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In most industrialized countries, power transformers built several decades ago are approaching the end of their operational lifespan. The ongoing energy transition, focused on developing 100% renewable energy sources and accelerating global transportation electrification, further exacerbates these assets. Combined with rising electricity demand, there is an increasing risk of critical transformers' degradation acceleration. In this context, understanding the aging mechanisms of the insulation system inside these essential assets, which form the core of every energy network, becomes paramount for today's managers and engineers responsible for their operations. The acids generated through oil oxidation can be classified into two categories: low molecular weight acids (LMAs), which are inherently more hydrophilic and consequently have a greater impact on the degradation rate of solid insulation through hydrolysis, and high molecular weight acids (HMAs), which do not significantly contribute to the degradation of paper insulation. This study specifically addresses the impact of acids generated through oil oxidation—focusing on LMAs. New oil samples were infused with different ratios of LMAs before impregnation. The impregnated paper samples underwent thermal aging at 115 °C. Different physicochemical and dielectric properties were investigated. The investigations revealed that oils blended with formic acid exhibited more adverse effects on the insulation system compared to other LMAs. This information is essential for industry professionals seeking to mitigate the risks associated with transformer degradation and extend the lifespan of these critical assets during the energy transition. [ABSTRACT FROM AUTHOR]

The insulation status of oil-impregnated paper (OIP) bushings directly indicates their operational condition. Damp defects constitute the primary factor contributing to the deterioration of oil-paper insulation. In this paper, the overall moisture distribution of capacitor cores with three damp defects (steady-state, transient, anzd tail moisture damp defect) and its effect on the electric field were studied. A moisture diffusion model for OIP bushing was established based on Finite Element Method (FEM) software, and it was also verified by moisture diffusion experiments. Then, the overall moisture distribution of the capacitor core under three damp defects was investigated. The findings indicate significant variation in the overall moisture distribution of the capacitor core influenced by factors such as aluminum foil and temperature. Furthermore, the relative permittivity of the oil-impregnated paper was computed. Subsequently, the impact of various moisture-induced defects on the electric field was determined. Simulation results unveiled distortion in the electric field of each electrode within the capacitor core. Specifically, the electric field of the zero electrode increased, while that of the outermost electrode decreased in all three damp defect scenarios. The conclusions drawn can thus be used for intelligent operation and condition assessment of OIP bushings.

Signal processing helps monitor the condition of power equipment. Partial discharge (PD) signals used in condition-based maintenance give crucial information in the diagnosis of degradation of insulation. The acoustic emission technique (AET) is one of the most widely used techniques in PD signal analysis due to its inherent advantages. Analyzing acoustic emission partial discharge (AEPD) signals in the wavelet-domain provides critical insights into the location and type of the sources of PD. Selection of the most suitable mother wavelet in applying discrete wavelet transform (DWT) on AEPD signals is important as it will directly impact the outcome. For this selection, 36 wavelets belonging to the Daubechies, Symlets, Coiflets, and Bi-orthogonal families are investigated. For this purpose, five experimentally collected AEPD test signals are used. The selection is based on the "accuracy of wavelet decomposition results" in this work, probably for the first time. One mother wavelet from each family is individually shortlisted for all three performances, namely (a) reconstruction, (b) denoising, and (c) compression, by computing and comparing their commonly used metrics. Further, based on percentage energy criteria, the most suitable mother wavelets are identified as coif3, coif4, and coif5, respectively, for the three performances. [ABSTRACT FROM AUTHOR]

The insulation status of oil-impregnated paper (OIP) bushings directly indicates their operational condition. Damp defects constitute the primary factor contributing to the deterioration of oil-paper insulation. In this paper, the overall moisture distribution of capacitor cores with three damp defects (steady-state, transient, anzd tail moisture damp defect) and its effect on the electric field were studied. A moisture diffusion model for OIP bushing was established based on Finite Element Method (FEM) software, and it was also verified by moisture diffusion experiments. Then, the overall moisture distribution of the capacitor core under three damp defects was investigated. The findings indicate significant variation in the overall moisture distribution of the capacitor core influenced by factors such as aluminum foil and temperature. Furthermore, the relative permittivity of the oilimpregnated paper was computed. Subsequently, the impact of various moisture-induced defects on the electric field was determined. Simulation results unveiled distortion in the electric field of each electrode within the capacitor core. Specifically, the electric field of the zero electrode increased, while that of the outermost electrode decreased in all three damp defect scenarios. The conclusions drawn can thus be used for intelligent operation and condition assessment of OIP bushings. [ABSTRACT FROM AUTHOR]

Power transformers are the most important and expensive assets in high-voltage power systems. To ensure an adequate level of reliability throughout the transformer's lifetime, its maintenance strategy must be well defined. When an incipient fault occurs in the transformer insulation, a gas concentration pattern, representative of the type of fault, is generated. Fault-identification methods use gas concentrations and their ratios to identify the type of fault. None of the traditional or new fault-identification methods attempt to detect transformer oil contamination from on-load tap changer (OLTC) gases. In this study, from dissolved gas analysis (DGA) samples of transformers identified as contaminated in a previous study, fault-identification methods based on graphical representations were used to observe the patterns of results. From such patterns, Duval's triangle and pentagon methods were modified to include a new zone indicating oil contamination (OC) from OLTC gases. Finally, the proposed modifications were validated using 75 DGA samples extracted from previous studies that were identified as D1 or D2 faults or contaminated from OLTC. This validation showed that only 14.7% and 13.3% of the DGA samples fell within the new OC zone of the proposed triangle and pentagon, respectively. [ABSTRACT FROM AUTHOR]

In this article, the effect of using phase change materials to improve heat transfer in power distribution transformers has been investigated experimentally. To enhance the cooling of the transformer, a new method has been proposed, which involves adding paraffin inside aluminium containers that are sealed to the transformer oil. The test setup includes an electric transformer filled with transformer oil, two electrical heaters, a power regulator, a thermal camera, oil insulation measuring device, and temperature sensors placed at various locations. The experimental results demonstrated that the addition of phase change materials to the electronic transformer oil led to a decrease in the temperature of the transformer, particularly in summer weather conditions. Additionally, the mean temperature of the transformer oil was reduced from 46.7 to 42.5 degrees Celsius by adding 8 kg of paraffin. However, it was observed that when the temperature increases suddenly and rapidly within an hour, these materials are ineffective in dissipating the heat and reducing the temperature of the transformers. Additionally, the research examines the impact of continuous and high-temperature increases on the oil electrical insulation. The results revealed that using phase change materials increased the voltage that the oil could withstand as an electrical insulator from 56.8 kV to 61 kV. [ABSTRACT FROM AUTHOR]

