Transformer Failure Research Articles

Stream ciphers for digital image transactions by learning quantum true random numbers

Abstract The digital economy drives a surge in online digital image transactions, increasing the risk of data breaches due to extensive image file transmission. Stream ciphers, known for their efficiency compared to block ciphers, have emerged as a preferred choice for encrypting images in such transactions to safeguard transmitted data. Nevertheless, traditional stream cipher algorithms face diverse security threats. To address this challenge, efforts have been devoted to generating stream ciphers by generative adversarial networks (GANs) transforming input style into random patterns. Regrettably, these ciphers face issues in key sensitivity, randomness, and style transformation failures. Quantum true random numbers offer a potential solution but are costly to deploy. To handle this dilemma, we design stream ciphers relied on a neural network random number generator (RNG) using quantum true random numbers for training least squares GANs. Specifically, two fully-connected layers are incorporated into the RNG, avoiding the defects of style transformation in existing GANs-based stream ciphers. Besides, a random number calculation formula is employed to ensure that each decimal place output by the generator contributes to the computation of the random numbers. By doing so, the randomness of GANs is enhanced and the deployment of costly quantum devices is avoided. Experiments reveal that the information entropy of our generated images reaches to 7.9991, the adjacent pixel correlation coefficient of the ciphertext attains -0.0015, the Number of Pixel Change Rate and Unified Average Changing Intensity achieve 99.62% and 33.52%, respectively. These results demonstrate that the designed RNG facilitates randomness, whilst having secure properties applied in stream ciphers.

Analyzing the Effects of Lightning Strike on Distribution Transformer Failure via Geographically and Temporally Weighted Regression

Analyzing the Effects of Lightning Strike on Distribution Transformer Failure via Geographically and Temporally Weighted Regression

Comprehensive Assessment of Ferroresonance and its Effects in Selected Distribution Substations in Nasarawa State, Nigeria

Ferroresonance in distribution networks generates harmful harmonics, excessive heating, and potential transformer failures, yet limited research focuses on its impact in Nigerian low-voltage systems. This study examines ferroresonance susceptibility in low-voltage distribution transformers, with an emphasis on core saturation, harmonic distortion, and grading capacitance effects under varied operational conditions across substations in Lafia, Akwanga, and Keffi. By analyzing transformer responses from 50V to 500V and harmonic profiles in Delta (1.12Ω) and Star-Grounded (1.53Ω) configurations, the research highlights transformer design's critical role in ferroresonance resilience. Findings reveal that increasing grading capacitance from 90 pF to 1550 pF substantially elevated peak voltages from 0.420kV to 2.400kV (Lafia), 2.450kV (Akwanga), and 2.540kV (Keffi), and currents from 28A, 26A and 27A to 103A, 118A, and 130A, respectively. Voltage THD similarly rose from 6.5%, 6.3% and 6.7% to 35.5%, 38.2%, and 36.0%, respective; with current THD climbing from 7.8% 7.5% and 7.9% to 40.2%, 44.1%, and 42.3% respectively, indicating varying ferroresonance susceptibility across the substations. Delta configurations show lower ferroresonance risk compared to Star-Grounded types, underscoring optimal winding configuration and capacitance management as key resilience factors. Recommendations include targeted harmonic filtering and precise capacitance management to stabilize transformer operation. Future research should consider broader transformer model diversity, adaptive control systems, and machine learning techniques for enhanced ferroresonance risk assessments.

