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Over the past few years, the COVID‐19 virus has had a significant impact on the physical and mental health of people around the world. Therefore, in order to effectively distinguish COVID‐19 patients, many deep learning efforts have used chest medical images to detect COVID‐19. As with model accuracy, interpretability is also important in the work related to human health. This work introduces an interpretable vision transformer that uses the prototype method for the detection of positive patients with COVID‐19. The model can learn the prototype features of each category based on the structural characteristics of ViT. The predictions of the model are obtained by comparing all the features of the prototype in the designed prototype block. The proposed model was applied to two chest X‐ray datasets and one chest CT dataset, achieving classification performance of 99.3%, 96.8%, and 98.5% respectively. Moreover, the prototype method can significantly improve the interpretability of the model. The decisions of the model can be interpreted based on prototype parts. In the prototype block, the entire inference process of the model can be shown and the predictions of the model can be demonstrated to be meaningful through the visualization of the prototype features. [ABSTRACT FROM AUTHOR]

Power outages can cause harm to both society and individual health. Among the crucial elements of power systems, power transformers play a vital role, and their failures can lead to incidents and disruptions. According to statistics, almost 50% of the explosions and fires in the power transformer are caused by oil‐impregnated paper (OIP) bushing failures. Moreover, moisture is a crucial factor that affects the health of OIP bushings, and assessing the moisture level in OIP bushings is vital to guarantee the safety of power systems. While the frequency‐domain dielectric spectroscopy (FDS) method has been widely used to assess the moisture content of OIP bushings on‐site, it has the disadvantage of requiring a long measurement time. Therefore, a novel technique was proposed in this study that involves measuring the OIP bushing dissipation factor (tanδ) at 1 Hz to estimate the moisture content in a significantly shorter time. Five samples of OIP with various moisture levels have been selected, and an equation based on the OIPs' value of tanδ at 1 Hz was proposed to assess the moisture level of OIP bushings. Furthermore, the validity of the proposed method was successfully verified, proving its precision and time efficiency. [ABSTRACT FROM AUTHOR]

Existing interturn fault detecting methods rely on winding impedance, winding current, and dissolved gases. They are effective only when the insulation is severely damaged. This paper proposes a novel detection method based on fusion analysis of electrothermal characteristics including winding currents, temperatures of four areas on the tank wall, top oil and ambient temperatures, which can identify the interturn fault at an early stage. When an incipient interturn fault occurs, the heat generated by the faulty turns is transferred to the oil and tank wall, leading to an increase in top oil and tank wall temperatures. Thus, the incipient fault can be detected by analysing these electrothermal characteristic parameters. Borrowing the idea of digital twin (DT), this method establishes a high‐fidelity simulation model to simulate the transformer electrothermal characteristics under different operating conditions. Afterward, an intelligent neural network is adopted to extract the quantitative relationship between the eight feature attributions and fault conditions. Finally, this neural network is utilized to detect the incipient interturn fault for the transformer entity. Case studies are conducted on a 100 kVA transformer with oil natural air natural (ONAN) cooling mode. The detection accuracy is improved by 68.5% compared to the winding current‐based method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel evaluation method to address the challenge of evaluating insulation degradation in power transformer windings based on incomplete online Internet of Things (IoT) sensing data. The method leverages the Wasserstein Slim Generative Adversarial Imputation Network with Gradient Penalty algorithm to fill the irregularly missing power transformer IoT perception data, including voltage, current, temperature, and partial discharge. Subsequently, electrical, thermal, and mechanical performance degradation damage indicators for transformer winding insulation are constructed using the filled and complete IoT perception data. By applying the tensor fusion algorithm, the characteristics of these degradation damage indicators are fused, leading to the development of a comprehensive degradation evaluation index for the winding insulation. The evaluation of the winding insulation degradation state is achieved through the minimum quantization error method. The proposed method is validated using the real‐world transformer IoT perception data, and the experimental results demonstrate its ability to accurately assess the degree of winding insulation degradation, regardless of the presence of random or continuous irregularities in IoT sensing data. [ABSTRACT FROM AUTHOR]

Few-shot semantic segmentation (FSS) is a challenging task that aims to segment new classes in query images with a few annotated support samples. One inherent challenge in FSS is the intra-class variation resulting from the limited availability of support samples and the diversity of query data. Current methods frequently employ prototype-support techniques to tackle this issue. However, it is important to note that a single support prototype shares limited commonalities with query features, and increases the difficulty of accurate segmentation. In this paper, we propose a lightweight and effective framework named Siamese Transformer (SiaT) with a mere 0.68M learnable parameters to enhance commonalities between prototypes and query features. The SiaT framework consists of two key modules: the Siamese Transformer Module (STM) and the Query Activation Module (QAM). The STM integrates two shared Transformer decoders for information propagation to generate two enhanced prototypes. One Transformer decoder facilitates the propagation of target-related information from the query to the original support prototype, while the other propagates foreground information from the support to the initial query prototype. The QAM utilizes the two enhanced prototypes from the STM as support information, engaging with query features through channel-wise allocation and concatenation, termed query activation. Moreover, SiaT showcases competitive performance on the widely used benchmarks PASCAL- 5 i and COCO- 20 i , which demonstrates its effectiveness in addressing the intra-class variation challenge within FSS tasks. [ABSTRACT FROM AUTHOR]