Power transformers, integral to electrical systems, necessitate substantial investment. Their operational longevity hinges critically on the quality of their insulation system. This study focuses on evaluating the condition of both oil and solid insulation within transformers, which is vital for preventing operational failures. The condition of oil insulation is influenced by a blend of chemical, electrical, and physical properties, while the aging of solid insulation is an irreversible process that ultimately dictates the end of a transformer's service life. Recent advancements, notably the guidelines from CIGRE TB 761, have provided a comprehensive framework for assessing transformer insulation. These guidelines employ a multi-parameter approach to derive a consolidated insulation index. This study applies the CIGRE TB 761 methodology to a population of 357 power transformers, facilitating a detailed comparative assessment of the oil and solid insulation. This paper discusses the methodology used and presents the results, highlighting the evaluation of both oil and solid insulation aspects. Furthermore, it investigates the relationships between various testing parameters and the overall insulation index in relation to the transformer's age. For oil insulation, key parameters include interfacial tension, color, breakdown voltage, acidity, and moisture content. Solid insulation assessment predominantly revolves around parameters such as methane, ethylene, ethane, carbon monoxide, carbon dioxide, CO2/CO ratio, and DP value. This study also correlates the insulation condition in power transformers to the operational age, and thereby contributing to more effective monitoring and maintenance strategies. [ABSTRACT FROM AUTHOR]

A contemporary research topic of interest is hybrid nanofluids. The goal of this study is to see how hybrid nanoparticles in transformer oil affect the failure probability, reliability, and streamer dynamics when subjected to massive electrical stress. A 2D axisymmetric hydrodynamic drift-diffusion model is developed to study the streamer dynamics in a hybrid nanofluid with different charging times. Failure probability and reliability for different nanofluids are estimated using the Weibull distribution function. The statistical study reveals that the hybrid nanofluid is more reliable and less dangerous, extending the transformers' service life. When compared to mineral oil and individually dispersed nanoparticles, a homogeneous mixture of Fe3O4 and Al2O3 nanoparticles reduces failure rate by 73.94%, 56.29%, and 30.41%, respectively. For positive streamer dynamics, the influence of altering the charging time of distributed multiple nanoparticles was explored. The dispersion of hybrid nanoparticles in transformer oil has been found to minimize the ionization rate, streamer velocity, and streamer re-ignition rate.

The oil spill detection of synthetic aperture radar (SAR) images has had great success. Existing deep-learning-based methods make predictions mainly based on the U-Net structure and Transformer, which fail to blend the local and global information generated by other different feature maps. In this letter, we proposed a dual-stream Unet (DS-Unet) for oil spill detection of SAR images. In particular, the proposed DS-Unet consists of two modules: an edge feature extraction module for extracting the local information and an interscale alignment module for capturing the global information. Moreover, an edge extraction branch is applied to handle the speckle noise of SAR images. Extensive experiments on two real-world datasets (Palsar and Sentinel) have shown that the proposed DS-Unet outperforms many existing state-of-the-art methods. [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]

Lifetime prediction of the power transformer plays an important role in maintaining the stable operation of power equipment. However, due to the complexity of insulation structure degenerative process, the existing methods featuring high cost and low precision are not effective enough in transformer life time prediction. Meanwhile, how to effectively and promptly respond to a new prediction scenario of insufficient and limited data is a common challenge for all the data-driven prediction methods. To address these concerns, a prediction approach of a back adoptive adjustment transfer learning scheme (BAATL) is proposed for lifetime degradation prediction of the dry-type transformer. The power transformer condition monitoring data of Supervisory Control and Data Acquisition system is conducted as the data driven. A deep neural network, a transfer learning module and a back adjustment module are constructed to realize feature extraction, domain adaptation and prediction network optimization. The proposed scheme is able to improve prediction accuracy and resolves the problems and drawbacks of traditional prediction methods, and presents its superior portability and application potential in the case of data shortage and scenario change. With authentic datasets, simulation tests performed on the condition monitoring data of dry-type transformers prove the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Owing to increasing power demand, new technologies, such as solid-state transformers, are progressively researched and improved. Thereby, galvanic isolation and voltage adaptation are achieved by medium frequency transformers. To reach high power density designs, transformer windings can be realized by means of copper pipes, allowing for efficient liquid cooling. This paper provides insight to many relevant aspects for the use of hollow copper conductors as windings. This includes electrical and thermal modeling, as well as considerations regarding mechanical implementations. Additionally, the winding concept is investigated based on a 1 MW medium frequency transformer prototype.

The evaluation of the state of transformer oil-immersed insulation based on the traditional methods becomes unreliable due to the nonuniform aging effect. To address this issue, a novel model for accessing the aging information of transformer oil-immersed insulation at the hotspot is proposed. In this article, frequency-domain spectroscopy (FDS) is selected as the carrier for characterizing the aging states of oil-immersed insulation. The multiobjective function for reversing the FDS of the hotspot is constructed based on the dielectric response equivalent circuit. Then, the nondominated sorting genetic algorithm with the multiconstraint scheme is proposed to solve the multiobjective function, by which the FDS data of the transformer hotspot region are acquired. The verification results indicate that the average standard deviation between the computed FDS of the hotspot region and the measured data is less than 0.26. The contribution of this article is in the exploration of the proposed model as a potential tool to extract the FDS data of the hotspot region. This will promote a more reliable aging evaluation of the transformer oil-immersed paper insulation at the hotspot. [ABSTRACT FROM AUTHOR]

