuring uniform distribution. General factorial design in design of experiments method is utilized in performing simulations to identify the significance of the factors considered. Volume fraction, permittivity, radius and interphase thickness of the nanoparticle inclusion are treated as the factors. Volume fraction is varied from 1% to 10% in ten steps to simulate the cases of particle interactions which include nanoparticles far apart, moderately apart, interphases of particles touching to each other and overlap of interphases. Particle permittivity is varied from 2 to 8 while keeping polymer permittivity constant. Conductivity of the nanoparticle is varied similar to permittivity variations. Radius and interphase thickness are varied from 20-50 nm and 20-40 nm respectively. Coefficient of variation as well as mean value of electric field intensity are chosen as output or response variables. Multiple linear regression is employed to formulate the relationship between the factors considered and the statistical parameters. Significance and effect of these factors on electric field distribution is discussed. Interparticle distance and interphase volume fraction are found to be the critical factors affecting variance and mean of electric field intensity in nanodielectrics.

In this study, the relationship between thermal ageing and charge trapping properties of epoxy-based nanocomposites has been investigated. With ageing, any dielectric material undergoes thorough degradation. This degradation significantly affects the space charge accumulation and charge trapping behaviour of the dielectric, which are very important parameters for insulation health under high-voltage direct current (HVDC) environment. In this work, an improved model based on the isothermal relaxation current (IRC) has been developed to study the charge trapping behaviour of pure epoxy and epoxy alumina (Al(2)O(3)) nano-composites at different ageing conditions. A methodology based on polarisation–depolarisation current (PDC) measurements has been proposed to identify the current component due to a dipolar relaxation in measured total IRC. This will help to identify the trap distribution characteristics more accurately compared to conventional IRC measurements. It was experimentally observed that the addition of nanoparticles significantly reduces trapped charge formation and reduces thermal degradation. It is observed that aging leads to the generation of deeper traps, while the addition of Al(2)O(3) nanoparticles mainly enhances the density of shallow traps. Results presented in this work indicate that epoxy–alumina nanocomposites are very much suitable in HVDC applications from the perspective of trapped charge accumulation.

AC breakdown strength is a critical parameter in the performance of an insulating liquid. Developing a method to predict the breakdown strength of dielectric liquid makes it possible to predetermine the voltage withstand capability of materials. Prediction of breakdown voltage of nanofluids (NFs) has great significance to the optimization of filler loading concentration of NFs. Based on the electric field distribution inside the dielectric liquid samples, this paper proposes a prediction method for AC breakdown voltage (BDV) using curve fitting technique. By analyzing various statistical parameters of electric field distribution such as average, standard deviation, variance, skewness, and kurtosis along with AC breakdown voltage values of NFs, a relationship connecting these parameters and breakdown voltage is formed. Using this relation and statistical parameters of electric field distribution obtained from simulation model, breakdown voltage is predicted for another set of NFs. TiO2 NFs are used for framing the equations and breakdown voltage is predicted for Al2O3 NFs. It is observed that AC breakdown strength is predicted with least error by using kurtosis as the statistical parameter. Test results show that the relative errors between predicted values and actual values are all less than 8%, which indicates the accuracy and reliability of this method.