Transformer Failure Research Articles

Research on the Influence of Compression and Offset of Cushion Blocks on the Axial Strength of Transformers

The instability of the winding-cushion structure is one of the primary causes of transformer failures. Insulation cushion compression and offset are the predominant forms leading to structural instability. Therefore, this paper, using the SFSZ7-31500/110 transformer as an example, first derives the theoretical formula for mechanical stress calculation. It clarifies the key influencing parameters of the winding-cushion block structure on the axial bending stress of the winding. Subsequently, an electromagnetic force finite element calculation model is established to obtain the axial force distribution in the winding and the distribution of unbalanced displacement during short-circuit processes. Based on the force and offset distribution, a specific cushion block compression and offset test platform is constructed. By setting different cushion block variables, the effects of cushion block unbalanced height and cushion block offset on the winding’s bending elastic modulus are determined. Finally, a simulation model for stress calculation of the winding-cushion block structure is established, revealing the influence pattern of cushion block compression and offset instability on the axial strength of the winding. The results of this study indicate that the greater the uneven cushion block height, the lower the axial strength of the winding. Under the same cushion block offset angle, winding structures with non-uniform cushion block offsets exhibit the worst axial stability. When the offset angles are 30°, 45°, and 60°, the maximum axial bending stress of the winding increases by 1.73%, 3.46%, and 7.82%, respectively. Increasing the offset angle exacerbates the decrease in the axial strength of the winding up to a certain extent. The findings in this study have significant implications for enhancing a transformer’s short-circuit resistance.

Research on Changes in the Phase Shift Angle and Admittance of the Cellulose–Bio-Oil Composite under the Influence of Increasing Moisture during the Long-Term Operation of Power Transformers

In this study, the temperature–frequency dependencies of the pressboard–bio-oil–water nanoparticle composite’s fundamental parameters—phase shift angle and admittance—were examined using the Frequency Domain Spectroscopy method. Measurements were conducted in a frequency range of 10−4 Hz to 5 × 103 Hz at temperatures ranging from 293.15 K to 343.15 K, with a step of 10 K. The temperature stabilization accuracy was less than ±0.05 K. A total of 15 Arrhenius plots were determined for various phase shift angle values, from which 15 values and the average activation energy of relaxation time were determined. Similarly, the values of the activation energy of admittance relaxation time and the activation energy of admittance were determined. It was established that all three average values are identical within the bounds of uncertainty. Based on 45 values, a generalized activation energy was determined, with a value of ΔW ≈ (1.032 ± 0.0196) eV. Using the generalized activation energy value, the phase shift angle curves determined for all temperatures were recalculated to a temperature of 293.15 K. It was found that after conversion, all curves perfectly overlap. A similar operation was carried out for the frequency dependencies of admittance. In this case, too, the recalculated dependencies perfectly match. This means that the shape of the frequency dependencies of the phase shift angle and admittance depends solely on the moisture content in the pressboard–bio-oil–water nanodrop composite. The position of the curves in doubly logarithmic coordinates depends only on temperature and is determined by the generalized activation energy Using the generalized activation energy determined in this study will allow for the development of accurate methods for estimating moisture content in cellulose insulation of power transformers containing bio-oil. This will contribute to the detection of critical moisture content, which is about 5% by weight, and to the elimination of the risk, associated with such a high moisture content, of catastrophic failure of power transformers.

Predicting the Ageing Time of Thin Film Sacrificial Copper Strip Sensors Over a Wide Range of DBDS Concentrations

Catastrophic transformer failures are often attributed to corrosive sulphur-related faults. However, the existing challenge lies in the absence of practical, real-time monitoring techniques for sulphur corrosion, especially on-site. Previous studies have proposed the use of thin film sacrificial copper strip sensors as a promising solution to address this issue. Yet, their effectiveness in detecting corrosive by-products resulting from varying concentrations of DBDS breakdown remains unexplored. To bridge this knowledge gap, this study introduces an alternative technique that utilizes MATLAB software for linear interpolation and extrapolation. The findings demonstrate the capability of this technique to predict the ageing time of thin film sacrificial copper strip sensors when exposed to corrosive by-products derived from DBDS breakdown across a broad concentration range. This research aims to provide a valuable contribution towards enabling effective monitoring of sulphur corrosion and enhancing transformer maintenance practices.