The single-stage dual active bridge (DAB) AC-DC converter has the advantages of high power density, low cost, and simple control; it has a broad potential for application in the field of onboard chargers (OBC). However, the lack of fast and accurate quantitative parameter optimization design methods in single-stage DAB AC-DC converters limits the overall efficiency of the converter. Based on the above problem, in order to improve the overall operating efficiency of the converter by optimizing the parameter transformer ratio and power inductance, this paper proposes a parameter design method considering a multi-timescale strategy by combining the steady-state analysis model of the converter in the line cycle and switching cycle and step-by-step reducing its design space through the constraints on the parameters. The first step is to obtain a safe design space for the parameters under the converter's transmitted power and current stress constraints. The second step obtains the optimization design space of the parameters under the optimization of conduction loss and switching loss of the converter. Finally, the optimal parameters are determined by the loss analysis model. The proposed parameter optimization method entirely takes into account the steady-state characteristics of the DAB AC-DC converter during the line cycle, and the step-by-step constraints greatly accelerate the parameter design process. In addition, the proposed parameter optimization design method applies to all types of single-stage DAB AC-DC converters, which can be well applied to engineering practice. [ABSTRACT FROM AUTHOR]

This paper presents the results of the study of the direct current (DC) and alternating current (AC) electrical properties of an electrical pressboard–bio-insulating oil–water composite in a wide range of water content and temperatures used in electric power transformers. These parameters allow the level of insulation reliability to be determined after many years of operation of power transformers. To analyse the experimental results, a model of the DC and AC conductivities of nanocomposites based on the quantum-mechanical phenomenon of electron tunnelling was used. It was found that in a low-frequency region, the conductivities of AC and DC and their activation energy are equal. The relaxation times of AC conductivity and permittivity are also equal. It was found that the dependence of the DC conductivity on the distance between water molecules is an exponential function. On the basis of the model of conductivity by electron tunnelling between potential wells, the average number of water molecules in a nanodroplet, located in a composite of electrical pressboard–bio-insulating oil–moisture was determined to be (126 ± 20). It was found that the measured dependencies of DC and AC conductivity, permeability and dielectric relaxation times are consistent with the results of computer simulations performed on the basis of the model. This study showed that the composite of pressboard impregnated with bio-oil spontaneously transforms through water absorption into a pressboard–bio-oil–water nanocomposite. These will serve as the basis for the application of actual conductivity and dielectric relaxation mechanisms to improve the accuracy of moisture estimation in the solid component of power transformer insulation carried out on the basis of measurements of DC and AC properties. This will improve the operational safety of the transformers, minimise the occurrence of transformer failure and the associated environmental pollution. [ABSTRACT FROM AUTHOR]

Mineral oil has been used for many years in various electrical equipment, including transformers, as a cooling and insulation medium. However, its low biodegradability and poor performance in terms of fire protection have prompted the search for fluids to replace it, with vegetable oils being prominently considered. In this study, the dielectric, chemical, and physical properties of four vegetable oils obtained from different seeds (sunflower, rapeseed, soybean, and palm) and a biodegradable synthetic fluid are analysed throughout their lifespan in transformers. Their performances are compared with a traditional mineral oil to assess which one is more suitable for use in transformers employing this type of paper. To achieve this, the fluids were subjected to thermal ageing in combination with copper and a thermally upgraded kraft (TUK) paper, with its degradation controlled by measuring the degree of polymerisation. The results demonstrate that the origin of the vegetable oils affects their properties and degradation rates. It was found that most of the alternative fluids are suitable for use in transformers with the TUK paper and that they can increase their lifespan. [ABSTRACT FROM AUTHOR]

To improve the power density of series‐connected multi‐pulse rectifiers (MPRs), the harmonic generation principle is studied and the optimization design method for main transformers is proposed. According to the topology of series‐connected 18‐pulse rectifier, its operation waveforms are analysed, and the Fourier series of the waveforms is obtained. Based on the operation waveforms and its Fourier analysis, the principle for harmonic generation of the rectifier is researched, and the phenomenon of autotransformer cannot be applied to series‐connected MPRs is researched. According to the characteristics of the transformer, its vector graphics are analysed. Based on the vector graphics, the optimal structure of the transformer with the highest power density is obtained. The results show that the premise of the normal operation of series‐connected MPRs is the potential difference between the neutral points of different secondary windings, and only isolation/semi‐isolation transformers meet the requirement; the kilovolt‐ampere (kVA) rating of the optimal isolation type transformer is 104.6% pu, and the kVA rating of the optimal semi‐isolation type transformer is about 71.23% pu. [ABSTRACT FROM AUTHOR]

The rapid growth of the Chinese economy has fueled the expansion of power grids. Power transformers are key equipment in power grid projects, and their price changes have a significant impact on cost control. However, the prices of power transformer materials manifest as nonsmooth and nonlinear sequences. Hence, estimating the acquisition costs of power grid projects is difficult, hindering the normal operation of power engineering construction. To more accurately predict the price of power transformer materials, this study proposes a method based on complementary ensemble empirical mode decomposition (CEEMD) and gated recurrent unit (GRU) network. First, the CEEMD decomposed the price series into multiple intrinsic mode functions (IMFs). Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF. Then, an empirical wavelet transform (EWT) was applied to the aggregation sequence with a large sample entropy, and the multiple subsequences obtained from the decomposition were predicted by the GRU model. The GRU model was used to directly predict the aggregation sequences with a small sample entropy. In this study, we used authentic historical pricing data for power transformer materials to validate the proposed approach. The empirical findings demonstrated the efficacy of our method across both datasets, with mean absolute percentage errors (MAPEs) of less than 1% and 3%. This approach holds a significant reference value for future research in the field of power transformer material price prediction. [ABSTRACT FROM AUTHOR]