Enhancing Predictive Maintenance of Power Transformers Through Machine Learning Approaches

This paper focuses on the use of machine learning algorithms to assist in predictive maintenance, aiming to reduce downtime and associated costs for electrical equipment. It introduces two machine learning predictive indicators: the Chromatographic Assay Indicator (CAI) and the Electrical Failure Risk Indicator (EFRI), which leverage chromatographic and sensor data, respectively. The CAI indicator was trained on external data, and its generalization capability was assessed using company-specific chromatographic data with expert assistance. The evaluation involved two heuristics, both focusing on keyword identification in free-text diagnoses. Results showed over 90% accuracy in predicting failures in reactors and power transformers, and an AUC of nearly 85% for reactors. Additionally, CAI outperformed classical Dissolved Gas Analysis (DGA) methods. On the other hand, the EFRI indicator was trained using company’s internal data from a monitoring system of operational sensors and maintaining power transformer data. The classification model achieved over 95% accuracy and an AUC of almost 90% on the test set. These results indicate that both indicators can be integrated into a solution to support maintenance specialists in their decision-making processes.

Implementation of Fuzzy Logic Scheme for Assessment of Power Transformer Oil Deterioration Using Imprecise Information

This research aims to analyze the implementation of a fuzzy logic-based approach in improving the diagnosis of power transformer oil deterioration, which is critical for maintaining the efficient performance and operational life of transformers. Traditional diagnoses are based on strict measurements that do not account for the factors of variability and uncertainty of the actual data. In this article, we perform six different types of tests in this regard, and data have been collected during the period of 2021 to 2022 of 188 power transformer failures in the New KotLakhpat Lahore unit, whose voltage range is 132/66 kv and rating capacity is 40/50 MVA. In this case, a fuzzy logic-based scheme is developed based upon the membership function, a rule-based and defuzzification method that works with imprecision and the implementation of uncertainty in assessing the condition of transformer oils. Moisture, acidity, and a dissolved gas analysis indicator, along with other indication approaches such as interfacial tension, viscosity, and tangent delta measurement, are used to analyze the deterioration process in transformer oils. In the visual representation, oil samples with the following properties were first fuzzified: 19.9 mm2/s of viscosity, 0.453 mgKOH/g of acidity, 695 ppm of DGA, 20.8 mg/kg of moisture, 19.98 of IFT, and 4.35 × 100.14 of tangent delta. The output that was generated by software using the values entered into the parameters (HI and Age) after defuzzification is 45. Fuzzy logic serves as a concrete framework for transforming the diagnostics system and deterring the threats to the entire transformer’s health and reliability in the future. By using this technique, various faults were hypothetically and practically analyzed in a transformer to implement early detection technologies with the possibility to reduce maintenance costs and extend operational life up to 45 years. Various case studies indicate the effectiveness of fuzzy logic in comparison to traditional diagnostics.

International case studies: success and failure factors for digital transformation in healthcare

Abstract Background The digitalization of healthcare systems is expected to lead to improvements in the quality and efficiency of healthcare services. Despite Germany’s leading economic and technological position, the implementation and utilization of digital health solutions is lagging far behind internationally. A look at other countries shows that the digital transformation in healthcare can be successful, and that financial resources and available technologies are not the only determining factors. Rather, it is a matter of political processes, the interests of different stakeholders and the acceptance of patients and HCPs. Methods While the impression often arises that the selection of countries in case studies is random, eight international case studies were identified using a scientific method. A systematic and multi-stage process was developed to select the case studies, taking into account various relevant criteria (e.g. degree of digitalization, geography, economy). The implementation and use of various digital health tools will be examined in selected countries and healthcare systems, in which the digitalization of the healthcare system is particularly advanced. Semi-structured interviews will be conducted with experts from the international healthcare systems between fall 2024 and summer 2025. The findings will inform the development of recommendations for actions for policymakers in Germany. Conclusions This study aims to provide new insights into the key success and failure factors for the digital transformation of healthcare systems. Recommendations for action, tailored specifically to the German healthcare system and aimed at policymakers, are intended to support decision-makers in setting the course for a successful further transformation towards digital health. Key messages • International case studies will be used to identify factors that contribute to the success or failure of digital transformation in healthcare systems. • Based on the findings of this research, recommendations for action will be developed for policymakers in Germany to set the course for the successful further digitalization of healthcare.