Insulation aging condition assessment of transformer in the visual domain based on SE-CNN

The aging of transformer oil–paper insulation (OPI) contributes significantly to transformer failure and reduced service life. Therefore, reliable and precise evaluation of the aging status of transformer insulation condition is crucial in averting transformer failure. This article proposes a novel deep learning-based aging condition assessment technique of transformers by combining FDS (Frequency domain spectroscopy) and squeeze-and-excitation-enabled convolutional neural network (SE-CNN). An intrinsic product-based transformation using a Gramian angular field is employed to dimensionally expand the 1-D FDS signals acquired from OPI samples into 2D images. Each feature extraction channel in the SE-CNN-based aging diagnosis framework is assigned a specific weight, enabling the model to concentrate on the most relevant features while discarding the rest. Furthermore, the effect of moisture variation on the model is also considered under three different conditions to ensure the consequence of compensation for the effect of moisture on FDS. A comparison with traditional machine learning models has also been made to ensure the viability of the proposed methodology. Additionally, the model is interpreted by creating explanation weight plots of the feature coefficients using a local interpretable model-agnostic explanations (LIME) method in Python. The findings demonstrate that the novel aging diagnosis framework visually assesses the transformer’s OPI system with the highest accuracy of 99.12% in the test set. Furthermore, it facilitates the automatic extraction of deep aging features, thus providing better generalization and convergence capability.

A Stochastic Load Forecasting Approach to Prevent Transformer Failures and Power Quality Issues Amid the Evolving Electrical Demands Facing Utilities

New technologies, such as electric vehicles, rooftop solar, and behind-the-meter storage, will lead to increased variation in electrical load, and the location and time of the penetration of these technologies are uncertain. Power quality, reliability, and protection issues can be the result if electric utilities do not consider the probability of load scenarios that have not yet occurred. The authors’ approach to addressing these concerns started with collecting the electrical load data for an expansive and diverse set of distribution transformers. This provided approximately two-and-a-half years of data that were used to develop new methods that will enable engineers to address emerging issues. The efficacy of the methods was then assessed with a real-world test dataset that was not used in the development of the new methods. This resulted in an approach to efficiently generate stochastic electrical load forecasts for elements of distribution circuits. Methods are also described that use those forecasts for engineering analysis that predict the likelihood of distribution transformer failures and power quality events. 100% of the transformers identified as most likely to fail either did fail or identified a data correction opportunity. The accuracy of the power quality results was 92% while allowing for a balance between measures of efficiency and customer satisfaction.

Distribution Transformer Failure Prediction for Predictive Maintenance Using Hybrid One-Class Deep SVDD Classification and Lightning Strike Failures Data

The distribution transformer failure may be influenced by its maintenance history and risk index in a distribution network associated with keraunic level, average number of lightning strikes, and protection devices employed. Transformer failure is a rare event, and the number of "failed" labels is much smaller than that of "non-failed" labels in field monitoring data. Therefore, the transformer failure prediction can be formulated as an anomaly detection or binary classification with an imbalanced dataset, which is challenging to handle. In this paper, we propose a novel distribution transformer failure prediction method through a hybrid one-class deep support vector data description (SVDD) that uses the synthetic minority oversampling technique (SMOTE) to handle the data imbalance between minority and majority class labels. Minimum redundancy maximum relevance (mRMR) is used as a feature selection technique to improve the model's accuracy. The proposed method uses the current condition data of transformers and the distribution network to predict transformer failure for the next year. Real-world field data for 15,066 distribution transformers is used to train and validate the proposed method. It shows superior performance when compared against five benchmark approaches.

Improved RAkEL's Fault Diagnosis Method for High-Speed Train Traction Transformer.

The traction system is very important to ensure the safe operation of high-speed trains, and the failure of the traction transformer is the most likely fault in the traction system. Fault diagnosis in actual work relies largely on manual experience. This paper proposes an improved RAkEL (Random k-Labelsets) algorithm for the fault diagnosis of high-speed train traction transformers. Firstly, this article starts from the large amount of "sleeping" fault maintenance data accumulated by the railway department, takes a single maintenance record as an instance, uses specific monitoring values to construct an instance vector, and uses the fault phenomena corresponding to the monitoring indicators as labels. Then, this paper improves the step of selecting k-labelsets in RAkEL, and extracts associated faults using the Relief algorithm. Finally, this paper excavates and uses the association rules between data and faults to identify traction transformer faults. The results showed that the improved RAkEL diagnostic method had a significant improvement in the evaluation indicators. Compared with other multi-label classification algorithms, including BR (Binary Relevance) and CLR (Calibrated Label Ranking), this method performs well on multiple evaluation indicators. It can further help engineers perform timely maintenance work in the future.

