Oil-filled Transformers Research Articles

Theoretical Calculation of Dissolved Gas in Transformer Oil Using the Gas Sensitive Properties of Sc- and Ti-Modified ZrS2.

To guarantee the secure functioning of the complete power system and minimize the risks associated with oil-filled transformers during their operation, it is greatly important to carry out gas sensing studies of dissolved gases in transformer research. Through the utilization of first-principles density functional theory calculations, the adsorption energy, electronic characteristics, and recuperation duration of ZrS2 modified with Sc and Ti were examined. The results show that compared to those of the initial ZrS2 material, the doping of TM atoms Sc and Ti significantly improved the adsorption properties of the material, and the adsorption of CO and C2H4 showed chemisorption. The adsorption capacity for gases decrease in the following order: C2H4 > CO > H2. The calculated recovery times indicate that Sc-ZrS2 and Ti-ZrS2 were ideal carbon monoxide sensing materials under the specific conditions. The results of this work can establish a fundamental rationale for the use of ZrS2 in sensing the conditions of oil-immersed transformers.

Enhanced Method for Localization of Partial Discharges in Oil-Filled Transformers Using Acoustic Emission Signals

The accurate measurement of partial discharges (PD) in power transformers is a critical component in identifying faults and planning effective maintenance strategies. Acoustic emission (AE) sensing technology is suitable for detecting partial discharges in transformers because it is less affected by external electromagnetic interference and detects PD non-invasively. This paper delves into examining the relationship between the detection intensity, the specific type of PD source, and the distance to the discharge source, using AE sensors. It was observed that AE wave intensities from corona discharges tend to be relatively strong, indicating a higher detection probability. Creepage discharges usually exhibit the next level of intensity, followed by the PD occurring in bubbles. It is considered that the intensity of the AE waves varies significantly depending on both the speed at which the discharge propagates and the medium and volume of the discharge space. Furthermore, this study has conducted experimental comparisons among three distinct methods for calculating the Time Difference of Arrival (TDOA) in localization calculations. These methods are the energy criterion, Generalized Cross-Correlation (GCC), and GCC with Phase Transformation (PHAT). The experimental results suggest that the energy criterion method is particularly effective when sensors are distributed placement around the entire tank. In contrast, the GCC-PHAT method shows greater suitability in scenarios where sensors are centralized placement in certain sections of the tank. It was clearly observed that the GCC-PHAT method, which suppresses the impact of noise and reflections, consistently achieves higher estimation accuracy in comparison to the standard GCC method. Notably, this method has shown the ability to maintain its accuracy levels even in cases of low discharge intensities. The implications of these findings are significant, as they could improve the precision and effectiveness of maintenance diagnostics in power transformers.

An Optical Device Based on a Chemical Chip and Surface Plasmon Platform for 2-Furaldehyde Detection in Insulating Oil.

2-Furaldehyde (2-FAL) is one of the main by-products of the degradation of hemicellulose, which is the solid material of the oil-paper insulating system of oil-filled transformers. For this reason, it has been suggested as a marker of the degradation of the insulating system; sensing devices for 2-FAL analysis in a wide concentration range are of high interest in these systems. An optical sensor system is proposed; this consists of a chemical chip, able to capture 2-FAL from the insulating oil, coupled with a surface plasmon resonance (SPR) probe, both realized on multimode plastic optical fibers (POFs). The SPR platform exploits gold nanofilm or, alternatively, a double layer of gold and silicon oxide to modulate the sensor sensitivity. The capturing chip is always based on the same molecularly imprinted polymer (MIP) as a receptor specific for 2-FAL. The system with the SPR probe based on a gold nanolayer had a higher sensitivity and a lower detection limit of fractions of μg L-1. Instead, the SPR probe, based on a double layer (gold and silicon oxide), has a lower sensitivity with a worse detection limit, and it is suitable for the detection of 2-FAL at concentrations of 0.01-1 mg L-1.