This paper proposes a method of beforehand prediction of electric equipment faults based on chain-linked recurrent neural network algorithm, which takes the operating parameters of power equipment and other relevant environmental factors as inputs, and takes the fault characteristics as output judgment marks, and constructs a machine learning training model to realize the prediction of power equipment faults. The neural network algorithm adopted in this paper adopts a tree structure. Each sub-node can transfer information with its multiple superior nodes, so that the correlation between the data of the front and back nodes can be obtained, which meets the needs of the equipment fault prediction model. Considering that the occurrence of power transformer faults is sudden and greatly affected by changes in the surrounding environment, the input of prediction algorithms should consider more environmental factors. This method takes the historical data of various parameters including meteorological phenomena, geography data, and temperature of adjacent equipment and facilities as the training sample set, improves the learning model, gives the trend curve of each index, and gives a prompt at its threshold to ensure the prediction accuracy and give the index prediction. [ABSTRACT FROM AUTHOR]

Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of failures in low-voltage terminal connections within power transformers and proposes a fault classification methodology based on infrared thermography (IRT). Low-voltage terminal connections play a critical role in the reliable operation of power transformers, and their failures can lead to severe operational issues. In this study, we employ IRT as a noninvasive and efficient diagnostic tool to identify and classify various types of failures, including loose connections, overheating, and corrosion. The research involves the collection of infrared thermograms (IRT images) from the low-voltage terminals of power transformers under different operating conditions. The proposed methodology demonstrates its effectiveness in detecting and classifying low-voltage terminal connection failures, thereby enabling timely preventive maintenance and minimizing the risk of transformer malfunctions. This research contributes to enhancing the reliability and longevity of power transformers, reducing downtime, and optimizing maintenance practices in the power industry. [ABSTRACT FROM AUTHOR]

In this work, a mathematical model of a three-phase nonlinear transformer is suggested. The model enables simulating the transformer operation with allowance for its nonlinearity and covers needs of the relay protection. Our model has been developed on the basis of a mathematical model with phase coordinates, where differential equations are composed by the Kirchhoff's phase-voltage law. Based on this model, we first compose a mathematical model for simulating steady-state operation modes of a transformer, taking into account the asymmetry and nonlinearity of its ferromagnetic core. In this model, the initial values of inductances and mutual inductances of loops are determined from the main phase inductance calculated by the experimentally found no-load current, and their current values are determined from the currents in windings and the magnetic fluxes in legs of the transformer core. The magnetic fluxes are calculated by the nodal-pair method. This improved mathematical model is verified through a comparison between the calculated harmonic components of the phase currents and the experimental results. The harmonic components are calculated with the use of Fourier expansion of the calculated phase currents. Their experimental values are determined with a spectrum analyzer. The calculated and experimental harmonic components of the currents of phase A during no-load and rated-load operation of the transformer are tabulated. The comparison of these results shows that the mathematical model of a three-phase transformer we suggest makes it possible to simulate currents in transformer windings under steady-state operation modes with accuracy acceptable for relay protection. [ABSTRACT FROM AUTHOR]

An On-Load Tap Changer (OLTC) that regulates transformer voltage is one of the most important and strategic components of a transformer. Detecting faults in this component at early stages is, therefore, crucial to prevent transformer outages. In recent years, Hydro Quebec initiated a project to monitor the OLTC's condition in power transformers using vibro-acoustic signals. A data acquisition system has been installed on real OLTCs, which has been continuously measuring their generated vibration signal envelopes over the past few years. In this work, the multivariate deep autoencoder, a reconstruction-based method for unsupervised anomaly detection, is employed to analyze the vibration signal envelopes generated by the OLTC and detect abnormal behaviors. The model is trained using a dataset obtained from the normal operating conditions of the transformer to learn patterns. Subsequently, kernel density estimation (KDE), a nonparametric method, is used to fit the reconstruction errors (regarding normal data) obtained from the trained model and to calculate the anomaly scores, along with the static threshold. Finally, anomalies are detected using a deep autoencoder, KDE, and a dynamic threshold. It should be noted that the input variables responsible for anomalies are also identified based on the value of the reconstruction error and standard deviation. The proposed method is applied to six different real datasets to detect anomalies using two distinct approaches: individually on each dataset and by comparing all six datasets. The results indicate that the proposed method can detect anomalies at an early stage. Also, three alarms, including ignorable anomalies, long-term changes, and significant alterations, were introduced to quantify the OLTC's condition. [ABSTRACT FROM AUTHOR]

Lane detection plays a pivotal role in the successful implementation of Advanced Driver Assistance Systems (ADASs), which are essential for detecting the road's lane markings and determining the vehicle's position, thereby influencing subsequent decision making. However, current deep learning-based lane detection methods encounter challenges. Firstly, the on-board hardware limitations necessitate an exceptionally fast prediction speed for the lane detection method. Secondly, improvements are required for effective lane detection in complex scenarios. This paper addresses these issues by enhancing the row-anchor-based lane detection method. The Transformer encoder–decoder structure is leveraged as the row classification enhances the model's capability to extract global features and detect lane lines in intricate environments. The Feature-aligned Pyramid Network (FaPN) structure serves as an auxiliary branch, complemented by a novel structural loss with expectation loss, further refining the method's accuracy. The experimental results demonstrate our method's commendable accuracy and real-time performance, achieving a rapid prediction speed of 129 FPS (the single prediction time of the model on RTX3080 is 15.72 ms) and a 96.16% accuracy on the Tusimple dataset—a 3.32% improvement compared to the baseline method. [ABSTRACT FROM AUTHOR]

A 500-kV power transformer substation was focused on for seismic performance examination. Finite element models of the power transformer, isolation layers, site soil, bus bars and support structure were established under various analysis conditions. By inputting typical ground motions into the models, seismic time history responses were obtained. First, the seismic performance, critical position and failure modes of a single power transformer without isolation were examined. Then, the effect on the power transformer by arranging isolation bearings was confirmed. Moreover, the influence of site soil dynamic characteristics on the isolating performance of the power transformer was examined. Finally, the influence of the length margin of the bus bars on the seismic response of the isolated power transformer was analyzed. It was revealed that the bottom of the transformer bushings which were made of porcelain was a weak part. Installing isolation bearings effectively reduced the maximum stress response of the bushing for most ground motions. The dynamic characteristics of the site soil significantly affected the isolation efficiency of the power transformer, and the expected isolation effect was achieved when the soil was soft. The displacement response of the transformer was largely increased by isolation, the pulling effect of the bus bars on the porcelain bushing was significant. When the length margin of the bus bars was enlarged to 1.0 m, the influence on the bushing decreased to a low level that could be ignored. [ABSTRACT FROM AUTHOR]