Harmonic blocking (HB)-based differential relays, generally employed in the transformer protection, are likely to fail to interrupt internal short-circuit faults that occur under geomagnetically induced current (GIC) conditions. This paper proposes a waveform-based approach that enhances the differential protection dependability under the GIC conditions by detecting the GIC and overriding the HB signals for the internal faults. A novel algorithm is developed for the GIC detection, which employs three-phase instantaneous differential current waveforms and recognizes a GIC condition if the waveforms are all asymmetric in one direction. The proposed waveform-based approach also utilizes trip request signals of the differential relay's fault detection module to unblock the relay for the internal faults during the GIC. The results show that the proposed waveform-based approach (i) can successfully unblock the differential protection for the internal faults under the GIC conditions, (ii) does not malfunction for the external faults during the GIC, (iii) discriminates transformer energization cases from the GIC conditions, and (iv) is immune to the measurement noise. The current transformer (CT) saturation is also considered in the studies. The proposed approach's effectiveness is verified for different transformer types, including three-phase and single-phase transformers. [ABSTRACT FROM AUTHOR]

The future demand for electric vehicle (EV) charging power is expected to increase the peak loading of existing distribution transformers. In the absence of appropriate solutions, this may lead to the replacement of transformers with higher-rated types, incurring significant expense. Therefore, a novel approach is proposed to improve the management of transformer EV charging loads, avoiding or postponing the need for a transformer upgrade. It allows for the determination of whether loads that exceed transformer nameplate ratings are acceptable in terms of power level, time and duration. This is achieved with knowledge of the maximum charging duration, transformer thermal time constants, current loading levels, voltage regulation limits, oil pressure and temperature limits, and desired transformer lifetime requirements. The results of a case study are presented based on a future EV car park scenario for which an EV charging load is added to the existing base load on a 300 kVA transformer. The results show the proposed approach has advantages over the traditional approach of limiting loading to nameplate levels. These include reduced waiting time and increased levels of charging power, which are achieved without compromising the desired transformer lifetime target. [ABSTRACT FROM AUTHOR]

Conventionally, the 2-parameter Weibull model, Arrhenius-Weibull model, has been used vastly for transformer aging-dependent unavailability modeling. However, this model only uses the lifetime feature to describe the transformer's degradation process and to calibrate the Weibull parameters, which harms the accuracy of aging-dependent unavailability forecasting. In response, this paper develops a 3-calibratable-parameter Weibull model for evaluating the transformer aging-dependent unavailability. In the proposed model, both the individualized aging threshold and lifetime are taken into the calibration of the Weibull parameters to accurately characterize the heterogeneity in transformer populations. First, a degree of polymerization (DP) analysis and Monte Carlo Simulation (MCS) based approach is proposed for estimating the transformers’ uncertain aging thresholds and lifetimes. Then, the Maximum Likelihood Estimate and Particle Swarm Optimization are jointly adopted to model the relationship among the calibratable Weibull parameters, aging threshold, and lifetime. Finally, the analytical formula of aging-dependent unavailability is derived from the established 3-calibratable-parameter Weibull model using an integral-discretization method. A real utility application example in China's Chongqing power system has been presented to validate and demonstrate the practicality and usefulness of this method. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis (DGA) is an important method to identify internal faults of transformers. A novel predictive method for dissolved gas content in transformer oil based on temporal convolutional network (TCN) and graph convolutional network (GCN) is proposed in this article. First, a TCN based on dilated causal convolution algorithm is designed to extract feature information from both current data and historical data of each gas content. Since the correlation coefficients among different gases may affect the accuracy of the prediction, a topological structure diagram is constructed to describe the relationship among different gases. Then, a GCN is designed to predict the gas content in the desired time horizon by using the information obtained from the topological structure diagram. The elements in the adjacency matrix of GCN are replaced by Pearson correlation coefficients to improve the accuracy of the prediction. The test results show that the proposed method in this article achieves high accuracy for the prediction of dissolved gas content in transformer oil. Compared with back propagation neural network, long short-term memory network and TCN, the mean absolute error of the predicted result by the proposed method is reduced by 12.23, 11.18, and 3.81%, respectively. [ABSTRACT FROM AUTHOR]

It is now possible with dissolved gas analysis and Duval Triangle 1 to distinguish between arcing faults in paper and in oil in transformers. [ABSTRACT FROM AUTHOR]

A quasi-distributed vibration sensing system based on a phase-sensitive optical frequency-domain reflectometer is proposed for monitoring the vibration of power transformer oil tanks. Compared with traditional accelerometers, the proposed system has high electromagnetic interference immunity. In this article, the principles of localization and vibration sensing are introduced. Then, the system layout and the vibration signal demodulation process are explained in detail. Next, the vibration sensing performance of the system is calibrated experimentally. Finally, vibration monitoring tests are carried out in a laboratory environment and on-site to verify the vibration detection capability of the system. The proposed system can be used to measure the time domain, frequency domain, and spatial distribution of oil tank vibration, which can be used for the online monitoring of the mechanical state of power transformers. [ABSTRACT FROM AUTHOR]

Large Power Transformers (LPT) are a major element of the modern transmission grid. The tanks of traditional LPTs are made of low carbon steel which could contribute to their overall weight. This study provides a preliminary evaluation and selection process to modernize LPT tank materials following well-established principles of advanced materials engineering. A primary objective of this study was to select an advanced material for an LPT tank to make it more resilient to potential in-service structural damage. The selection process resulted in five advanced composite materials based on glass and carbon fibers with epoxy and phenolic resins employing two different fiber architectures. These composites were further screened to identify the most suitable materials considering several environmental conditions that the tank could be exposed to in service. As part of the final materials selection analysis, the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method was employed for the final ranking of the LPT tank replacement materials. Preliminary recommendations of modifications to the composites to withstand the effects of the thermal, electrical, and electromagnetic environment of the LPT are presented. [ABSTRACT FROM AUTHOR]

Power transformers are critical assets for generation and grid. Controlling their aging is an essential task to ensure the availability of installations, and to optimize maintenance and operational costs over their lifetime. Large power transformers do not always operate at their full cooling capacity, and the cooling efficiency is often adjusted by steps (each different step being also called a “cooling stage”) based on the top-oil, winding temperature indicator or hot-spot temperature. Various dynamic thermal models exist in the literature, but none of them provide easy-to-use correction factors to consider the effect of the cooling stage on the coolers’ thermal resistance as well as on top-oil and hot-spot gradients. The purpose of this article is to propose a simple yet accurate model for the thermal monitoring of Oil Directed Air Forced power transformers. Several applications of the model are given such as cooling efficiency monitoring, evaluation of the load capability (or loadability), and coolers temperature control improvement. [ABSTRACT FROM AUTHOR]