Predictive Model for Incipient Faults in Oil-Filled Transformers

Predictive Model for Incipient Faults in Oil-Filled Transformers

System for assessment and prediction of the technical condition of power oil-filled transformer equipment of distribution networks using machine learning

RELEVANCE the research is to develop a new system for assessing the technical condition of power oil-filled transformer equipment of distribution networks.OBJECT. To increase the accuracy of assessing the technical condition of power oil-filled transformer equipment (POTE) of distribution networks through the use of machine learning methods. Currently, an increase in the volume of analyzed information about the state of the management system of distribution networks leads to significant changes in the choice of data processing methods. The use of machine learning methods is associated both with the need to apply operational experience (in the form of expert assessments) and to obtain objective assessments of the condition of transformer equipment of distribution networks from instrumentation and sensors.METHODS. This work uses research methods such as mathematical modeling and the method of paired comparisons. As an example, we consider the oil-filled power transformer TMN-6300, its diagnostic parameters, external and operating parameters. The technical condition of the TMN-6300 transformer is assessed and a predictive model is created based on the existing monitoring system and machine learning methods, which make it possible to formalize expert knowledge and automate the process of data processing and analysis.RESULTS. A database has been created to assess and predict the technical condition of POTE of distribution network management systems. The algorithm for predicting the technical condition of POTE of the technical equipment in the form of an artificial neural network model was tested in the developed assessment system.CONCLUSION. The results of assessing and predicting the technical condition of POTE of the metering system of distribution networks obtained in this work prove the unconditional relationship between the parameters of the metering system and external, operating parameters. The data obtained as a result of modeling helps to increase the accuracy of forecasting the technical condition and determine the longterm prospects for the functioning of POTE the equipment management system, timely maintenance and repairs over the course of years and months.

Investigating the effects of corrosion parameters on the surface resistivity of transformer’s insulating paper using a two-level factorial design

The integrity of the insulation in oil-filled power transformers, shunt reactors, and high voltage bushings can be affected when copper dissolves in the insulating oil and then deposits onto the paper insulation. The presence of dissolved copper in the oil increases dielectric losses, while copper deposition significantly improves the conductivity of the paper insulation. Various factors, including temperature, oxygen, sulfur groups, passivators, and ageing time, have been found to contribute to the acceleration of corrosion activity in transformer insulating oils. Unfortunately, there is a lack of extensive research focused on systematically analysing and measuring the impact of corrosion-related factors on the dissolution of copper in transformer insulating oils and the deposition of copper onto solid insulation surfaces (Kraft paper). Therefore, this study aims to thoroughly examine the effects of corrosion factors on copper and sulfur deposition on Kraft paper insulation when it is submerged in transformer mineral oil (TMO). Using a two-level (2k) factorial design, we investigated three crucial factors: i) oil temperature, ii) elemental sulfur concentration, and iii) ageing time. It is worth mentioning that the results obtained from the two-level factorial design indicate that the surface resistivity is primarily affected by the temperature of the oil. This factor alone explains a significant 38.68% of the observed variation. In order to improve predictability, a regression model was created to estimate the surface resistivity of TMO-impregnated paper insulation. This model takes into account factors such as oil temperature, elemental sulfur concentration, and ageing time.

Health Assessment of Solid Insulation of Mineral Oil-Filled Converter Transformer

Health Assessment of Solid Insulation of Mineral Oil-Filled Converter Transformer

Impact of one and two partial cooling ducts on temperature rise in transformer windings

We apply our quasi-1D model to the temperature rise in oil-filled transformer foil windings with two symmetric partial ducts. We compare the results with a two-dimensional FEM simulation. To compare the results to industrial tests we try to find a relationship between the effective values for heat transfer coefficients of different foil windings. Finally, we compare the effectiveness of adding one vs two partial cooling ducts to the winding.

Quasi-one-dimensional approach to heat transfer: General formulation and its application to transformer windings

A quasi-one-dimensional approach to conduction heat transfer with convective boundary conditions is developed for bodies of variable geometry. The novelty of the method is demonstrated on the windings in oil-filled distribution transformers with partial cooling ducts. We obtain analytical solution for the windings average temperature rise and tangential temperature distribution and find them in an excellent agreement with the FEM simulation, numerically confirming the validity of the reduction from 2D to 1D. When the quasi-one-dimensional approach is applied to calculation of temperature rise for the low and high voltage windings in industrial setting we obtain 1% and 5% discrepancy with the industrial tests respectively. Hence, the method offers reliable estimates of the temperature rise, and temperature distribution in a chosen direction, in bodies of variable geometry.

