• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.2
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• pp.70-74
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• 2018

Social infrastructure such as production, transportation, gas, and electrical facilities would be degraded according to time and those facilities might need more maintenances, repairing, and management as time goes by. Especially, in the case of a power transformer, it is important to diagnose the transformer in order to avoid malfunction and failure because they could cause enormous damage. The economic as well as technical aspects of a transformer management must be considered while it is operated. Therefore, the concept of asset management should be applied as an advanced method of transformer management. Asset management refers to a series of processes to make a plan of maintenance and conservation of a power transformer considering the soundness, investment cost, and importance of equipment. It is important to apply the asset management considering calculation of a lifetime loss. In this paper, the lifetime loss calculation method of asset management for a power transformer is suggested.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.30 no.1
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• pp.96-101
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• 2016

In this paper, We investigate the number of 795 transformer in the private electrical facilities and analyze the annual load factor. The results show that the annual load factor of transformer is 20.16% in manufacturing industry, education services(school) is 9.59%, retail and wholesale services is 19.68%, resort and leisure industry is 10.93%, office building is 13.10%, and apartment houses is 14.69%. Education services, resort and leisure industry are being operated with a very low annual load factor. The relatively small capacity of less than 500kVA transformer also been analyzed that is being operated with a low load factor. Therefore, In order to minimize the power loss of the transformer, it is advisable to complement the Transformer Efficiency Management system to be designed the efficiency is good transformer when the load is low. Analysis results will be used as the basis for the provision of transformer efficiency management system and be used High-efficiency transformers promotion system.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.1
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• pp.9-14
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• 2022

Social infrastructure facilities such as production, transportation, gas and electricity facilities may experience poor performance depending on time, load, temperature, etc. and may require maintenance, repair and management as they are used. In particular, in the case of transformers, the process of managing them for the purpose of preventing them from failing is necessary because a failure can cause enormous social damage. The management of transformers should consider both technical and economic aspects and strategic aspects at the same time. Thus, it applies the Asset Management concept, which is widely used in the financial industry as an advanced method of transformer management techniques worldwide. In this paper, the operation and power outage data were secured for the asset management of the transformer for distribution, and the asset status was analyzed. Analysis of asset status using actual operation and power outage data is essential for assessing the statistical life and failure rate of the facility. Through this paper, the status of transformer assets for arbitrary A group distribution was analyzed, and the end of life and replacement life were calculated.

• The Transactions of the Korean Institute of Power Electronics
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• v.23 no.4
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• pp.247-255
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• 2018

This paper proposes a transformer design of a direct cell-to-cell active cell balancing circuit with a multi-winding transformer for battery management system (BMS) applications. The coupling coefficient of the multi-winding transformer and the output capacitance of MOSFETs significantly affect the balancing current transfer efficiency of the cell balancing operation. During the operation, the multi-winding transformer stores the energy charged in a specific source cell and subsequently transfers this energy to the target cell. However, the leakage inductance of the multi-winding transformer and the output capacitance of the MOSFET induce an abnormal energy transfer to the non-target cells, thereby degrading the transfer efficiency of the balancing current in each cell balancing operation. The impacts of the balancing current transfer efficiency deterioration are analyzed and a transformer design methodology that considers the coupling coefficient is proposed to enhance the transfer efficiency of the balancing current. The efficiency improvements resulting from the selection of an appropriate coupling coefficient are verified by conducting a simulation and experiment with a 1 W prototype cell balancing circuit.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.17-25
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• 2023

In this paper, we propose a model that can perform human pose estimation through a MobileViT-based model with fewer parameters and faster estimation. The based model demonstrates lightweight performance through a structure that combines features of convolutional neural networks with features of Vision Transformer. Transformer, which is a major mechanism in this study, has become more influential as its based models perform better than convolutional neural network-based models in the field of computer vision. Similarly, in the field of human pose estimation, Vision Transformer-based ViTPose maintains the best performance in all human pose estimation benchmarks such as COCO, OCHuman, and MPII. However, because Vision Transformer has a heavy model structure with a large number of parameters and requires a relatively large amount of computation, it costs users a lot to train the model. Accordingly, the based model overcame the insufficient Inductive Bias calculation problem, which requires a large amount of computation by Vision Transformer, with Local Representation through a convolutional neural network structure. Finally, the proposed model obtained a mean average precision of 0.694 on the MS COCO benchmark with 3.28 GFLOPs and 9.72 million parameters, which are 1/5 and 1/9 the number compared to ViTPose, respectively.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.115-127
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• 2024