Electricity distribution companies are in-charge-of supplying stable electric energy to urban customers over a wide geographical area. In these distribution networks, large number of Distribution-Transformers (DTs) are installed on towers or in electric-rooms with the ratings below 1000 kVA, which are the most important assets of distribution companies. Periodic maintenance and test services are not economically feasible for all in-operation transformers. Hence, estimating the remaining useful life and scheduling the maintenance times of DTs are of great technical and economic importance. An analytical approach is presented in this paper for predicting lifetime of DTs based on degree of polymerization. An appropriate Machine-Learning (ML) algorithm trained by measured data is selected for forecasting driven load according to the ambient temperature and humidity and day-hour schedules. The relation of Hot-Spot-Temperature in IEC60076∼7 is modified based on the measured data for accounting unbalanced-loading. Neutral-current is predicted using a trained ML-model. Another approach is proposed for determining times of oil filtering services aiming to minimize the maintenance cost and maximizing the remaining life. The proposed lifetime estimation method is validated by comparing its results with the damaged DT data of a sample network chosen as the case studies for this research, i.e., Bushehr province, Iran. [ABSTRACT FROM AUTHOR]

In this paper, a reduced computational fluid dynamics (CFD) model is proposed for determining the total heat dissipation and the oil temperature distribution of transformer radiators in oil natural air natural (ONAN) cooling mode. In the reduced radiator CFD model, the air flow simulation is replaced with an optimized air heat transfer coefficient (hair) equation. An experimentally verified full radiator CFD model, which includes the air flow simulation, has been introduced as a benchmark model for evaluating the validity of the reduced model. It was found that one ${{\boldsymbol{h}}_{{\boldsymbol{air}}}}$ can provide sufficient accuracy in simulating the air convection effect of transformer radiators. An ${{\boldsymbol{h}}_{{\boldsymbol{air}}}}$ equation was derived through a parametric study using the full radiator CFD model. The performance of the proposed ${{\boldsymbol{h}}_{{\boldsymbol{air}}}}$ equation was compared with the existing empirical equations, and its validities for different insulating liquids and different ambient temperatures were proven by additional CFD modelling. This ${{\boldsymbol{h}}_{{\boldsymbol{air}}}}$ equation can be transformed into a dimensionless form to help the radiator design and facilitate the complete-cooling-loop (CCL) based transformer CFD modelling. [ABSTRACT FROM AUTHOR]

This papers presents a numerical approach based on computational fluid dynamics (CFD) that was developed to ensure that gravity oil-water separators at Hydro-Québec electrical substations comply with the environmental legislation in place. More precisely, multiphase simulations are performed for two operating conditions of an in service separator, and an experimental setup is used to produce an oil spill and to measure the time-varying oil concentration at the separator outlet. The results show that, for the tested conditions, the oil droplet size at a separator inlet generally follows a log-normal distribution and that the droplet diameter varies between 100 and 1000 µm with a mean value of 475 µm. Furthermore, both the experimental and numerical results show that the time required for the oil droplets to reach the separator outlet is quite shorter than the theoretical mean residence time. Finally, this study demonstrates that the numerical model predicts the separator efficiency within 1% of the measured value, and that it can be a valuable tool to investigate existing separators or to design new ones. [ABSTRACT FROM AUTHOR]

This article proposes a multiport modular single-stage current-source solid-state transformer (SST) for applications like photovoltaic, energy storage integration, electric vehicle fast charging, data center, etc. The 7.2 kV 50 kVA current-source SST consists of five input-series output-parallel modules, each based on 3.3 kV SiC reverse-blocking mosfet-plus-diode modules. The proposed SST has some unique features. First, compared with the voltage-source or matrix converter-based SSTs, the current-source SST has a unique advantage of single-stage isolated ac/dc or ac/ac conversion with an inductive dc link, but no medium-voltage (MV) ac experiments have been reported. This article for the first time demonstrates MV ac current-source SST up to 7.5 kV peak. Second, the multiport SST has a buffer port for active power decoupling (APD) or energy storage integration. The double-line-frequency power ripple from single-phase ac grid normally results in a large capacitor size in MV SSTs. The APD scheme is proposed in MV applications for the first time to enable a reduced dc link and the electrolytic capacitor-less SST with high reliability. Third, for a direct grid-connected converter without line-frequency transformer, insulation and protection are critical. A medium-frequency transformer design passes 55 kV basic-insulation level (BIL) and 60 kV high potential dielectrics withstand test with only 0.09% leakage inductance. Importantly, a lightning protection scheme is presented to protect the SST itself from 90 kV BIL impulse. Fourth, the proposed current-source SST topology is a modular soft-switching solid-state transformer (M-S4T) with full-range zero-voltage switching and controlled dv/dt for low electromagnetic interference. These concepts are verified in a three-port M-S4T prototype with forced oil cooling under single-module, stacked-module, steady-state, and dynamic operations. [ABSTRACT FROM AUTHOR]

The corrosion of transformers by multiple sulfides seriously affects the safe operation of transformers. However, there is a lack of quantitative characterization of the extent to which temperature and oxygen as well as changes in sulfide species affect the aging of oil–paper insulation. In this study, the aging characteristics of oil–paper insulation systems were analyzed in terms of the moisture in the oil, moisture in the paper, acid value, and furfural in the presence of multiple sulfides, where the temperature and the oxygen concentration are used as independent variables. A strong positive correlation between the acid value and the furfural obtained by means of Spearman’s correlation analysis. The acid value and furfural content in the oil were then analyzed and compared according to a nonparametric test, and it was found that both the oxygen changes and the temperature changes had a relatively obvious effect on the acid value in the oil. The effect of the temperature change on the furfural was greater than the effect of the oxygen change on the furfural. Finally, a multiple linear regression analysis was performed for the change in the acid value of the oil with the change in the oxygen content and temperature to obtain the linear fitting formulas for the effects of the time, temperature, oxygen content percentage, and sulfide groups on the acid value. This study can be used to quantify the magnitude of the influences of the oxygen, temperature sulfide groups, and other factors on the aging characteristic parameters of the oil–paper insulation system and to provide a certain reference for studying the quantitative characterization of the sulfur corrosion degree by using the change in the acid value in oil. [ABSTRACT FROM AUTHOR]