A Combined Computational Fluid Dynamics and Thermal–Hydraulic Modeling Approach for Improving the Thermal Performance of Corrugated Tank Transformers: A Comparative Study

In this study, we present a complete analysis of the thermal behavior of an oil-filled distribution transformer using two different approaches: numerically, by applying computational fluid dynamics (CFD) simulations; and analytically, by using thermal–hydraulic modelling (THM). The THM method becomes challenging when the corrugated transformer tank wall structures are complex, and therefore CFD simulations are required to provide in-depth details of the winding oil thermal–hydraulic behavior and to generate parameters for improving the THM calculation. The numerical and analytical thermal performance results were then compared with heat-run measurements of a case study transformer and the accuracy of both approaches on different thermal performance parameters was validated. This study has certain reference value for improving the thermal performance and operation efficiency of distribution transformers, and eventually aims to provide engineers with an effective tool to design the most efficient and reliable distribution transformers with corrugated tank walls for various applications.

Transformer hot spot temperature estimation through adaptive neuro fuzzy inference system approach

Transformer performance and efficiency can be enhanced by effectively address the properties of its insulation system. The power transformer insulation system weakens as a result of operational thermal stresses brought on by dynamic loading and shifting environmental patterns. Winding hot spot temperature is a crucial metric that must be maintained below the prescribed limit while power transformers are operating so as to maintained power system reliability. This is due to the fact that, among other variables, the time-dependent aging effect of insulation depends on transitions in hot spot temperatures. Due to the non‐linear nature of the conventional mathematical models used to determine these temperatures, and complexity of thermal phenomena, investigations still need to be exercised to fully understand the variables that associate with hot spot temperature computation with minimum error. This paper explores the possibilities of enhancing top oil and hot spot temperature estimation accuracy through the use of an adaptive neuro-fuzzy inference (ANFIS) technique. The paper presents an adaptive neuro fuzzy model to approximate the hot spot temperature of a mineral oil-filled power transformer based on loading, and established top oil temperature. Initially, a sub-ANFIS top oil temperature estimation model based on loading and ambient temperature as inputs is established. Using a hybrid optimization technique, the ANFIS membership functions were fine-tuned throughout the training process to reduce the difference between the actual and anticipated outcomes. The correctness and reliability of the created adaptive neural fuzzy model have been verified using real-world field data from a 60/90MVA, 132 kV power transformers under dynamic operating regimes. The ANFIS model results were validated against field measured values and literature-based electrical-thermal analogous models, establishing a precise input-output correlation. The developed ANFIS model achieves the highest coefficient of determination for both TOT and HST (0.98 and 0.96) and the lowest mean square error (7.8 and 10.3) among the compared thermal models. Correct determination of HST can help asset managers in thermal analysis trending of the in-service transformers, helping them to make proper loading recommendations for safeguarding the asset.

Comparison of Empty and Oil-Filled Transformer Tank Mode Shapes Using Experimental and FEM Modal Analysis

In this paper, the mode shapes of an empty and oil-filled transformer experimental model tank are obtained using 3D finite element method (FEM) modal analysis. For verification of the FEM analysis results, experimental modal analysis (EMA) is carried out in both cases using appropriate impact hammers and accelerometers. Simulated and measured results are visualized and compared for mode shapes in a frequency range of interest for both empty and oil-filled tanks. In order to avoid overly stiff FEM models of transformer tanks, the welded joint modeling technique is presented and analyzed in detail. For an oil-filled tank, the most accurate results are calculated in the model where the welded joint is modeled as half the tank wall’s thickness. In that case, the mean absolute error for the given ten-mode shapes is 1.7 Hz. Also, mesh sensitivity analysis is performed. It is concluded that a 10 mm maximum element size is an optimal solid (3D) mesh. However, shell mesh can be used to reduce computing requirements.