With the introduction of numerous technologies and equipment, the volume of data in smart substations has undergone exponential growth. In order to enhance the intelligent management level of substations and promote their efficient and sustainable development, the one-key sequential control system of smart substations is being renovated. In this study, firstly, the intelligent substation is defined and compared with the traditional substation. The one-key sequential control system is introduced, and the main issues existing in the system are analyzed. Secondly, experiments are conducted on the winding temperature, insulation oil temperature, and ambient temperature of power transformers in the primary equipment. Combining data fusion technology and transformer neural network models, a Power Transformer-Transformer Neural Network (PT-TNNet) model based on data fusion is proposed. Subsequently, comparative experiments are conducted with multiple algorithms to validate the high accuracy, precision, recall, and F1 score of the PT-TNNet model for equipment state monitoring and fault diagnosis. Finally, using the efficient PT-TNNet, Random Forest, and Extra Trees models, the cross-validation of the accuracy of winding temperature and insulation oil temperature of transformers is performed, confirming the superiority of the PT-TNNet model based on transformer neural networks for power transformer state monitoring and fault diagnosis, its feasibility for application in one-key sequential control systems, and the optimization of one-key sequential control system performance. [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]

This paper develops a technical framework for the next‐generation power grid transformer (NGPGT) for grid renewables. This framework is structured to overcome the environmental challenges produced by the explosive use of nonrenewable base energy generation sources. The use of these sources cannot meet the required electricity for the world's growing community due to their availability, cost, and lack of flexibility. However, modern energy systems focus on the use of renewable energy sources, where the grid transformer's interaction plays an essential role in their generation, transmission, and distribution. The lack of centralization, local monitoring, interoperability, authenticity, and precise bi‐directional flow may limit the application of current framework power grid transformers in grid renewables. In this paper, a new technical framework, called NGPGT, is developed by introducing some extended features for addressing the challenges shown in current‐generation transformers. This is structured by enabling some advanced technical features with the existing framework, which includes automatic condition monitoring, intelligent inverters, edge computing, automatic controlling, and intelligent management. This paper also illustrates the benefits and scope of the NGPGT compared to the existing transformer by assembling essential requirements and obligatory components. Additionally, this paper highlights a few difficulties of implementing NGPGT in terms of operational, communication, energy management, and economic points of view, which may enable further research scopes for the researchers. [ABSTRACT FROM AUTHOR]

The LLC resonant converter used in high-power situations suffers from the problems of high conduction loss and current stress, which can be solved using input-parallel output-parallel (IPOP)-connected converter modules. However, this leads to a multiple increase in the number of magnetic components, which reduces power density. Magnetic integration technology is an effective way to reduce the volume of converters. Currently, the magnetic integrated transformer based on EE-type cores is widely used to realize miniaturization, and it uses leakage inductance instead of resonant inductance to improve power density. However, leakage inductance is difficult to control, and the external radiated magnetic field will produce serious eddy current loss and electromagnetic interference. This article proposes a novel double B-type magnetic integrated transformer, which can integrate the magnetic components of two LLC resonant converters simultaneously and where the resonant inductances are wound independently. The structure contains four low reluctance branches, which are used as the cores of the transformer and the resonant inductance. The decoupling integration method, which integrates the four components into a single core, has been used to increase core utilization and improve power density. On this basis, the transformer's high- and low-voltage windings are cross-arranged to reduce the magnetic field intensity in space, further decreasing the loss and electromagnetic interference. Compared with the EE-type magnetic integrated transformer, the volume of the proposed structure is reduced by 5.9%. A 400W experimental prototype is built, and the results verify the validity of the design. [ABSTRACT FROM AUTHOR]

To detect the oil leakage problem of the power transformer in time and guarantee the normal operation of the power system, this paper proposes a lightweight-attention YOLOv8s, called LA_YOLOv8s, for oil leakage detection in the power transformer. First, the idea of lightweight structure + attention is used to redesign the backbone and neck of the original network respectively, which greatly reduces the number of parameters and calculation amount, but also retains strong feature extraction ability. Second, the dynamic head frame Dyhead is used to improve the representation ability of the detection head. Finally, the Wise-IoU bounding box loss function with a dynamic non-monotonic focusing mechanism is used to further improve the overall performance of the model by balancing the learning of high and low-quality samples. Using a self-constructed outdoor power transformer oil leakage dataset for training and testing, the results show that compared with the original model, the attention of LA_YOLOv8s becomes focused, enabling the network to detect oil leaks in power transformers better. LA_YOLOv8s greatly reduces the number of parameters and computation, while maintaining the same detection performance as the original network, which is conducive to engineering deployment. • The backbone and neck of YOLOv8s are redesigned. • The Dyhead is introduced to enhance the detection capability of the head network. • Introduced Wise-IoU to trade off the learning between low-quality and high-quality samples better. • Parameters and computation drop by 55.9% and 36.9%, respectively, with less than 1% change in precision, recall, and mAP. • A visualization algorithm is used to preliminarily validate the reliability of the model. [ABSTRACT FROM AUTHOR]