Natural ester insulating oil not only has a biodegradation rate of almost 100% but also meets the carbon emission requirements of China’s “Carbon Peak and Carbon Neutrality” and the “European Green Deal” proposed by the European Union. It is considered to be a good substitute for mineral insulating oil. However, due to its higher kinematic viscosity than traditional mineral oil in low-temperature environments, it limits the safety promotion and application of transformers in cold regions. First, we design an experiment to test the kinematic viscosity and density of natural ester insulating oil. Under extremely harsh experimental conditions, we measure key experimental data such as kinematic viscosity. Second, the structure of the four main triglyceride molecules is optimized, and the molecular dynamics (MD) simulation technology is used to establish an MD model that can predict the kinematic viscosity of natural ester insulating oil (−20 °C to 20 °C). Finally, the model is further simplified by the free volume theory to better predict the kinematic viscosity of natural ester insulating oil. This study makes up for the lack of laboratory low-temperature testing of the kinematic viscosity of natural ester insulating oil and provides a convenient and reliable tool for predicting the kinematic viscosity of natural ester insulating oil. It can not only predict the natural ester insulating oil mixed in various proportions but also predict the kinematic viscosity of a certain natural ester molecule. Furthermore, it also provides a guiding direction for the improvement of low-temperature kinematic viscosity of natural ester insulating oil, as well as a strong reference for predicting other properties of other substances. [ABSTRACT FROM AUTHOR]

Measuring the furan concentration in transformer oil is one of the most common methods of monitoring and evaluating insulation paper and estimating its degree of polymerization (DP). Here, methods for measuring furan concentration, especially ultraviolet-visible (UV-Vis) spectroscopy method, are reviewed on the old transformers oil. The results show that the excessive turbidity of old oils and noise at low wavelengths are the main disadvantages of this method, which reduce the measurements accuracy. The following solutions are proposed to overcome these disadvantages: 1) diluting insulation oil with hexane to reduce absorption intensity and 2) using average absorption at a given wavelength to reduce noise impact. These solutions are applied to the oil samples of ten old distribution transformer. It is observed that the correlation between 2-furaldehyde (2-FAL) concentration and average absorption intensity in 190–600 nm wavelength range is equal to ${R}^{{2}} =0.98$. A mathematical equation for calculating 2-FAL concentration based on average absorption is presented. It has been shown that to reduce the test time, a 190–400 nm range is the optimal measurement wavelength range. The results show that the average difference between the values of DP in the proposed method and the direct measurement method is equal to 77.5. [ABSTRACT FROM AUTHOR]

The detection of copper particles in transformer oil based on laser-induced breakdown spectroscopy (LIBS) technology has been preliminarily studied, but its limit of detection (LOD) needs to be further strengthened. Therefore, spark-discharge assistant method was used to solve the problem, and its enhancement mechanism has been studied in the research. The study of the correlation between spark-discharge-assisted LIBS (SD-LIBS) spectral intensity and Cu concentration shows that spark discharge has a significant increase in plasma size and spectral intensity; for Cu atomic lines at 324.75 and 327.39 nm, the determination coefficient ${R}^{{2}}$ between their internal standard correction intensity and Cu concentration is promoted to 0.996 and 0.997, respectively; spark-discharge assistant method improves the LOD of LIBS from 0.775 to 0.112 $\mu \text{g}$ /g. Due to the great improvement in detection performance, the application prospect of LIBS as fine particles detection method in transformer oil has become practical. [ABSTRACT FROM AUTHOR]

Although the circulating flow of insulating oil can enhance heat dissipation in converter transformers, it can also influence the space charge distribution in oil–paper composite insulation. In this article, the oil–paper composite insulation between flat electrodes was used as the experimental model. Based on the pulsed electroacoustic (PEA) method, we investigated the effect of uncharged flowing oil on the space charge distribution in composite insulation under a dc electric field and polarity reversal. Under a dc electric field, as the oil flow velocity increased, the amount of interface charge between the oil and paper first increased and then decreased. During the polarity reversal process, the field distortion was aggravated at slower oil flows. These experimental results were explained in terms of the effect of oil flow on ionized charges in the oil. [ABSTRACT FROM AUTHOR]

Thermal aging and ultraviolet (UV) irradiation are used as methods to prepare samples of kraft paper spanning its full lifecycle within a high-voltage transformer, from new to end of life. The mechanical break strain is correlated with the degree of polymerization (DP), representing a progressive deterioration of mechanical properties during aging. Subsequent exposure to oil flow mimicking both normal plant operation and reclamation activities shows that aged paper exposed to high rates of flow experiences noticeable surface roughening. Despite this, no measurable erosion occurs even after long-term temperature/flow cycling in used mineral oil containing a notable particulate content. This indicates that kraft paper is unlikely to be eroded by normal reclamation activities, even in a severely aged asset. This confirms the validity of using this approach to extend plant life. [ABSTRACT FROM AUTHOR]

This research investigates a tropical indigenous nonedible ester such as Pongamia oil (PO). PO and PO methyl ester (POME) were taken as test samples to develop a nonparametric model for investigations. This work implements the empirical cumulative distribution function (eCDF) to develop a PO and POME lifetime model. The lifetime model of the PO and POME acts as a decisive factor in determining the early failure regions corresponding to the bathtub model. On the other hand, the Bayesian theorem calculates the Bayesian inference. This enables one to foresee the variable as a posterior distribution to conclude their correlation in the parameter space. The developed lifetime model of PO and POME supports experimental results over hypothesis and assists one to predict the risk probability. The model predicted early failures of the PO at less than 40 kV and POME at less than 54 kV and life of POME from 41 to 54 kV and POME at 55–65 kV. The useful life of PO and POME will experience a failure rate of 45% and 25%, respectively. Furthermore, such detections aid power engineers to treat insulating fluids to overcome early aging effects and deterioration of oil–paper insulation. [ABSTRACT FROM AUTHOR]