This research aimed to construct models with various structures based on the Transformer module and to perform land cover classification, thereby examining the applicability of the Transformer module. For the classification of land cover, the Unet model, which has a CNN structure, was selected as the base model, and a total of four deep learning models were constructed by combining both the encoder and decoder parts with the Transformer module. During the training process of the deep learning models, the training was repeated 10 times under the same conditions to evaluate the generalization performance. The evaluation of the classification accuracy of the deep learning models showed that the Model D, which utilized the Transformer module in both the encoder and decoder structures, achieved the highest overall accuracy with an average of approximately 89.4% and a Kappa coefficient average of about 73.2%. In terms of training time, models based on CNN were the most efficient. however, the use of Transformer-based models resulted in an average improvement of 0.5% in classification accuracy based on the Kappa coefficient. It is considered necessary to refine the model by considering various variables such as adjusting hyperparameters and image patch sizes during the integration process with CNN models. A common issue identified in all models during the land cover classification process was the difficulty in detecting small-scale objects. To improve this misclassification phenomenon, it is deemed necessary to explore the use of high-resolution input data and integrate multidimensional data that includes terrain and texture information.

• Journal of Power Electronics
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• v.8 no.3
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• pp.217-227
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• 2008

The electric power subsystems (EPS) of most remote sensing satellites consist of a solar array as a source of energy, a storage battery, a power management and control (PMC) unit and a charge equalization unit (CEU) for the storage battery. The PMC and CEU use high frequency transformers in their power modules. This paper presents a design, implementation and testing results of a high frequency transformer for the EPS of satellite applications. Two approaches are used in the design process of the transformer based on the pre-determined transformer specifications. The transformer is designed based on an ETD 29 ferrite core. The implemented transformer consists of one center-tapped primary coil with eleven center-tapped secondary coils. The offline calculation results and measured values of R, L for transformer coils are convergence. A test circuit for measuring the transformer parameters like voltage, current and B-H hysteresis was implemented and applied. The test results confirm that the voltage waveforms of both primary and secondary coils were as desired. No overlapping occurred between the control signal and the transformer, which was not saturated during testing even during a short circuit test of the secondary channels. The dynamic B-H loop characteristics of the used transformer cores were measured. The sample test results are given in this paper.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2134-2143
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• 2018

This work introduces a fault diagnosis method for transformer based on self-powered radio frequency identification (RFID) sensor tag and improved Hilbert-Huang transform (HHT). Consisted by RFID tag chip, power management circuit, MCU and accelerometer, the developed RFID sensor tag is used to acquire and wirelessly transmit the vibration signal. A customized power management including solar panel, low dropout (LDO) voltage regulator, supercapacitor and corresponding charging circuit is presented to guarantee constant DC power for the sensor tag. An improved band restricted empirical mode decomposition (BREMD) which is optimized by quantum-behaved particle swarm optimization (QPSO) algorithm is proposed to deal with the raw vibration signal. Compared with traditional methods, this improved BREMD method shows great superiority in reducing mode aliasing. Then, a promising fault diagnosis approach on the basis of Hilbert marginal spectrum variations is brought up. The measured results show that the presented power management circuit can generate 2.5V DC voltage for the rest of the sensor tag. The developed sensor tag can achieve a reliable communication distance of 17.8m in the test environment. Furthermore, the measurement results indicate the promising performance of fault diagnosis for transformer.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.3
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• pp.123-128
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• 2017

Electric utilities has been considered the necessity to introduce AM(asset management) of electric power facilities in order to reduce maintenance cost of existing facilities and to maximize profit. In order to make decisions in terms of repairs and replacements for power transformers, not only measuring by counting parts and labor costs, but comprehensive comparison including reliability and cost is needed. Therefore, this study is modeling input cost for power transformer during its entire life and also the life cycle cost (LCC) technique is applied. In particular, this paper presents an application of heuristic harmony search(HS) optimization algorithm to the convergence and the validity of economic life assessment of power transformer from LCC technique. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. The effectiveness of the proposed identification method has been demonstrated through an economic life assessment simulation of power transformer using HS optimization algorithm.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.05c
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• pp.15-17
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• 2002

Leakage inductance and temperature rise are two of the more impotent problems facing the magnetic core technology of today's high frequency transformers. Excessive leakage inductance increases the stress on the switching transistors and limits the duty-cycle, and excessive temperature rise can lead the design limitation of high frequency transformer with high current. The flat transformer technology provides a very good solution to the problems of leakage inductance and thermal management for high frequency power. The critical magnetic components and windings are optimized and packaged within a completely assembled module. The turns ratio in a flat transformer is determined as the product of the number of elements or modules times the number of primary turns. The leakage inductance increase proportionately to the number of elements, but since it is reduced as the square of the turns, the net reduction can be very significant. The flat transformer modules use cores which have no gap. This eliminates fringing fluxes and stray flux outside of the core. The secondary windings are formed of flat metal and are bonded to the inside surface of the core. The secondary winding thus surrounds the primary winding, so nearly all of the flux is captured.