Development of a simulation model for assessing the technical condition of the magnetic circuit of oil power transformers by measuring the temperature of the tank and the external environment

In the article, the authors analyzed how oil-filled power transformers ensure reliable and stable operation of power transmission systems by providing efficient transmission of electricity of different voltages. In the article, the authors found that the main elements of oil-filled power transformers - the magnetic circuit, the coil and the technical condition of the oil - directly determine the performance of oil-filled power transformers. In this work, a simulation model was developed to evaluate the magnetic circuit of power oil transformers based on the measured temperature of the tank and the external environment during express diagnosis. A simulation model has been developed that allows to calculate the technical condition of oil power transformers operating in city power networks. It is shown that the developed model allows to calculate the value of the technical condition of the oil power transformer with the help of diagnostic parameters. In order to represent the technical state of the magnetic circuit of the oil power transformer, the functions of changing the state value of the output variable in the Mamdani fuzzy logic method are shown and the block diagrams are drawn up. Author’s using the Mamdani fuzzy logic method, express diagnosis of the technical condition of the “Magnetic core of the oil power transformer” was carried out in the Matlab simulink program. During the evaluation, the value of the output parameter is taken from 0 to 100, and each step is divided by 10, the smallest value is 0, and the largest is 100.

APPLICATION OF THE DECISION TREE ALGORITHM TO EVALUATE THE RESULTS OF CHROMATOGRAPHIC ANALYSIS OF TRANSFORMER OIL

Power oil-filled transformer is the main link in the process of conversion and transmission of electrical energy in electrical networks at almost all voltage classes. At the moment in our country more than 50% of them are operated with significant excess of service life, which increases the requirements to their proper technical control and full-fledged diagnostics of all its main components.
 
 The aim of the research is to consider the ways of simplification and automation of the process of technical diagnostics of power oil-filled equipment by means of application of artificial intelligence methods, namely the decision tree algorithm for evaluation of the results of chromatographic analysis of transformer oil.
 
 Materials and Methods. As input data, the results of chromatographic analysis of dis-solved gases in transformer oil conducted from December 11, 2009 to December 12, 2020 for two power transformers of voltage class 110 kV installed in the power system of one of the regions of our country were considered and analyzed. As a software application used for diagnosing the technical condition of a power transformer using artificial intelligence methods, we selected the free application Deductor Academic 5.3 Build 0.46.
 
 Research results. The article considers the application of a promising method (decision tree algorithm) of interpretation of data obtained as a result of chromatographic analysis of dissolved gases in transformer oil. The analysis data were processed by means of artificial intelligence methods, the result of which was the reliability and accuracy of determining the technical condition of the power oil-filled transformer.
 
 Conclusions. According to the results of the study, a decision tree algorithm is proposed for the implementation of artificial intelligence in solving the problem of power transformer diagnostics using chromatographic analysis results.

IMPROVEMENT OF THE METHOD OF DIAGNOSTICS OF HIGH-VOLTAGE TRANSFORMERS VOLTAGE BASED ON THE RESULTS OF CHROMATOGRAPHIC ANALYSES OF TRANSFORMER OILS

The relevance of the work is due to the need to improve methods for diagnosing high-voltage oil-filled voltage transformers with paper-oil electrical insulation, which makes it possible to solve the problems of monitoring the technical condition, finding the place and determining the causes of failure (malfunction), and predicting the technical condition of this equipment on the basis of appropriate diagnostic software. The aim of the work is to ensure the reliability of operation of high-voltage oil-filled voltage transformers with paper-oil electrical insulation by improving the method of their diagnosis based on the results of analyses of samples of mineral transformer oils by gas chromatography. In accordance with the research objective, the following tasks were solved: 1. Determine the content of such dissolved diagnostic components in samples of mineral transformer oils from high-voltage oil-filled voltage transformers of NKF-110, NKF-220, NKF-330 types as gases H2, CH4, C2H6, C2H4, C2H2, CO, CO2, O2, N2; additive "Ionol"; furan compounds (2-furyl alcohol; 2-furfural; 2-acetylfuran; 5-methyl-2-furfural) and the sum of their concentrations. 2. The basic procedures for diagnosing high-voltage oil-filled voltage transformers based on the results of analyses of mineral transformer oil samples with the determination of the content of dissolved diagnostic components in them were improved. For the diagnosis of high-voltage oil-filled voltage transformers based on the results of gas chromatographic analyses of samples of mineral transformer oils from this equipment, it is recommended to use the following methods: "Limit level of concentrations", "Tabular method", "Graphic images of defects", "ETRA", "Duval triangle", "Visual inspection after disassembly of high-voltage oil-filled voltage transformers". To determine the degree of danger of the development of the identified probable defects, the method of determining the "Relative growth rate of dissolved gas concentrations" can be used. The prospect of practical application of the results of the work is to ensure the reliability of operation of high-voltage oil-filled voltage transformers by improving the method of their diagnosis based on the results of analyses of samples of mineral transformer oils with simultaneous determination of the content of dissolved gases, the additive "Ionol", furan compounds by gas chromatography.