Transformer oil and insulation paper are subject to the combined effects of the electric field and temperature operations, which significantly impact insulation deterioration. This article designs a comparative test of the electro-thermal combined aging to obtain several characteristic parameters for the aging degree of oil-pressboard insulation. Molecular dynamics are used to establish a mixed molecular model of oil-pressboard insulation to simulate the pyrolysis reactions at high temperatures using the ReaxFF force field. The microscopic effects of the electric field on the aging of oil-pressboard insulation are studied. The rationale of the simulation model is verified from experimental results, and the trend of the reactants and products in the reaction process was considered to determine the influence mechanism of electric–thermal coupling on oil-pressboard insulation. The experimental results show that thermal stress remains the most important factor toward cracking in insulation paper. The acid value and furfural formation rate in oil increase, and the polymerization degree of the insulation paper decreases rapidly after the combined electric and thermal aging. In addition, the molecular dynamics simulations show that pyrolysis increases the bond energy, promotes repulsion between atoms, and leads to fractured bonds. The addition of the electric field increases the susceptibility of the chemical bonds in cellulose molecules to breakage. The morphology of the cellulose chain then changes, and the diffusion of small molecules intensifies. This study reveals the mechanism of insulation aging under electro-thermal coupling through combined macroscopic testing and molecular dynamics simulations. [ABSTRACT FROM AUTHOR]

Although vegetable oil is the best substitute for mineral oil, it exhibits numerous disadvantages, including a high dielectric loss factor, low volume resistivity, and high viscosity. Nanoparticle modification may improve the electrical properties of vegetable oil considerably. In this study, vegetable oil infused with Fe3O4, TiO2, and Al2O3 nanoparticles was investigated. Thermal aging experiments were performed at 100 °C, 130 °C, and 150 °C. The aged vegetable insulating oil was periodically sampled and tested. The results revealed that the nanoparticles with a mass fraction of 0.001% exhibited the best modification effect. The electrical properties of the vegetable insulating oil modified with magnetic nanoparticles were pronounced, whereas the modification effect of nonmagnetic nanoparticles was mild. The general trend of the modification effect of the nanoparticles on vegetable oil became obvious with the increase in the temperature. At the same content, the modification effect of nano-Fe3O4 was superior to that of nano-TiO2 and nano-Al2O3. The results of this study provide experimental and theoretical support for the mechanism of long-term stability of vegetable insulating oil with nanoparticles and can be used to improve the long-term stability of vegetable insulating oil. [ABSTRACT FROM AUTHOR]

Dielectric loss factor is an important index to evaluate the quality of transformer oil, which is very sensitive to the response of polar impurities. As a nondestructive testing technology, ultrasonic can identify polar impurities through attenuation characteristics and then determine dielectric loss. In this article, a novel method for detecting dielectric loss factor of transformer oil based on multifrequency ultrasound and particle swarm optimization-Elman neural network (PSO-ENN) forecasting model was proposed. The analysis of the forecast results and precision shows that the dielectric loss factor can be effectively forecast via this method. The experiment verifies that the ultrasonic effect is nondestructive to transformer oil, which lays a foundation for the development of online monitoring system. [ABSTRACT FROM AUTHOR]

The latest 2011 edition of the IEEE C57.100 standard introduced a new sealed tube accelerated aging test (STAAT) procedure and methodology for evaluating the thermal index (TI) of liquid-immersed transformer insulation systems. The application of this standard becomes increasingly important, as new insulating materials (both solid and liquid) have been developed, and their thermal performance shall be qualified. The experimental work presented in this article investigated the effects of different test parameters on the aging test results and data interpretation. The new findings on the dependence of the aging process on the ratio and type of materials in the system, as well as on the selected aging criteria provide a better understanding of the accelerated aging and interpretation of the data along with practical recommendations for the improvement of the test procedure. In parallel, for the first time, the TI of the Transformerboard, as a component of the so-called industry-proven insulation system, was investigated and defined based on the standard industry test. The results confirmed that the Transformerboard’s thermal class is, at least, 120 °C; this validates an empirically based application of this material as a part of the 120 °C class insulation system in both power and distribution transformers. [ABSTRACT FROM AUTHOR]

This study is devoted to the improvement of the transformer oil quality, using a new inhibiting additive, since the extensively used inhibitory additive ionol is sensitive to organosulfur compounds of sulfur oils. From the earlier works, the organosulfur compounds are known to affect ambiguously the transformer oils characteristics. Therefore, it was of interest to study the individual sulfides as the inhibitory additives. The goal was achieved by the researches with the use of the model mixtures consisting of the selectively purified transformer oil (free from ionol) along with the individual sulfides, decylcyclohexylsulfide and decylfenylsulfide, at concentration of 0.5 %, Their effect on stability against oxidation of the selectively purified oil, on its electric strength and tangent of the dielectric losses’ angle was studied. The primary results obtained were, firstly, the rate slowing down of water formation in oil and of water-soluble acids formation in oil upon its oxidation in the presence of the individual sulfides, and, secondly, an increase in the electric strength and decrease in the dielectric losses compared to the transformer oil, containing ionol. It was established that decylcyclohexylsulfide inhibitory properties are stronger compared to those of decylfenylsulfide, which was testified by less quantities (by 1.8 times) of the water formed and of water-soluble acids (by 2.22 times). The introduction to oil of 0.5 % decylcyclohexylsulfide and decylfenylsulfide increased the oil electric strength, correspondingly, by 2.6 and by 5.5 times, upon water concentration in oil equal to 15·10-2 g/kg. The significance of the results obtained is in improving the transformer oil quality produced from the sulfur oils with the use of a novel additive.