Small independent retailers: digitalize or disappear? An empirical study based on dynamic capabilities

PurposeThis study analyzes how small French retailers are adapting their front-office to the digitalization of their business environment.Design/methodology/approachThe qualitative study focuses on dynamic capabilities of 27 independent French retailers, in a wide variety of sectors.FindingsThe digitalization of small retailers does not date from the pandemic health crisis. Small retailers are willing, agile and organized to make controlled progress, ranging from the visibility on social networks to online sales with its specific logistical constraints. Even if their presence on marketplaces is trickier to implement, it represents the culmination of the digitalization process, once their online store has been launched. The digital transformation of independent retailers should be less radical than for large retailers.Research limitations/implicationsBy distinguishing between the concepts of adaptive, absorptive and innovative capabilities, this research highlights strong differences between small retailers, that is SMEs, and larger companies. In terms of adaptive capabilities, it confirms that small retailers are not embracing digitalization as a fad, but because of real changes in the market, and particularly in demand. In contrast to large companies, small retailers drive it more around external objectives linked to their intimate knowledge of changing customer behavior (customer centricity). In terms of absorptive capabilities, the success or failure of digital transformation weighs directly on the entrepreneur's shoulders, but is less hampered by technological legacy. Despite interviews only conducted in the Paris region, it converges with professional studies carried out on a larger scale in France. Its widespread use is certainly easier in countries at the same stage of commercial development.Practical implicationsIn terms of innovative capabilities, independent retailers need to focus on four key areas: reinventing the in-store experience; increasing visibility on social networks; creating an online store; being present in one or two marketplaces or creating a common platform with other local merchants.Originality/valueThis research is one of the first to analyze the digital transformation experienced by small structures. It draws on the concept of dynamic capabilities, well-suited to technologically and commercially dynamic markets. It puts into perspective studies carried out in other countries on less diversified types of shops. Unlike other studies examining the front office, it does not exclude stores and SEO in marketplaces.

Internal clock errors in synchrophasor ambient data: Effects, detection, and a posteriori estimation-based correction

Synchrophasor data from real world power systems are exposed to numerous adverse cyber-and-physical operating conditions that can negatively impact their fitness for use in PMU applications. Although the most significant impairments such as GPS loss, instrument transformer failure, data drop-off, etc., have been studied in the literature, there exist other more complex nuisances that impact PMU data quality. This is the case of differences in how clock synchronization, time disciplining, and phasor estimation is performed by different PMU vendors, which have important implications on the PMUs’ data fitness for use in PMU applications. In particular, ambient data applications, which have increasingly become a focus for tracking grid performance indicators, are extremely sensitive to periodic clock errors. Using synchrophasor and waveform data from Dominion Energy’s power system, this article provides an in-depth analysis of the effect of seemingly minor device clock errors on the measurement signals’ content. Finally, a method to distinguish clock errors from normal system dynamics and to correct them is proposed.

Fault analysis of oil-immersed transformer based on digital twin technology

Oil-immersed transformer is one of the important equipment to support the safe and stable operation of power system. At the same time, in the process of transformer operation, transformer failure is inevitable, this kind of failure belongs to multiple latent faults, which seriously endangers the safe operation of the transformer. Therefore, the maintenance of the transformer has played a crucial role. A fault detection method based on digital twin model is proposed to solve the problem that the internal dynamic characteristics of oil-immersed transformer are difficult to extract, and the fault detection error is large due to noise interference. The dynamic model of oil-immersed transformer is constructed, the main cause of failure is analysed, the failure time of internal insulator is taken as a linear index, and the linear correlation between failure probability and failure time is solved by Weibull distribution function. The fitting function between transformer fault residence time, operating days and fault probability variance is established, and the fitting value obtained is used as the initial data reference of the fault detection model. The digital twin model is used to establish the dynamic fault probability calculation function, and the on-site data is substituted to calculate the fault detection threshold, and then the data is compared to complete the detection.