A multi-step approach for the fast calculation of the magnetic field inside transformer tank shields, based on the 2D FEM, is presented in the paper. Due to the limitations of the 2D FEM, the proposed approach utilizes several 2D FEM models and calculates the magnetic field in multiple steps to account for the 3D geometry of the problem. In the first step, a distribution of the magnetic flux density that enters the tank shields is calculated using the quasi-3D model of the transformer. This quasi-3D model is obtained by superimposing the solution of multiple axisymmetric 2D FEM models, and assumes a considerably simplified transformer geometry. To account for the tank shield geometry that is neglected in the quasi-3D FEM model, an additional 2D FEM model with tank shields is introduced. After the distribution of the magnetic flux density that enters the tank shields is calculated, it is imposed in the final 2D FEM model with a non-linear tank shield which is used to calculate the magnetic flux density distribution inside the tank shields. The proposed approach enables a fast calculation of magnetic field distributions, both in the vertical and horizontal directions. The results of the proposed approach are compared against the 3D FEM. The relative error of the maximum magnetic flux density is under 2%, while the NRMSE of the magnetic flux density distribution within the tank shields is under 10%. The key contribution of the proposed approach is a low computation time. In the presented case study, the total computation time of the proposed approach is ~30 s, while the computation time of the 3D FEM is ~1 h. As the computation time is significantly reduced, while the accuracy is acceptable, the proposed approach can be a good alternative to the 3D FEM for design purposes. Therefore, it has industrial value. [ABSTRACT FROM AUTHOR]

To study the influence of the voltage total harmonic distortion (THDV) and its spectrum on the harmonic loss factor (FHL) and the maximum allowable load capacity ratio (Imax (pu)) for the transformer of the LHCD system, we use MATLAB software to model the LHCD system and study the winding loss and maximum allowed load capacity of the LHCD system transformer when the supply voltage source is sinusoidal and non-sinusoidal, respectively. The calculation is carried out by the method of IEEE standard C57.110 and the method of considering the skin effect. The calculation results of both methods clearly show that the THDV value of the supply voltage has a significant effect on the harmonic dissipation factor (FHL) and the maximum allowable load capacity ratio (Imax (pu)) of the transformer. And the calculation method considering the skin effect increases the maximum allowable load capacity ratio of the LHCD system transformer by about 2%. The research results have important reference value for the future retrofit design of LHCD system transformers. [ABSTRACT FROM AUTHOR]

The LLC resonant converter used in high-power situations suffers from the problems of high conduction loss and current stress, which can be solved using input-parallel output-parallel (IPOP)-connected converter modules. However, this leads to a multiple increase in the number of magnetic components, which reduces power density. Magnetic integration technology is an effective way to reduce the volume of converters. Currently, the magnetic integrated transformer based on EE-type cores is widely used to realize miniaturization, and it uses leakage inductance instead of resonant inductance to improve power density. However, leakage inductance is difficult to control, and the external radiated magnetic field will produce serious eddy current loss and electromagnetic interference. This article proposes a novel double B-type magnetic integrated transformer, which can integrate the magnetic components of two LLC resonant converters simultaneously and where the resonant inductances are wound independently. The structure contains four low reluctance branches, which are used as the cores of the transformer and the resonant inductance. The decoupling integration method, which integrates the four components into a single core, has been used to increase core utilization and improve power density. On this basis, the transformer's high- and low-voltage windings are cross-arranged to reduce the magnetic field intensity in space, further decreasing the loss and electromagnetic interference. Compared with the EE-type magnetic integrated transformer, the volume of the proposed structure is reduced by 5.9%. A 400W experimental prototype is built, and the results verify the validity of the design. [ABSTRACT FROM AUTHOR]

Power transformers play a critical role in electrical networks, making their uninterrupted functioning and durability paramount, especially in regions prone to seismic events like the Cianjur Earthquake in West Java, Indonesia in 2022. Ensuring transformers' resilience under various environmental conditions becomes imperative. This study explores innovative methodologies for transformer diagnostics, aiming to address gaps in existing practices. While conventional diagnostic techniques are widely used, this research uniquely integrates several methods to provide a comprehensive assessment. Notably, the Sweep Frequency Response Analysis (SFRA) emerges as a standout approach for detecting flaws or deformations in power transformers, offering insights beyond traditional diagnostic protocols. Our findings, juxtaposed against conventional tests such as turn ratio, insulation resistance, and breakdown voltage tests, reveal nuanced insights into transformer health. Contrary to conventional assessments indicating satisfactory performance, SFRA uncovers subtle yet significant flaws within the transformer, underscoring the novelty of our approach. Despite moderate degradation levels, our study underscores the critical need for advanced diagnostic strategies, shedding light on transformer resilience in seismic-prone regions. Such insights contribute to the evolving discourse on transformer diagnostics, guiding future maintenance practices for enhanced grid reliability. [ABSTRACT FROM AUTHOR]