This article aims at developing a multigene symbolic regression model of interfacial tension (IFT) for determining the health index of insulating oil. The oil parameters considered for modeling of IFT are break down voltage, resistivity, acidity, tan delta and water. The unique study undertaken in this article attempts to develop a multigene symbolic regression model for generating and evaluating the relationship between IFT and other oil parameters. Data utilized in this study involves actual oil testing parameters of in-service transformers and reactors of age more than 15 years. A sensitivity analysis has been carried out to investigate the relative contribution of input variables to the model output. The result of sensitivity analysis has further been used to determine the weight of parameters for calculating the health index of insulating oil. Finally, the health index of 400 in-service transformer and reactors are evaluated and analyzed. This research will help in undertaking decisions for the replacement of oil based on health index, thereby increasing the life span of transformer and reactors. The health index so obtained will act as a reference for planning necessary maintenance activities of the equipment and thereby saving unprecedented failure of the oil-filled transformer and reactors. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) online monitoring is an essential part of transformer insulation condition based maintenance and prognostics and health management in industrial power system. In order to realize online localization of multiple PD sources conveniently, an approach that uses single-channel mixed acoustic emission (AE) signal is proposed in this article. First, the combined method based on the singular spectrum analysis and the independent component analysis is used to calculate the number of PD sources and realize the separation of mixed AE signals. Next, a difference selection algorithm is proposed to solve the problem that the inherent disorder of signal blind source separation makes the time difference of arrival technology difficult to localize the multiple PD sources directly. Then, the simulation signals and measured signals have verified that the signal separation effect of the proposed single-channel-based method is no less than that of the multichannel-based method. Finally, the proposed approach is applied to a transformer. The disordered separated signals were successfully grouped to realize multisource PD localization. [ABSTRACT FROM AUTHOR]

The insulation evaluation in converter transformer is a nonlinear thermoelectric coupling problem whose numerical solution is time consuming. To decrease the computation cost, a discrete empirical interpolation method (DEIM) is usually utilized to approximate the nonlinear term with a reduced order model (ROM), but it suffers sometimes from the unstable convergence problem. In this article, a local DEIM based on QR factorization (LDEIM-Q) is proposed. It has a better error bound and can avoid the unexpected instabilities. The LDEIM-Q is combined with the proper orthogonal decomposition (POD) to deal with the nonlinear thermoelectric coupling. A converter transformer model is taken as an example to validate our method in terms of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

This article presents an investigation of the optimal positioning of tank-wall magnetic shunts in power transformers. Magnetic shunts are made of grain-oriented magnetic silicon steel laminations and are used to avoid hot spots in metallic regions inside the power transformer. The magnetodynamic equations are solved in terms of the electric vector potential and the magnetic scalar potential. Thermal effects are calculated using heat transfer coefficients. This article proposes and solves an optimization problem where the objective is to position several shunts near the tank wall. Several design variables, temperature, and geometrical constraints are considered. As the finite-element (FE) model to be solved is in 3-D, the computing resources must be efficiently used. A multi-threading method was developed and numerically tested where the 3-D FE model, temperature calculation, and the particle swarm optimizer (PSO) are executed in a multicore computer in a parallel scheme. [ABSTRACT FROM AUTHOR]

Transformer oil is a liquid insulating material used in ultra-high voltage power equipment such as oil-immersed transformers and reactors. Online monitoring of its characteristics helps to maintain the status of power equipment. Aiming at the problem of single detection parameter in traditional methods, a new detection method for density, viscosity and dielectric constant based on quartz tuning fork is proposed in this paper. Firstly, by analyzing the working mechanism of the quartz tuning fork, the sensor detection model and parameter extraction method in air are constructed based on the equivalent circuit model of the Butterworth-Van Dyke model. Then, the working mechanism of the density, viscosity and dielectric constant of the transformer oil is analyzed, and the method of parameter extraction of sensor in standard oil sample and measurement model of sensor in oil sample to be measured are obtained. Finally, an experimental platform is built to verify the above conclusions. The results show that the quartz tuning fork detection model has good repeatability, the measurement errors of density, viscosity and dielectric constant are 2.52%, 6.53% and 3.36%, respectively, which meet the online monitoring accuracy requirements. The detection technology of transformer viscosity, density and dielectric constant based on quartz tuning fork realizes the simultaneous detection of three parameters in the same method, which meets the requirements of miniaturization, diversification and real-time detection of power parameters under the condition maintenance system, and also provides a new application scenario for tuning fork quartz crystal oscillators. [ABSTRACT FROM AUTHOR]

High-frequency (HF) oil-immersed transformers with small size and light weight have been paid wide attention by researchers. Its oil–paper insulation frequently withstands HF over-voltages at frequencies of several kilohertz and above, resulting in the too early failure of oil–paper insulation. In order to investigate the effect of HF on oil–paper insulation, this article investigates the partial discharge (PD) characteristics of oil–paper insulation under HF stress. The experimental results show that a frequency-induced inflection point is existed near 10 kHz and the PD parameters of HF voltages is much higher than that ac voltages. Besides, the theoretical mechanism is expounded to interpret and understand the formation process of frequency-induced inflection points more easily. It can be found that the change in frequency causes the change in the charge decay time constant and the residual charge on the insulating surface, resulting in the formation of the frequency-induced inflection point at HF. These results might contribute to determine the characteristics of PDs between HF voltages and ac voltages. It can provide references for the research and production of HF transformers. [ABSTRACT FROM AUTHOR]

Condition monitoring of power transformers, which are key components of electrical power systems, is essential to identify incipient faults and avoid catastrophic failures. In this paper machine learning algorithms, i.e., nonlinear autoregressive neural networks and support vector machines, are proposed to model the transformer thermal behavior for the purpose of monitoring. The thermal models are developed based on the historical measurements from nine transformers comprised of two 180-MVA units, four 240-MVA units and three 1000-MVA units. The data consist of load profile, tap position, winding indicator temperature (WTI) measurement, ambient temperature, wind speed and solar radiation. The results are validated against field measurements, and it is clearly demonstrated that the alternative algorithms surpass the IEEE Annex G thermal model. An incipient thermal fault identification algorithm is then proposed and successfully used to identify an issue using measurements taken in the field. This algorithm could be used to alert the operator and plan intervention accordingly. [ABSTRACT FROM AUTHOR]