Digital transformation as a factor of changes in the organizational behaviour of international companies

Digitalization is an essential process for enterprises in the post-industrial economy, which contributes to increasing their competitiveness and opens up new opportunities for involvement in global digital value chains. Digitalization practice provides for developing recommendations and algorithms for overcoming and preventing errors within the scientific discourse, thus the aim of the article was to determine the impact of digital technologies on the organizational behaviour of international companies and generalize causes of digital transformation failure. Changes in organizational behaviour under the influence of digitalization have been systematized using the generalization method. The causes of unsuccessful digital transformations in organizations have been structured using the five-stage digital transformation model. The main factors of failures at each stage have been discovered. At the stage of automation errors result from the failure to understand the mission and value of the business and to implement digital technologies. At the isolated stage the negative factor is the insufficient support of priority changes and the wrong choice of what exactly needs to be transformed. It has been discovered that at the stage of partial synchronization systemic digital transformation is hindered by an ineffective change management strategy or an insufficient number of transformation projects for adequate change of the overall operation. Problems with the organizational structure or digital literacy can lead to the disruption of digital transformation at the stage of full synchronization. The risks of continuous transformation stage result from losing the priority, ensuring the transformation, due to inflexible culture, lack of discipline regarding the constant identification of new business disruption risks and responding to them. The practical significance of the present research is stipulated by the proposed recommendations for changes in the organizational behaviour of companies that will reduce the risks of failures in particular and failures of digital transformation as a whole

Analyses on an electronic voltage transformer's failure by its resonance with very fast transient overvoltage and suppression

AbstractAs voltage measuring devices are widely used in the high voltage power system with a primary‐secondary‐fusion structure, electronic voltage transformers are directly connected to the primary conductor and would face much more serious electromagnetic environments than general secondary equipment. As a result, they would still experience failures even when general protection measures are adopted to suppress overvoltages' amplitudes. To address this issue, an innovative method is proposed after analyses are conducted on the resonance between a transformer and an overvoltage. Within this method, an air‐cored coil is installed in a transformer to artificially shift its natural frequency and to dodge the high‐frequency dominant component of a transient overvoltage, avoiding the aforementioned resonance and suppressing the secondary overvoltage. In addition, simulations and laboratory tests are conducted to prove this method's validity.

Dielectric Failure Analysis of Distribution Transformer

The distribution Transformer acts an important function in the electrical distribution network. Failure of the transformer ends in an outage of power supply to end users. Failure of the distribution transformer usually results in the spare time of some weeks transformer accompanied by commercial misfortunes and the necessity to fix the system at the earliest. The prime reason for the outage of distribution transformers is a short circuit, insulation breakdown, and prolonged overloading. This paper investigates failure analysis of distribution transformers during separate source voltage withstand tests, induced over-voltage withstand tests and short circuit withstand strength tests. In this paper failure cases of three distribution transformers are resolved and the results are analyzed.

Analyzing Organization Culture’s Role on Digital Transformation During Pandemic Era: An Ethnographic Study

Public behavior toward shopping is changing due to limited mobility. E-commerce has changed consumer shopping behavior. The pandemic has accelerated the digital transformation process. From this, the company finally decided to carry out digital transformation and try to sell their product to e-commerce. The company uses digital marketing to market its brands effectively to the public during a pandemic. The application of digital transformation makes organizational culture an important thing that influences the success or failure of digital transformation. The culture in each company will make the strategy of each company different. Corporate culture has a role in measuring the company's success in implementing digital transformation to deal with the pandemic. Because of this, this study uses Hofstede's organizational culture theory and cultural dimension theory. Ethnography is the method in this research because researchers can focus on culture. In general, ethnography is for researchers who are outside the organization. However, in this study, researchers are part of an organization, so they have a more in-depth picture of the culture that occurs within it. From this study, the symbol in each category of organizational culture in the company has a unique meaning, which plays a role in digital transformation that will impact company performance. The combination of cultural dimensions is also a factor in the success of digital transformation in companies. Culture, organization, consumers, technology, and strategy are five things in digital transformation that affect company performance.

