• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.304-309
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• 1991

We introduce the trend, of several countries, to the gas insulated transformer recently coming into the spot light by the advantages of less weight, GIS-lization, low noise level and easiness to decrease environmental pollutions, and the insulating gases. the cooling media and the insulating materials used for transformer. Hereinafter, additively the design of proto type $SF_6$ gas insulated transformer (insulation, cooling), which was developed by us(HICO) from 1987. 2 through 1990. 3, the manufacturing processes(leak protection, pressure, drying of main body), the accessories, the protective system, the cooling system, the method and the results of test and the evaluation of economics compared with conventional oil-filled transformer are represented.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.1
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• pp.31-36
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• 2000

To enhance the fault diagnosis ability for the dissolved gas analysis(DGA) of the power transformer, this paper proposes a learning vector quantization(LVQ) for the incipient fault recognition. LVQ is suitable expecially for pattern recognition such as fault diagnosis of power transformer using DGA because it improves the performance of Kohonen neural network by placing emphasis on the classification around the decision boundary. The capabilities of the proposed diagnosis system for the transformer DGA decision support have been extensively verified through the practical test data collected from Korea Electrical Power Corporation.

• Proceedings of the KIEE Conference
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• 2001.07c
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• pp.1676-1678
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• 2001

In general, power demand is on an increasing trend as industries have made rapid strides. Power transformer is the most important equipment in substation for this reason. Transformer trobles go with blackout, expensive repair costs and huge economic losses. Therefore it is important to find the quick detection of incipient fault for the least losses. There have been gas, partial discharge, temperature, OLTC, fan and pump diagnosis for preventive techniques by present. Specially gas analysis has been adapted for a long time and proved as confident method. In this paper, we analysed the fault causes of used power transformer. The insulation faults was occupied 40% of inquired 152 faults from 1991 to 2000. This study presents the developed algorithm and expert system for finding abnormal status within transformer. We used the Element Expert tool developed Neuron DATA Inc.

• Electrical & Electronic Materials
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• v.8 no.2
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• pp.217-223
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• 1995

In this paper, we investigated physical properties and electrical characteristics of the fluorocarbon that used as coolants for large power gas-insulated transformer. Volume resistivity of the fluorocarbon was .rho.=1.87*10$^{15}$ [.ohm.cm] at 1 atm, 27.deg. C. Dielectric constant was 1.86 and decreases as temperature increase. The breakdown voltage at 1 atm was higher than that of transformer oil. The breakdown voltage of fluorocarbon vapor was about 18kV when pressure in a test chamber increases over lkg/cm$^{2}$. When fluorocarbon was mixed with SF$_{6}$ gas, breakdown voltage of the mixed was higher than that of fluorocarbon. Then fluorocarbon leads to increase over 4kg/cm$^{2}$ in pressure as temperature increase. Therefore, when a gas-insulated transformer is manufactured, the design must be taken into consideration a high-pressure.

• Journal of Sensor Science and Technology
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• v.32 no.1
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• pp.55-61
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• 2023

The objective of this study is to determine the concentrations of mixed gases by establishing a diagnosis method of a transformer using tunable-wavelength optical infrared sensors. Absorption of infrared light by methane, acetylene, and ethylene gases injected is measured from the outputs of the infrared sensors. Regression analysis equations of the gas concentrations are acquired from their respective measured absorption. The obtained concentrations are as follows: -3-9 % errors above 600 ppm(methane), 3 % errors above 1200 ppm(acetylene), and 10 % errors above 500 ppm(ethylene). The concentration inference equations obtained using the individual gases are applicable when the absorption wavelength bands do not overlap. The results of the fault analysis of a transformer using the Duval triangle method and the tunable infrared gas sensors are as follows: temperature faults with -1-1% errors and energy faults with -7-7 % errors.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.615-621
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• 2014

Power transformer is one of the most important equipments in electrical power system. The detection of certain gases generated in transformer is the first indication of a malfunction that may lead to failure if not detected. Dissolved gas analysis (DGA) of transformer oil has been one of the most reliable techniques to detect the incipient faults. Many conventional DGA methods have been developed to interpret DGA results obtained from gas chromatography. Although these methods are widely used in the world, they sometimes fail to diagnose, especially when DGA results falls outside conventional method codes or when more than one fault exist in transformer. To overcome these limitations, fuzzy inference system (FIS) is proposed. 250 different cases are used to test the accuracy of various DGA methods in interpreting the transformer condition.

• KIEE International Transactions on Electrophysics and Applications
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• v.3C no.4
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• pp.130-135
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• 2003

This study describes neural network based dissolved gas analysis using composition patterns of gas concentrations for transformer fault diagnosis. DGA samples were gathered from related literatures and classified into six types of faults and then a neural network was trained using the DGA samples. Diagnosis tests were performed by the trained neural network with DGA samples of serviced transformers, fault causes of which were identified by actual inspection. Diagnosis results by the neural network were in good agreement with actual faults.

• Proceedings of the KIEE Conference
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• 1999.11d
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• pp.1019-1021
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• 1999

In this paper, we describe the design and appliance of the preventive diagnostic expert system of gas in oil for power transformer. This expert system is developed to use expert system development tool; Element Expert (NEURON DATA Inc.) Analysis is developed by four diagnose methods. In first, the standard of KEPCO is applied. It classifies the state of transformer by four level: Normal, warning, abnormal and danger. And the others are gas pattern methods, IEC code method, and Dornen & Roger Ratio method and applied later. These latter methods analyse the cause of result. Inference engine was designed with Element Expert. In last, we operate the system with sample data and we can obtain the correctly inferred result for the state of transformer.

• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1800-1802
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• 2003

This paper describes a study on gas dissolved distribution in oil for simulated transformer thermal faults. Experimental chamber was setup for simulation of transformer thermal faults or discharge in oil with or without insulation paper. The experimental results showed that dissolved gases in oil excluding the paper did not evolved upto $150^{\circ}C$. Hereafter the planned gas dissolved analysis will be continuously carried out for transformer fault conditions with or without insulation paper related to water absorption, arc and partial discharge.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1463-1464
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• 2015

This paper describes for the diagnosis insulating oil for transformers integrity assessment. The high-capacity oil transformer has several insulators are entered for the purpose of insulation, such as insulating oil, insulating paper and press board. Irradiated with a gas component dissolved in the insulating oil can analyze the status of the transformer, and can prevent a sudden accident in advance.