Methanol concentration as an aging indicator can assess cellulose aging and estimate cellulose degree of polymerization (DP). Despite the advantages of methanol (MeOH), its major disadvantage is that its concentration in the oil decreases after some time. This point is called the MeOH peak in this article. Therefore, using this indicator when MeOH concentration is reduced leads to incorrect results. When using methanol, the main problem is that whenever the methanol concentration in a transformer is measured, it cannot be identified that the measured concentration belongs to the left or right side of the MeOH peak. If the measured concentration belongs to the right side of the MeOH peak, the estimated DP is incorrect. In this article, an empirical equation is established to estimate the DP value as a function of methanol concentration when methanol concentration belongs to the left side of the MeOH peak. Then, a new index called methanol peak detector index (MPDI) composed of a 2-furfural/MeOH ratio and normalized DP is presented to ensure the validity of the calculated DP value. If it is found that the measured concentration belongs to the left side of the MeOH peak, then the estimated DP by the presented equation is valid. Finally, the application of this method is verified by applying it to measurement data samples indicating the validity of the method. [ABSTRACT FROM AUTHOR]

The carbon oxides (CO2 and CO) in the insulating oil of power transformers come from the thermal aging of cellulosic insulating paper; the CO2/CO ratio can reflect the aging state of cellulosic insulating paper. At present, the formation mechanisms of CO2 and CO in oil-immersed power transformers are still unclear. In this article, the ReaxFF reaction force field is used to simulate the thermal aging process of cellulosic insulating paper, and the main reaction pathways of CO2 and CO molecules formed by cellulose pyrolysis are obtained. The study finds that the amount of CO2 and CO generated is related to temperature. When the pyrolysis temperature continues to rise, the amount of CO2 generated shows a downward trend, while the amount of CO generated continues to increase, and the CO2/CO ratio continues to decrease. The simulation results are consistent with the previous experimental results. Through pathway tracking, it is found that the CO2 and CO molecules are mainly derived from certain specific atoms of cellulose molecules. The COSMO-RS module is used to analyze the change trend of the solubility of CO2 and CO in insulating oil with temperature. The results show that the solubility of CO2 and CO decreases with the increase of insulating oil temperature, which provides theoretical support for accurate application of carbon oxides aging evaluation index at different temperatures. [ABSTRACT FROM AUTHOR]

In order to improve the low-temperature operational reliability of natural ester insulating oil-immersed transformers, the low-temperature property improvement on natural ester is investigated. The results show that poly alpha-olefin, hexyl naphthalene, and polymethacrylate have good effects of pour point depression, but poly alpha-olefin and hexyl naphthalene cause properties deterioration of natural ester such as acid value and dielectric dissipation factor because of their own characteristics. When the adding proportion of polymethacrylate is 1%, the pour point of natural ester is reduced to −20.5 °C, which can maintain good physicochemical and electrical insulating properties. Polyacrylate has no depression effect and reduces the insulating property. During the crystallizing fractionation (CF) process, when the stirring speed of low-temperature crystallization (II) is 3 r/min, and the temperature difference between cooling medium and natural ester is 3 °C, the pour point is reduced to −20.3 °C. However, due to the reduction of high melting point glycerides content, the oxidation resistance of natural ester decreases. The effective combination of CF process and polymethacrylate as a pour point depressant makes the pour point reduce to −27.3 °C, which also ensures good physicochemical and electrical properties. Also, the synergistic antioxidation between phenolic antioxidants and polymethacrylate enhances the antioxygenic property, which effectively expands the application conditions and ranges of natural ester. [ABSTRACT FROM AUTHOR]

Due to their large power draws and increasing adoption rates, electric vehicles (EVs) will become a significant challenge for electric distribution grids. However, with proper charging control strategies, the challenge can be mitigated without the need for expensive grid reinforcements. This article presents and analyzes new distributed charging control methods to coordinate EV charging under nonlinear transformer temperature ratings. Specifically, we assess the tradeoffs between required data communications, computational efficiency, and optimality guarantees for different control strategies based on a convex relaxation of the underlying nonlinear transformer temperature dynamics. Classical distributed control methods, such as those based on dual decomposition and alternating direction method of multipliers (ADMM), are compared against the new augmented Lagrangian-based alternating direction inexact Newton (ALADIN) method and a novel low-information, look-ahead version of packetized energy management (PEM). These algorithms are implemented and analyzed for two case studies on residential and commercial EV fleets with fixed and variable populations. The latter motivates a novel EV hub charging model that captures arrivals and departures. Simulation results validate the new methods and provide insights into key tradeoffs. [ABSTRACT FROM AUTHOR]

An accelerated loss of life (LOL) of distribution transformers has been observed in recent years owing to the increasing penetration of electric vehicles (EVs). This paper proposes an evolutionary curriculum learning (ECL)-based multi-agent deep reinforcement learning (MADRL) approach for optimizing transformer LOL while considering various charging demands of different EV owners. Specifically, the problem of charging multiple EVs is cast as a Markov game. It is solved by the proposed MADRL algorithm by modeling each EV controller as an agent with a specific objective. During the centralized training stage, a novel centralized ECL mechanism is adopted to enhance the coordination of multiple EVs. It enables the proposed approach to address the management of large-scale EV charging. When the training procedure is completed, the proposed approach is deployed in a decentralized manner. Herein, all the agents make decisions based solely on local information. The decentralized manner of execution helps preserve the privacy of EV owners, reduce the related communication cost, and avoid single-point failure. Comparative tests utilizing real-world data demonstrate that the proposed approach can achieve coordinated charging of a large number of EVs while satisfying the various charging demands of different EV owners. [ABSTRACT FROM AUTHOR]

The power transformer is one of the most important and critical assets involved in the grid and, at the same time, one of the most expensive. Several transformer condition parameters allow to assess the degradation of assets. These help in the decision-making process on the operation, repair, refurbishment, or replacement of transformers. Dissolved gas analysis (DGA) is one of the most commonly used methods to manage maintenance and establish the health index of power transformers, as well as to identify the type of fault. This paper examines and explores the studies related to graphical methods for the identification of faults in power transformers that had been developed over the last almost 50 years. The main types of faults and the sub-types presented in the analysed studies are compiled in this paper. The main differences between the methods in terms of their graphical representation, number of gases used, type of data used from the DGA results, and number of faults and sub-faults identifiable in each of them, are also presented. The application of the reviewed methods was carried out using two real DGA results. [Display omitted] • This paper reviews graphical methods for identifying transformer insulation faults. • Twenty-seven graphical methods were developed over the last 50 years. • Nine different shapes of graphical methods were found. • There are five different ways of using DGA results to identify faults. [ABSTRACT FROM AUTHOR]