Flash Point Improvement of Mineral Oil Utilizing Nanoparticles to Reduce Fire Risk in Power Transformers: A Review

Transformers are crucial equipment in electrical distribution systems but have a significant potential for failure. Insulation materials, including transformer oil (TO), play a primary role in transformer failures. A fire involving the TO can lead to a large explosion, causing the main tank to rupture and resulting in extensive damage to the entire transformer and the surrounding area. Mineral oil (MO) is the most widely used type due to its availability and relatively low cost compared to other types of oil. However, MO has a critical disadvantage, which is its very low flash point. The low flash point makes MO highly flammable. When the oil fires in an enclosed space, such as a transformer tank, the pressure inside the tank increases, leading to a large explosion. Therefore, research on increasing the flash point of MO is highly necessary. The application of nanotechnology is a promising approach to increasing the flash point of base fluids. Research on the effect of nanoparticles (NPs) on flash points is very limited in the literature; thus, there is significant potential for further research in this field. The majority of studies indicate an increase in flash points with the addition of NPs to MO. There is only one study that shows a decrease in flash point, which is −1.33% compared to MO. From all the reviewed studies, it can be concluded that NPs are a potential solution to increase the flash point of MO. Despite their benefits, NPs require a thorough examination of health and environmental impacts, along with proper waste management, to ensure their advantages.

Electromagnetic–Structural Finite Element Analysis of Copper and Aluminum Windings in Power Transformers under Short-Circuit Conditions

Electromagnetic forces can lead to the structural failure of power transformers due to extreme loading conditions, vibration, and/or fatigue. Therefore, studying the nature and the magnitude of these forces is a key task in the design and failure analysis of such important equipment. Keeping this issue in mind, this work aims at conducting a numerical analysis in order to evaluate the mechanical stresses and displacements of windings in power transformers due to the action of electromagnetic forces. With this purpose, a finite element model is developed considering electromagnetic and mechanical effects assuming short-circuit conditions. The study compares the cases employing copper and aluminum windings with header tap, in different temperatures. The model developed in this work is verified against analytical solutions. The numerical calculations allow for performing a detailed analysis in terms of the distribution of both displacements and stresses along the windings, which is of great relevance for identifying critical structural points and avoiding structural failure. Overall, the obtained results demonstrate that the finite element model is a useful tool for the structural design of power transformers that allow for investigating and optimizing key aspects before manufacturing.

Analysis of Breakdown Characteristics in Nanofluid Insulation Materials with Metal Particle Contamination

Optimal insulation in transformers is essential to maintain their performance and reliability. However, insulation-related problems, particularly transformer oil contamination, often result in premature transformer failure. To address this issue, a solution has been proposed in the form of a nanofluid, a mixture of transformer oil, and nanoparticles. This study aims to investigate the impact of metal particle contamination on the breakdown and levitation voltage characteristics of nanofluids as liquid insulating materials. The nanofluids were prepared by mixing mineral oil with Fe3O4 nanoparticles at three different concentrations. Tests were conducted to evaluate the AC breakdown and levitation voltages for various contaminant metal particle sizes. The experimental results demonstrate that contaminant particle size has a significant impact on the breakdown voltage. The nanofluid with a concentration of 0.008% Fe3O4 exhibited superior breakdown voltage performance compared to mineral oil and 0.016% Fe3O4 nanofluid. Furthermore, an increase in the levitation stress was observed as the contaminant particle size increased. The findings of this study emphasize the importance of controlling contamination and selecting the appropriate concentration of nanoparticles to enhance transformer insulation performance. This study also highlights the necessity of monitoring the size of contaminant particles to prevent potential damage. Consequently, this study makes a significant contribution to the advancement of more reliable and efficient transformer insulation technology.