Transformer oil plays a critical role in ensuring the insulation and normal operation of power transformers. However, during operation, the transformer oil would undergo oxidation and degradation, leading to the formation of acids and polar compounds that increase its acidity and degrade its performance. Hence, the recycling and regeneration of aged transformer oil is an essential practice in transformer maintenance, contributing significantly to the extension of a transformer's operational lifespan and the reduction of potential risks. Adsorption separation is a preferred method for regeneration, offering simplicity and high efficiency. This study focuses on impregnated γ‐Al2O3 as an adsorbent for acidity removal in waste transformer oil. We conducted static adsorption experiments to investigate the optimal loading amount of the active component and the best calcination temperature for acid removal. XRD technique is employed to monitor the phase changes of the carrier during the calcination process. BET analysis is used to observe variations in the specific surface area and pore volume of the adsorbent during the modification process. DTG‐TGA is utilized to assess the weight loss during the calcination process. FTIR is employed to observe changes in functional groups during modification and adsorption processes. Along with these investigations, a study is also carried out to assess the dynamic acid removal performance of the acid‐removing adsorbent loaded into a fixed bed. The findings contribute to the development of sustainable methods for regenerating transformer oil, ensuring efficient and environmentally friendly operations. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis (DGA) in transformer oil, which analyzes its gas content, is valuable for promptly detecting potential faults in oil-immersed transformers. Given the limitations of traditional transformer fault diagnostic methods, such as insufficient gas characteristic components and a high misjudgment rate for transformer faults, this study proposes a transformer fault diagnosis model based on multi-scale approximate entropy and optimized convolutional neural networks (CNNs). This study introduces an improved sparrow search algorithm (ISSA) for optimizing CNN parameters, establishing the ISSA-CNN transformer fault diagnosis model. The dissolved gas components in the transformer oil are analyzed, and the multi-scale approximate entropy of the gas content under different fault modes is calculated. The computed entropy values are then used as feature parameters for the ISSA-CNN model to derive diagnostic results. Experimental data analysis demonstrates that multi-scale approximate entropy effectively characterizes the dissolved gas components in the transformer oil, significantly improving the diagnostic efficiency. Comparative analysis with BPNN, ELM, and CNNs validates the effectiveness and superiority of the proposed ISSA-CNN diagnostic model across various evaluation metrics. [ABSTRACT FROM AUTHOR]

Because large oil-immersed transformers are enclosed by a metal shell, the on-site localization means it is difficult to achieve the accurate location of the patrol micro-robot inside a given transformer. To address this issue, a spatial ultrasonic localization method based on wavelet decomposition and PHAT-β-γ generalized cross correlation is proposed in this paper. The method is carried out with a five-element stereo ultrasonic array for the location of a transformer patrol robot. Firstly, the localization signal is decomposed into wavelet coefficients of different scales, which would realize the adaptive decomposition of the frequency of the localization signal from low frequencies to high frequencies. Then, the wavelet coefficients are denoised and reconstructed by using the semi-soft threshold function. Second, a modified phase transform-beta-gamma (PHAT-β-γ) method is used to calculate the exact time delay between different sensors by increasing the weights of the PHAT weighting function and introducing a correlation function. Finally, by using the proposed method, the accurate localization of the transformer patrol micro-robot is achieved with a five-element stereo ultrasonic array. The simulation and test results show that inside a transformer experimental oil tank (120 cm × 100 cm × 100 cm, L × W × H), the relative error of transformer patrol micro-robot spatial localization is within 4.1%, and the maximum localization error is less than 3 cm, which meets the requirement of engineering localization. [ABSTRACT FROM AUTHOR]

Owing to the diverse connection configurations of dual active bridge converters, a multiplicity of low-voltage DC port structures are anticipated to emerge in the independent DC bus structure multiport power electronic transformer (IDBS-MPET). An inadequate low-voltage DC port structure exacerbates the power imbalance in IDBS-MPET, presenting a risk of overmodulation even when transmitting relatively low levels of power. To overcome this limitation, a design scheme of IDBS-MPET topology based on the maximum power transmission capability of the low-voltage DC ports is proposed in this paper. Three topology design rules are derived from the maximum power transmission capability results of more than 80 typical IDBS-MPET topologies. The symmetrical triple cross-phase connection structure, the symmetrical double cross-phase connection structure and the single-phase connection structure are sequentially identified as the three most optimal structures of low-voltage DC ports. By employing the proposed design methodology, each low-voltage DC port achieves its maximum power transfer capability relative to other configurations. The effectiveness of the proposed design scheme is validated by an optimal designed IDBS-MPET topology with six low-voltage DC ports. [ABSTRACT FROM AUTHOR]

Summary: To accurately and efficiently calculate the frequency response of the lumped parameter equivalent circuit model (LPECM) of a transformer winding is the basis for studying the simulation method of the frequency response analysis (FRA). In this paper, two different cut‐set matrix (CSM) methods, including fundamental cut‐set (FCS) and independent cut‐set (ICS) matrix methods, are proposed to calculate the FRA curves of the LPECM of a three‐winding power transformer. First, based on graph theory and electrical circuit laws, the basic CSM equation is deduced. Second, four different FCS matrix selection methods of the LPECM are introduced and the best FCS matrix is determined by deeply analyzing their characteristics. Then, an ICS matrix method is proposed to obtain the ICS of the LPECM. Furthermore, FCS and ICS are taken into the CSM equation to calculate the FRA results of the LPECM, respectively. Finally, the accuracy of the proposed methods is validated by Multisim software and the performance of these two methods is compared with node voltage matrix (NVM) method by analyzing the calculation time. The results show that FCS and ICS matrix methods both can be used to accurately obtain the FRA results of the LPECM of the three‐winding power transformer, but ICS is more simple than FCS matrix method due to avoiding the procedure for the selection of the tree. In addition, since ICS matrix method has the shortest running time among these methods, it can be used to improve the calculation efficiency of the FRA curves of the LPECM of a three‐winding power transformer. [ABSTRACT FROM AUTHOR]

Summary: Traditional multiple‐output AC‐DC power supply with power factor correction (PFC) function usually employs a two‐stage cascaded topology that is composed of prestage PFC converter and N independent poststage DC‐DC converters to achieve PFC and voltage or current regulation of N outputs. Hence, the conventional two‐stage cascaded multiple‐output AC‐DC power supply needs N + 1 magnetic components as power inductors or transformers and N + 1 controllers, which increases the cost and size of the multioutput converters. The conventional multiple‐string AC‐DC LED driving circuits usually employ a two‐stage topology to realize PFC and independent current regulation of each LED string. To address the issue of complexity, high cost, and large size of the two‐stage multistring LED driving circuit, a single‐stage triple‐output Cuk PFC converter is proposed by cascading boost converter and single‐inductor triple‐output buck converter with sharing main power switch in this paper. The topology operation principle, control strategy, design consideration, and operation characteristics of the proposed circuit are investigated in detail. With the switch sharing and inductor multiplexing technologies at the same time, the proposed converter utilizes only single‐stage converter to achieve PFC function, independent regulation of triple‐channel output current, and flicker‐free with low twice line frequency LED current ripple. The proposed converter is verified by a 50‐W experimental prototype of LED driver for triple string LEDs. The proposed converter only uses two power inductors and a set of controller to achieve greater than 0.945 PF, up to 90.5% efficiency, low LED current ripple, and independent current regulation for triple LED strings with different forward voltage, which may significantly reduce the cost and size of the traditional AC‐DC multistring LED driving circuit. [ABSTRACT FROM AUTHOR]