Interpretation of IS/IEC and IEEE Standards for Dielectric Type Testing of Instrument Transformers

The requirements of Instrument Transformers are given in standards IS16227-1, IEC 61869-1 and IEEE Std. C57.13.5. Indian Standard IS 16227-1 is in line with IEC 61869-1. Most of the utilities in India are the following IS: 16227-1/IEC 61869- 1. Many failures of the high voltage instrument transformers are being reported by the utilities. In this connection, a review of the requirements/test procedure and acceptance criteria specified by IEC 61869-1 and IEEE Std C57-13.5 for dielectric-type tests are reviewed in this paper which may help decision-making for adopting a particular standard for dielectric-type tests on instrument transformers considering Indian climatic conditions.

Development of fuzzy classifier for technical condition ranking of power transformer

The work aim is to develop a fuzzy classifier for technical condition ranking of power transformer under condition of vagueness and ambiguity diagnostic information. Methodology. The fuzzy classifier developing for technical condition ranking of power transformer was based on approach of using fuzzy set theory and optimization methods. The proposed approach for power transformer rank assessment by using a classifier was developed on the basis of Takagi-Sugeno fuzzy inference system. The input indicators choice is justified and their efficiency for classifier is evaluated by expert evaluation method. This makes it possible to formalize expert assessments regarding the development of power transformer defects. Results. The formalization of technical condition assessment of power transformer in knowledge base form, which implemented in expert system prototype for technical condition assessment, was carried out. The complex technical condition assessment for each functional unit of power transformer was determined based on expert evaluations with using the test and measurement parameters results. Originality. The considered approach to formalization of uncertainty regarding technical condition of power transformer allows building a deterministic decision-making scheme for further maintenance strategy, in which the ranking and decommissioning procedures for specific objects are implemented on the basis of objective criteria. Practical value. The proposed fuzzy classifier allows determination with a high probability degree of technical condition assessment of power transformer based on the test and measurement parameters results. Thus, an applied aspect of using the obtained scientific result is the possibility to objectively rank of power transformers park based on the identified possible defects and their development degree. This constitutes the prerequisites for determining the failure probability evaluation of power transformer at nearest observation period and emergency risk assessment in integrated electric power systems under power transformer failures.

Ключевые элементы цифровой трансформации бизнеса

In the article, based on the analysis of statistical data on the introduction of digital technologies by small and medium-sized businesses in the Russian Federation, a trend in the sustainable development of business digitalization is revealed. During the study of the current direction of the development of the world economy, it was noted that the trend of digital transformation of enterprises is due to the need for business to adapt to new benchmarks of the economy. But despite the relevance of introducing digital technologies, experts note that more than 70% of digital projects are failures. Accordingly, the purpose of this study is to determine the reasons for such an extremely high value of unsuccessful digitalization. As a result of the study, the key elements of digital transformation have been identified, the inattentive attitude to which leads to the disruption of digital progress. Thus, it is determined that the inattentive attitude of business to digital strategy, personnel, organizational structure, customers, ecosystem, technology, and innovation is the reason for the failure of digital transformation.

Measurement and Analysis of the Switching Transients in 500 kV Pumped Storage Power Plants

Pumped storage power plants (PSPPs) are among the most efficient and practical large-scale energy storage systems. In recent years, several transformer failures have occurred in PSPPs, and transient overvoltage is considered a potential reason for failure. However, there is a lack of field measurement results for transient analysis. In this paper, a switching transient measurement system and multipoint synchronous triggering system were developed. Field measurements were carried out in three PSPPs. According to the measurement results, the maximum amplitude of the switching overvoltage was 630.6±46.5 kV. The shortest front time was 76±7.6 ns. The largest steepness was 1130±140.8 kV/μs. A simulation model was built to analyze three phenomena in the transient, including multistep waveform, overvoltage peak, and power transformer demagnetization. The measurement and analysis results are beneficial for the improvement of simulation modeling. It is helpful to pinpoint potentially damaging transients for equipment manufacturers and designers of PSPPs.