Methyl Ester-Mineral Oil Mixture under Thermal Aging

Replacement with natural esters (retrofilling) is an alternative solution to extend the life expectancy of an oil-filled transformer containing mineral oil. During the retro-filling process, some portions of the mineral oil remain inside the transformer's tank, forming a mixture between the used mineral oil and the new vegetable oil. In this investigation, methyl ester was mixed with a relatively low percentage of mineral oil to simulate the retro-filling process. The aim is to examine the characteristics of a mixture of mineral and vegetable oils under thermal aging. Thermal aging treatments were carried out at 120°C for 28 days and 140°C for 14 days. Kraft paper and copper wire were incorporated during the aging test. After thermal aging, the characteristics of the oil mixture were evaluated based on electrical, physical, and chemical characteristics. The result shows that physical and chemical characteristics are significantly influenced by the increase in mineral oil content and the temperature of thermal aging. However, the breakdown voltage increased after thermal aging, but at a higher temperature, the breakdown voltage dropped as the mineral oil content increased.

Подтверждение необходимости и целесообразности применения различных методик интерпретации результатов газохроматографического анализа силовых трансформаторов

The method of analysis of gases dissolved in oil is one of the most informative methods of early detection of defects in power oil-filled transformers. Now, the decision on the state of the transformers is based on the method of interpretation of the results of the gas chromatographic (GC) according to the guideline document RD 153-34.0-46.302-00. At the same time, there are situations when this document does not provide accurate analysis results. Thus, it is proposed to use several methods of interpreting the results of the GC to obtain refined conclusions. The purpose of the study is to substantiate the need to use various methods of interpretation of the results of gas chromatographic analysis of the oil of power transformers. The following methods for dissolved gas analysis have been reviewed: Rogers Ratio Method, IEC 60599 Standard Method, Doernenburg Ratio Method, Duval Triangle Method, ETRA method, as well as the guideline document RD 153-34.0-46.302-00 method adopted in the Russian Federation. These methods are implemented in various power companies of the Russian Federation, such as PJSC “Rosseti MR”, PJSC “FGC UES” and PJSC “Mosenergo”. The article reveals the need to consider the totality of all available methods and techniques based on RD 153-34.0-46.302-00 and development of training samples. The scientific novelty and significance of the conducted research lies in the confirmation of the need to use a set of methods for interpreting the results of the GC. An algorithm for the complex application of the methods described in the article for interpreting the results of the GC and training samples has been formed. The obtained results allow us to consider the possibility to develop software for the complex application of the methods of interpretation of the results of the GC described in the article and the formation of training samples based on the developed algorithm.