The lumped element network model has been proven to be an efficient tool for the interpretation of transformer Frequency Response Analysis. However, it is challenging to obtain parameters of the model if transformer design information is unavailable. In such a case, optimisation algorithms can be used for gray‐box model parameter estimation. A methodology is developed by the authors to establish a transformer network model without winding design data, and instead, end‐to‐end open circuit Frequency Response and other terminal test results are utilised; and Genetic Algorithm is applied to approximate the unknown parameters of the model. The modelling approach developed is independent of the unit number of the network model and therefore guarantees the accuracy of optimisation. Furthermore, it can deal with transformers with a complicated winding structure such as interleaved disc type winding. Two single windings, helical and interleaved disc type, and a single‐phase 144/13 kV 60 MVA transformer are used to demonstrate the method. FRA spectra produced by the best estimated gray‐box model and the corresponding white‐box model are compared. The main features in amplitude and phase spectra are well matched, with low values of Relative Standard Deviation, for both single windings and transformer. Estimated electrical parameters show a high consistency with reference values, which are calculated based on winding design data. This validates the methodology and gives confidence to apply grey‐box modelling for FRA interpretation. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motors (PMSM) are very popular in the industrial market. Recently, robots are installed to compensate for the shortage of labor. Due to the increase in motors, the number of power lines and position sensor cable wiring are increased; this increase in complicated wiring is a problem. As a method to solve these problems, power superimposition technology in the power line of a PMSM is expected to reduce the distribution cables. In this paper, the power superposition technology method, and circuit equations of mounting transformers in power lines are presented and are then verified by experiment, simulation, and linear analysis. The circuit equation is proposed as a three‐phase circuit equation, which is transformed into a dq‐circuit equation by a transformation matrix. The characteristics of the electric angular frequency versus the dq‐axis voltage demonstrated satisfactory results, with errors within 1% in linear analysis and simulation. The linear analysis and experiment are shown to be similar, although there are some errors. The validity of the circuit equations could be demonstrated by the verification results. [ABSTRACT FROM AUTHOR]

The analysis of gases dissolved in oil is the main technique used to predict possible failures in power transformers. Currently, there are some assessment algorithms for the predictive analysis, but all of them have in common the fact that they are based on the classical gas formation theory. This work aims to analyze the use of decision trees (DTs) for the analysis of gases dissolved in mineral oil, also establishing a relationship associated with decision making during the formation of trees and the formation of gases. In this way, some important aspects involving the insulating mineral oil can be better understood and quantified. For this purpose, one dataset containing 201 samples is used in the study. The results provided by the tree are arranged in a Cartesian plane, and the gases are analyzed based on the ratios used by Doernenburg. DT is discussed in detail to validate the algorithm performance based on the classical gas formation theory. [ABSTRACT FROM AUTHOR]

Transformer fault diagnosis is very important for the stable operation of transformers, but it is still difficult to recognize transformer faults. The main obstacle is attributed to the lack of labeled data for transformer faults. Due to the limited data of transformer faults, a great deal of unlabeled data in real life is not difficult to obtain and contains a lot of effective information. Semi‐supervised self‐training can significantly enhance the classification accuracy and performance of the base classifier by applying unlabeled data. However, one of the reasons for the degradation of semi‐supervised self‐training is the lack of initial labeled data to train the initial classifier. In this paper, we propose an improved semi‐supervised SMOTE (SS‐SMOTE), which uses the potential information contained in unlabeled data to generate synthetic samples, and then reasonably increases the labeled data set. Meanwhile, we combine SS‐SMOTE with self‐training into an algorithm framework. Finally, a series of experiments are carried out to evaluate the performance of the proposed algorithm, and compared with other existing state‐of‐the‐art semi‐supervised algorithms. The results show that the performance improvement of the classifier using the semi‐supervised improved SMOTE algorithm framework is effective, and has a good effect on solving the problem of small sample classification. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The enhancement of the thermal conductivity of dielectric oils has a positive effect on the performance of electrical equipment that uses these oils as a cooling medium. Nanofluids (NFs) have inspired high-voltage engineers to use them as alternative fluids in power transformers due to their impressive heat transfer and insulation compared to traditional dielectric oils. The present study is a numerical simulation by COMSOL Multiphysics of the thermal conductivity of NFs based on dielectric oils used in power transformers, to identify the effect of temperature, the concentration of nanoparticles (NPs), type of insulating fluid and NPs on thermal conductivity. The NFs were modeled inside a cube using the finite element method (FEM) by applying a temperature gradient. Several types of NPs were used (SiC, ZnO, TiO2, and Al2O3) in addition to several volume concentrations (0%, 0.001%, 0.002%, 0.01%, and 0.02%). The results showed a significant improvement in the thermal conductivity of the NFs with increasing concentration since the best results were recorded at an estimated volume concentration of 0.02%, while the lowest results were obtained for samples using a volume concentration estimated at 0.001%. The base fluid (BF) type and NPs play a dominant role in the thermal performance of the NFs, as the vegetable oil-based nanofluid provided the highest thermal conductivity values and silicon carbides (SiC) was the best NPs used in this study. However, a decrease in thermal transfer capacities was observed for all samples with increasing temperature. [ABSTRACT FROM AUTHOR]

