• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2000년도 춘계학술대회 논문집 유기절연재료 방전 플라즈마
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• pp.29-32
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• 2000

The partial discharge testing is widely used in insulation property measurement because it gives low stress to high voltage equipment which is undertaken tests. Therefore it is very useful method compare to previous destructive methods and effective diagnosis method in power transformer that requires on-line & on-site diagnosis. But partial discharges have very complex characteristics of discharge pattern so it is required continuous research to development of precise analysis method. In recent, the study of partial discharge is carrying out discover of initial defect of power equipment through condition diagnosis and system development of degradation diagnosis using HFPD(High Frequency Partial Discharge) detection. In this study, simulated transformer is manufactured and PD/HFPD occurred from transformer is measured with broad band antenna in real time, the degradation grade of transformer is analyzed through produced patterns in simulated transformer according to applied voltages.

• 조명전기설비학회논문지
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• 제23권2호
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• pp.62-69
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• 2009

본 논문에서는 전력용 변압기의 고장진단을 위해 써포트 백터머신에 기반을 둔 고장진단 알고리즘을 제안한다. 제안된 기법은 데이터 취득부, 정상/고장판별부, 고장원인판별부로 구성된다. 제안한 고장진단과정을 보면, 데이터 취득부에서는 변압기에서 가스성분을 취득한다. 정상/고장 판별부에서는 취득된 가스성분들을 KEPCO 규정과 비교하여 정상/고장 여부를 판단한다. 고장원인 판별부에서는 입력 데이터가 고장으로 판정이 난 경우에 다중-클래스 써포트 백터머신에 의해 고장원인을 판정한다. 제안된 방법은 사례연구를 통해 우수성을 입증하였다.

• Journal of Electrical Engineering and Technology
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• 제9권2호
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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.

• KEPCO Journal on Electric Power and Energy
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• 제6권4호
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• pp.447-454
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• 2020

Since SF₆ gas was discovered in the early 1900s, it has been widely used as an insulation material for electrical equipment. While various indicators have been developed to diagnose oil-immersed transformers, there are still insufficient indicators for the diagnosis of gas-insulated transformers. When necessary, chemical diagnostic methods can be used for gas-insulated transformers. However, the field suitability and accuracy of those methods for transformer diagnosis have not been verified. In addition, since various types of decomposition gases are generated therein, it is also necessary to establish appropriate analysis methods to cover the variety of gases. In this study, a gas-insulated transformer was diagnosed through the analysis of decomposition gases. Reliability assessments of both simple analysis methods suitable for on-site tests and precise analysis methods for laboratory level tests were performed. Using these methods, a gas analysis was performed for the internal decomposition gases of a 154 kV transformer in operation. In addition, simulated discharge and thermal fault experiments were demonstrated. Each major decomposition gas generation characteristics was identified. The results showed that an approximate diagnosis of the inside of a gas-insulated transformer is possible by analyzing SO₂, SOF₂, and CO using simple analysis methods on-site. In addition, since there are differences in the types of decomposition gas generation patterns with various solid materials of the internal transformer, a detailed examination should be performed by using precise analysis methods in the laboratory.

• Journal of Electrical Engineering and Technology
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• 제13권5호
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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.

• 전기학회논문지P
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• 제65권3호
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• pp.188-193
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• 2016

It has been proven that the dissolved gas analysis (DGA) is the most effective and convenient method to diagnose the transformers. The DGA is a simple, inexpensive, and non intrusive technique. Among the various diagnosis methods, IEC 60599 has been widely used in transformer in service. But this method cannot offer accurate diagnosis for all the faults. This paper proposes a fault diagnosis method of oil-filled power transformers using DGA and Intelligent Probability Model. To demonstrate the validity of the proposed method, experiment is performed and its results are illustrated.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제54권12호
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• pp.553-557
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• 2005

This paper describes the application results of an ultrasonic detection system for the power transformer. The ultrasonic detection system with 6 sensors was applied to detect partial discharge in a 154kV transformer with a dangerous levels of $C_{2}H_{2},\;C_{2}H_4$ and $CH_{4}$ gases. The ultrasonic detection tests were carried out 2 times, respectively, to confirm the existence and location of the partial discharge in the transformer. As a result of internal inspection, the arc trace between the pressure ring and core due to the partial discharge was found at the estimated position based on the amplitude and arriving time of the ultrasonic signals. Therefore, it was verified that the ultrasonic detection system is effective as a preventive diagnosis method for the power transformer. Also, the reliability of the ultrasonic detection system in detecting partial discharges in the transformer was also confirmed. It is expected, therefore, that the ultrasonic detection system will have beneficial effects on applications and verifications in detecting partial discharges for the power transformer.

• Transactions on Electrical and Electronic Materials
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• 제7권6호
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• pp.324-329
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• 2006

As the average usage period of transformers increases, it is becoming increasingly necessary to know the internal condition of transformers. It is therefore critically important to establish monitoring and diagnostic techniques that can perform transformer condition assessment. Frequency response analysis, generally known as FRA, is one of the technologies to diagnose transformers. Using case studies, this paper presents the effectiveness of FRA as measurements for detecting transformer failures. This paper introduces the fact that FRA waveforms have useful information about diagnosis of failure on core earths and winding shield, and that the condition outside transformers can affect frequency response characteristics.

• 전기학회논문지P
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• 제66권4호
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• pp.194-199
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• 2017

Power transformers are an important factor for power transmission and cause fatal losses if faults occur. Various diagnostic methods have been applied to predict the failure and to identify the cause of the failure. Typical diagnostic methods include the IEC diagnostic method, the Duval diagnostic method, the Rogers diagnostic method, and the Doernenburg diagnostic method using the ratio of the main gas. However, each diagnostic method has a disadvantage in that it can't diagnose the state of the power transformer unless the gas ratio is within the defined range. In order to solve these problems, we propose a diagnosis method using ELM based intelligent algorithm and fuzzy membership function. The final diagnosis is performed by multiplying the result of diagnosis in the four diagnostic methods (IEC, Duval, Rogers, and Doernenburg) by the fuzzy membership values. To show its effectiveness, the proposed fault diagnostic system has been intensively tested with the dissolved gases acquired from various power transformers.

• KEPCO Journal on Electric Power and Energy
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• 제8권2호
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• pp.119-126
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• 2022

A growing number of gas-insulated transformers in underground power substations in urban areas are approaching 20 years of operation, the time when failures begin to occur. It is thus essential to prevent failure through accurate condition diagnosis of the given facility. Various solid insulation materials exist inside of the transformers, and the generated decomposition gas may differ for each gas-insulated equipment. In this study, a simulation system was designed to analyze the deterioration characteristics of SF₆ decomposition gas and insulation materials under the conditions of partial discharge and thermal fault for diagnosis of gas-insulated transformers. Degradation characteristics of the insulation materials was determined using an automatic viscometer and FT-IR. The analysis results showed that the pattern of decomposition gas generation under partial discharge and thermal fault was different. In particular, acetaldehyde was detected under a thermal fault in all types of insulation, but not under partial discharge or an arc condition. In addition, in the case of insulation materials, deterioration of the insulation itself rapidly progressed as the experimental temperature increased. It was confirmed that it was possible to diagnose the internal discharge or thermal fault occurrence of the transformer through the ratio and type of decomposition gas generated in the gas-insulated transformer.