Remote monitoring and control of the status of transformers in distribution electrical networks

RELEVANCE. One of the most important expected effects of digitalization of objects of the power grid complex of the Russian Federation is to increase the level of reliability of its functioning. In this regard, research and development in this direction is undoubtedly relevant. THE PURPOSE. To propose a mathematical model for monitoring dangerous developing defects in power oil-filled transformers, this would meet the properties of predictability and adaptability. Based on the model, to develop a decision-making algorithm for long-term reliable operation oftransformers. METHODS: Methods of statistical pattern recognition theory, correlation analysis and Bayesian classification will be used to solve the problems to ensure high reliability of diagnostic assessments, validity and effectiveness of operational solutions. RESULTS. A predictive model was obtained and verified in the form of a correlation function of a sign of a faulty state of a transformer from the values of its electrical load. An event tree has been formed that restores the causal relationship between the result of monitoring the transformer, the defect sign and the operational decision being made. Based on the event tree and diagnostic evaluation criteria, a control algorithm is implemented, with the help of which calculations are performed confirming the effectiveness of the proposed approach. CONCLUSION. The possibility of effective application of the developed defect recognition model and operational control algorithm as tools of industrial technology of the Internet of Things is illustrated, in particular, when organizing remote diagnostic monitoring of oil-filled transformer equipment at substations of the distribution grid area.

Effect of Fullerene and Graphene Nanoparticles on the AC Dielectric Strength of Natural Ester

The current study addresses the impact of the addition of fullerene and graphene nanoparticles on the AC breakdown voltage (AC BdV) of natural ester (FR3). The nanofluids (NFs) were prepared following the two-step process, and the AC BdV was performed in compliance with IEC 60156 standards. Five different concentrations of NPs were considered (0.1 g/L, 0.2 g/L, 0.3 g/L, 0.4 g/L, and 0.5 g/L). A Student’s t-test was performed to compare the base liquid’s AC BdV data with different nanofluids. The experimental data were checked to see if they obeyed the Weibull distribution fitting curve, and the AC BdV at 1%, 10%, and 50% risk levels were then calculated. The performed t-test provides evidence that AC BdV data from the base liquid were different from those of different NFs (except 0.2 g/L fullerene, and 0.1 g/L and 0.4 g/L graphene NFs). It is also shown that the Weibull distribution fit the BdV data of all liquids (except 0.5 g/L fullerene NF), and remarkable improvements of AC BdVs at 1%, 10%, and 50% were observed. The best improvement was obtained with 0.4 g/L fullerene and 0.3 g/L graphene NFs. These results show the importance of using nanofluids as substitutes for the existing insulating liquids for current oil-filled power transformers.

Development of insulation moisture level criteria as a result of technological progress in the maintenance system of oil-filled transformers

Transformers are critical elements of electrical networks and systems. The operational reliability of oil-filled transformers largely depends on the characteristics of the insulation, which is confirmed by the failure statistics. A number of factors affecting the insulation of electrical machines are identified, including excessive moisture, with its negative consequences outlined. The complexity and shortcomings of the use of traditional technologies for drying solid insulation are indicated, which focus on the search and implementation of the latest technologies in the system of maintenance and repair of transformers — automated insulation restoration systems (AIRS).Taking into account the development of technical progress in the field of service maintenance of transformers, the negative impact of moisture on the reliability, durability and overload capacity of electrical machines, especially those beyond their normal service life, a new look at the criteria for the moisture content of cellulose insulation is proposed. A review is made of the normative requirements for the moisture content of the insulation of oil-filled transformers of a number of Russian and foreign standards. On the basis of the Oommen equilibrium curves, the shortcomings of the lack of comparability of the humidity criteria for solid and liquid insulation in STO 34.01–23.1– 001–2017 and RD 34.45–51.300–97 are presented. It is proposed to single out several areas of the operational state of transformers in terms of “moisture content of solid insulation (paper)”. The optimal period for the use of AIRS has been determined.The AIRS technology is implemented in a TRANSEC module, which enables to carry out work under load, without adversely affecting predictive diagnostics. The design features of the TRANSEC module, the principle of operation, functional and design differences from standard transformer units – thermosiphon /adsorption filters — are outlined. The results of testing the TRANSEC technology in an existing electrical installation serviced by PJSC Rosseti are presented. Six months of trial operation of the TRANSEC module demonstrated the restoration of insulation characteristics on operating equipment without additional involvement of personnel. Besides, during the testing of the TRANSEC technology, the influence of oil temperature on the information content of the assessment of solid insulation parameters was confirmed. Proposals are formulated on the revision of industry documentation in the established part.