Operational failure mechanisms of electromagnetic potential transformer

In response to the problem of transformers blowing up or even burning up together with detuning devices in recent years, an assessment method based on the equivalent of transformer winding currents is proposed for potential transformers (Potential Transformer, PT) in low and medium voltage distribution networks. Firstly, the damage mechanism of potential transformers is analyzed and the thermal breakdown is identified as the main cause of damage to the winding insulation. At the same time, the winding currents under different faults are analyzed theoretically to identify the characteristics of various fault currents. Secondly, an operational failure indicator based on thermal equivalence, i.e. the fault current threshold indicator, is proposed in conjunction with the general voltage transformer specification. Finally, the proposed indicator is verified by means of ATP simulation software and combined with simulation tests. The results show that the proposed index can accurately reflect the winding failure of potential transformers, and the research results can provide a reference for the selection of potential transformers and the prevention of insulation breakdown.

Initial temperature of bubble formation at Oil-Paper interface based on bubble dynamics

The initiation of bubbles at the surface of insulating paper under high temperature may result in partial discharge and ultimately lead to the insulation failure of oil-immersed transformers. To study the formation mechanism of bubbles, the process of bubble growth at the oil-paper interface is established by using Rayleigh-Plesset equation. The Ideal Gas Law as well as the Hertz-Knudsen relation is used to quantify the flux of water vapor entering the bubble. The growth curve of bubbles in the oil-paper during temperature rise is calculated. And the Initial Temperature of Bubble Escape (ITBE) is then obtained based on the force analysis of the bubble growth. The results show that the bubble detaches from the surface of paper when its radius reach 198 μm, which is consistent with experimental observations. And the minimum average relative error between the predicted ITBE and experimental results is 1.11%. The effect of influence factors on the ITBE of oil/paper are then studied in detail. It is shown that the increased moisture content in paper accelerates the growth rate of bubbles, then reduces the ITBE. And the higher pressure inside oil tank restrains the bubble growth through the overall process. Additionally, as the cavity size of paper increase, the surface tension of bubble increases. Consequently, the bubbles require a larger size to achieve sufficient buoyancy for their escape from the aged paper.

Monitoring Transformer Condition with MLP Machine Learning Model

Failures of large power transformers in transmission system are always followed by significant costs, which is especially problematic because they present an unplanned expenditure. Aside from derailing financial plans, these events can lead to lower system reliability. This paper describes the development and potential application of transformer model based on multilayer perceptron class of artificial neural networks. Model is built in Python programming language and data collected over the span of one year for a single transformer. Three input features (oil temperature, winding current and outside temperature) are used in the input layer, with the goal of predicting the winding temperature in the transformer. Predicted temperature of the windings can then be compared with the actual winding temperature, which can serve as an indicator of transformers internal condition. Two types of transformer condition degradation are simulated to show the model in action, and certain indicators are explored.

Integral Approach for Overvoltage Management Based on Field Data and Modelling

The requirements for the power network to be flexible and to be able to transmit more power are becoming more important as a consequence of high share of the renewables. Therefore, to maintain the level of reliability of the power network, it is necessary to monitor more and more data as well as to better understand the phenomena that might exist in the power system, such as transients. One of the points that are of interest are interaction between power transformer and the other components in the network. A failure of such component may lead to power network unavailability and high economic cost. According to the CIGRE WG A2.37, high portion of the transformer failure may have been caused by the overvoltage. Therefore, it is important to handle overvoltage in the power network with high care. In this paper, an integral approach for power transformer overvoltages management based on field measurements and advanced electromagnetic transients (EMT) modelling is presented. It consists of simulating overvoltages using detailed power network models, including the high frequency transformer models and continuous monitoring of overvoltage events at the places of interest in the power network. This work gives a framework for dealing with overvoltages in the power network that could be implemented by power utilities.

Bubble Effect Phenomenon in Modern Transformer Insulation Systems Using Aramid-Based Materials and Alternative Insulating Liquids

One of the possible causes of transformer failures is high moisture in the winding insulation system. In an extreme case, when the critical temperature is exceeded, a sudden release of water vapor from the transformer insulation, called the bubble effect, can occur. This article analyzes the initiation temperature of the bubble phenomenon in various solid insulation materials (Kraft cellulose paper and aramid-based high-temperature papers such as Nomex® 910 and Nomex® 926) immersed in two electro-insulating liquids (mineral oil and Midel 7131 synthetic ester). The initiation temperature of the bubble effect depends mainly on the moisture content of the solid insulation, but it was found to be slightly lower for high-temperature materials than for cellulose. However, after taking into account the differences related to uneven water absorption of the tested materials, the differences in the initiation temperature of individual solid materials are very small. Synthetic ester, compared to mineral oil, slightly increases the bubble initiation temperature, regardless of the solid material used.