The need for a constant electricity supply is at an alarming rate especially in the 21st century due to the high rate of increase in industrialization across the globe. Conventional protection schemes such as differential relays, Buchholz relay, and other techniques such as genetic algorithms and artificial neural networks, do not match the precision and reliability needed for transformer fault indentification, due to their complexity in computation, tedious training system, time consumption, and need the of human experts. The method proposed in this research is the use of a fuzzy inference system in detecting potential faults in power system transformers. The faults in the transformer were observed and analyzed using a simulation system of MATLAB/Simulink software. The suggested approach ensures swift identification of faults as it relies on if-then rules and only uses current and voltage measurements with 100% independence toward the power flow direction, making it highly reliable and simple to implement compared to other techniques for transformer fault identification. [ABSTRACT FROM AUTHOR]

As an important hub equipment in the power system, the power transformer has increasingly higher requirements for its reliability and safety with the rapid development of the power industry. However, the vibrations and noise generated during its operation not only cause a series of issues such as loose windings, core wear, and fatigue of fasteners, but also lead to anxiety and unease among nearby residents, severely affecting their physical and mental health. Therefore, the generation mechanism and control measures of vibration and noise in power transformers have attracted extensive attention from scholars. The magnetostrictive effect of silicon steel sheets is one of the important factors in the causes of transformer vibration and noise. This paper focuses on the magnetostrictive effect of silicon steel sheets. Firstly, the micro mechanism of magnetostriction is introduced. Secondly, based on current research status, the vibration and noise mechanism caused by magnetostriction are summarized, and the influencing factors, measurement, modeling, and noise reduction methods based on magnetostriction are summarized. Finally, based on the current research status and future development trends, prospects are proposed, aiming to provide references for subsequent research on transformer vibration and noise. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SVM) to more complex approaches like convolutional neural networks (CNN). Multiple case studies are considered, each with different attributes, including sensor position, frequency content of the PD signal, and size of the transformer tank. The paper focuses on predicting the PD location in three-dimensional space using single-sensor electric field measurements. Various aspects of each method are analyzed, such as the input signal, core methodology, correlation coefficient between the predicted location and the actual location, and root mean square error (RMSE). These features are discussed and compared across the different methods. The results indicate that the CNN model exhibits superior performance in terms of location accuracy among the methods considered.

Abstract Power outages can cause harm to both society and individual health. Among the crucial elements of power systems, power transformers play a vital role, and their failures can lead to incidents and disruptions. According to statistics, almost 50% of the explosions and fires in the power transformer are caused by oil‐impregnated paper (OIP) bushing failures. Moreover, moisture is a crucial factor that affects the health of OIP bushings, and assessing the moisture level in OIP bushings is vital to guarantee the safety of power systems. While the frequency‐domain dielectric spectroscopy (FDS) method has been widely used to assess the moisture content of OIP bushings on‐site, it has the disadvantage of requiring a long measurement time. Therefore, a novel technique was proposed in this study that involves measuring the OIP bushing dissipation factor (tanδ) at 1 Hz to estimate the moisture content in a significantly shorter time. Five samples of OIP with various moisture levels have been selected, and an equation based on the OIPs' value of tanδ at 1 Hz was proposed to assess the moisture level of OIP bushings. Furthermore, the validity of the proposed method was successfully verified, proving its precision and time efficiency.

Abstract Existing interturn fault detecting methods rely on winding impedance, winding current, and dissolved gases. They are effective only when the insulation is severely damaged. This paper proposes a novel detection method based on fusion analysis of electrothermal characteristics including winding currents, temperatures of four areas on the tank wall, top oil and ambient temperatures, which can identify the interturn fault at an early stage. When an incipient interturn fault occurs, the heat generated by the faulty turns is transferred to the oil and tank wall, leading to an increase in top oil and tank wall temperatures. Thus, the incipient fault can be detected by analysing these electrothermal characteristic parameters. Borrowing the idea of digital twin (DT), this method establishes a high‐fidelity simulation model to simulate the transformer electrothermal characteristics under different operating conditions. Afterward, an intelligent neural network is adopted to extract the quantitative relationship between the eight feature attributions and fault conditions. Finally, this neural network is utilized to detect the incipient interturn fault for the transformer entity. Case studies are conducted on a 100 kVA transformer with oil natural air natural (ONAN) cooling mode. The detection accuracy is improved by 68.5% compared to the winding current‐based method.

Abstract Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation.

Abstract The lumped element network model has been proven to be an efficient tool for the interpretation of transformer Frequency Response Analysis. However, it is challenging to obtain parameters of the model if transformer design information is unavailable. In such a case, optimisation algorithms can be used for gray‐box model parameter estimation. A methodology is developed by the authors to establish a transformer network model without winding design data, and instead, end‐to‐end open circuit Frequency Response and other terminal test results are utilised; and Genetic Algorithm is applied to approximate the unknown parameters of the model. The modelling approach developed is independent of the unit number of the network model and therefore guarantees the accuracy of optimisation. Furthermore, it can deal with transformers with a complicated winding structure such as interleaved disc type winding. Two single windings, helical and interleaved disc type, and a single‐phase 144/13 kV 60 MVA transformer are used to demonstrate the method. FRA spectra produced by the best estimated gray‐box model and the corresponding white‐box model are compared. The main features in amplitude and phase spectra are well matched, with low values of Relative Standard Deviation, for both single windings and transformer. Estimated electrical parameters show a high consistency with reference values, which are calculated based on winding design data. This validates the methodology and gives confidence to apply grey‐box modelling for FRA interpretation.