Intelligent Diagnosis Method of Transformer Based on Oil Chromatographic Data

Abstract As one of the most important equipment in the operation of power system, ensuring the safe and stable operation of power transformer is a prerequisite for ensuring the normal supply of power grid. The failure of the power transformer will lead to the interruption of the power supply of the power grid and cause huge losses to the national economy. With the rapid development of China’s power grid scale and the continuous improvement of the intelligent construction of modern power system, the number of power equipment such as transformers is increasing. Therefore, it is necessary to make timely and accurate judgment on the status of key power transformation and distribution equipment in the power system, and grasp the operation of electrical equipment in real time, so as to ensure the reliability of power supply in the power system. This paper takes 220kV oil-immersed transformer as the research object. This paper considers the problems and characteristics of the current transformer fault diagnosis methods. By making full use of dissolved gas analysis (DGA) information in oil, a method for diagnosing faults in electrical transformers based on particle swarm optimization and generalized regression neural networks has been proposed. The simulation comparison experiment is carried out to select the most suitable transformer intelligent diagnosis method based on oil chromatographic data.

Intelligent Fault Diagnosis Method for Transformer Driven by Multiple Vibration Data

Abstract Early monitoring and early warning of transformer failure are crucial to ensure the safe and dependable functioning of the power system, as transformers are essential equipment within the system. On multivariate vibration data, this work presents a sophisticated approach for diagnosing faults in transformers which integrates entropy model and machine learning. Firstly, a new coarse-grained method is introduced into the improved multiscale fuzzy entropy to improve the disadvantage of the traditional entropy model with large entropy fluctuation under high-scale factors. Furthermore, the features of transformer multivariate vibration signals are extracted by the entropy model. Finally, the extreme learning machine is utilized to achieve efficient detection of transformer defects. This method provides a useful method reference for fault diagnosis of power equipment with multi-vibration signal, and has good reference value.

DC bias impact analysis on the capability of power transformer and failure risks

A significant transition towards low-carbon, renewable energy sources and technological adaptation is mandatory in the present scenario to address climate change and achieve a sustainable energy system. Aligned with the upgradation in technology, as the need for efficient and smart energy grows, power electronics are being extensively used in power systems. Various factors can affect the pervasive use of power electronics associated with power transformers, thereby introducing the DC bias in the system. DC bias in the transformer leads to a shift in operating point, a rise in excitation current and a generation of harmonics. This causes an increase in core losses and temperature, resulting in exceeding the thermal limits and leading to transformer failure. Consequently, there is an emergency need to understand the DC bias capability of power transformers that aims at optimal transformer selection without being overrated. This research employs Ansys maxwell software to model and simulate a 500 kVA, 11kV/420V three-phase power transformer. The experimental investigation is carried out through a scale-down value of a 5 kVA laboratory prototype. The research findings revealed a significant negative impact of DC bias on power transformer capabilities that could be detrimental to its performance, health and lifespan. The analysis results provide valuable insights for the design engineer during the initial stages of designing a high-performance, cost-effective transformer with a low failure rate.

A causal reasoning approach for power transformer failure diagnosis

A causal reasoning approach for power transformer failure diagnosis

Miniaturized quantitative detection of particles in transformer oil based on lensless holographic microscopy

The insulation effectiveness of transformer oil has a substantial impact on the safety of transformers. In the presence of particles within the transformer oil, they are able to adversely affect its performance, potentially originating from electrical arcs or severe carbonization processes. These particles are commonly capable of reducing the insulating capability of the transformer oil, thereby enhancing the risk of transformer failure. To overcome this dilemma, we propose a miniaturized quantitative particle detection approach in transformer oil based on a lensless digital holographic microscope. In general, methods based on traditional high-resolution optical microscopy are essentially limited by their fairly huge cost and importability. However, the proposed approach utilizes a homemade miniaturized model combined with a CMOS image sensor to achieve microscopic imaging. The deployed device here weighs ∼ 50 g, making it portable and affordable for on-the-go detection. By simplifying the microscope system, we can quickly and accurately detect tiny particles in transformer oils. This enables us to efficiently assess the status of impurities in power transformers and to identify potential issues in advance, thereby enhancing the reliability of transformer oil. This detection method not only features simplicity of operation, convenient on-the-go detection, low cost, and high precision but also provides a convenient approach for accurately assessing the condition of impurity particles in power transformers and preemptively identifying potential issues.

Creating a Digital Culture for Digital Transformation: A Literature Review of Practical Steps

Abstract In the context of a world characterized by volatility, uncertainty, complexity and ambiguity, the digital transformation of organizations has become unavoidable. Digital culture has received increased attention as an essential factor in the success or failure of digital transformation. However, there is no generally accepted digital cultural model for organizations to build upon, nor a clear roadmap to follow on how to change the existing culture into a digital one to facilitate the process of digital transformation. This paper represents a review of the so-far proposed digital cultural models, respectively of the action steps suggested by literature for creating a digital culture. First, the paper combines the dimensions of three existing digital culture models into a more comprehensive model and secondly, it structures the action steps by the new digital culture dimensions. The action steps are further divided into high-level and practical ones. This is the first study comprising a sum of action steps towards a digital culture from published literature up to this point. The findings show firstly that digital cultural models overlap to a high degree. Secondly, the suggested action steps are similar, clear patterns becoming recognizable, however that a lot of them remain high-level, abstract or theoretical. This study represents a good start into creating a roadmap for organizations for creating a digital culture, however more research is required to be able to provide a detailed, practical and actionable set of steps for organizations on their digital transformation and digital culture change journey.

Extraction of Dibenzyl Disulfide from Transformer Oils by Acidic Ionic Liquid.

In recent years, dibenzyl disulfide (DBDS) in transformer oils has caused many transformer failures around the world, and its removal has attracted more attention. In this work, nine imidazolium-based ionic liquids (ILs) were applied as effective, green desulfurization extractants for DBDS-containing transformer oil for the first time. The results show that the desulfurization ability of the ILs for DBDS followed the order of [BMIM]FeCl4 > [BMIM]N(CN)2 > [BMIM]SCN > [BMIM](C4H9O)2PO2 > [BMIM]MeSO4 > [BMIM]NTf2 > [BMIM]OTf > [BMIM]PF6 > [BMIM]BF4. Especially, [BMIM]FeCl4 ionic liquid had excellent removal efficiency for DBDS, with its S partition coefficient KN (S) being up to 2642, which was much higher than the other eight imidazolium-based ILs. Moreover, the extractive performance of [BMIM]FeCl4 increased with an increasing molar ratio of FeCl3 to [BMIM]Cl, which was attributed to its Lewis acidity and fluidity. [BMIM]FeCl4 ionic liquid could also avail in the desulfurization of diphenyl sulfide (DPS) from model oils. The experimental results demonstrate that π-π action, π-complexation, and Lewis acid-base interaction played important roles in the desulfurization process. Finally, the ([BMIM]FeCl4) ionic liquid could be recycled five times without a significant decrease in extractive ability.

A Fault Restoration Method for DC-interconnected Distribution Systems with Renewable Resources Considering Multiple Fault Scenes

Background:: The distribution systems are gradually transformed into active systems with the large-scale integration of renewable resources, which brings greater potential to the fault recovery of the distribution networks and higher requirements for the power scheduling capability under fault conditions. Power electronic devices such as soft open points (SOPs) can break the radial constraints of the traditional distribution network, provide cross-station power support in fault scenarios, and enhance the resilience of the distribution networks. Methods:: This paper focuses on the distribution systems with multi-transformer flexible interconnection, and a multi-device coordinated fault restoration method is proposed. Firstly, the characteristics of different transformer fault scenes and the fault restoration process coordinated by the bus-bar switches and SOPs in each scene are sorted out. Secondly, an optimization model of the fault restoration for DC-interconnected distribution system is established considering the network operation and the equipment constraints in the fault restoration process. Result:: The proposed model aims at the maximization of the weighted load restoration amount, and is transformed into a second-order cone programming model to be solved quickly. Lastly, the effectiveness of the proposed method is verified in a typical flexible interconnection distribution system. Conclusion:: The simulation results prove that it can effectively improve the capability of preserving power supply for important loads after transformer failure.