• Journal of Electrical Engineering and Technology
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• 제3권3호
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• pp.293-299
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• 2008

One of the problems with the fault diagnosis of transformers based on dissolved gas is the inability to match the result of the different standards of fault diagnosis with real world standards. In this paper, the results of the different standards are analyzed using fuzzy logic and then compared with the empirical test. The proposed method is based on the standards and guidelines of the International Electrotechnical Commission (IEC), the Central Electric Generating Board (CEGB), and the American Society for Testing and Material (ASTM) and its main task is to assist the conventional gas ratio method. The comparison between the suggested method and existing methods indicates the capability of the suggested method in the on-line fault diagnosis of transformers. In addition, in some cases the existing standards are not able to diagnose the fault. For theses instances, the presented method has the potential of diagnosing the fault. In this paper, the information of three real transformers is used to show the capability of the suggested method in diagnosing the fault. The results validate the capability of the presented method in fault diagnosis of the transformer.

• 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.

• 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.

• International Journal of Control, Automation, and Systems
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• 제4권3호
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• pp.365-371
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• 2006

This paper presents an algorithm based on a combination of Discrete Wavelet Transforms and neural networks for detection and classification of internal faults in a two-winding three-phase transformer. Fault conditions of the transformer are simulated using ATP/EMTP in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented using toolboxes on MATLAB/Simulink. Various cases and fault types based on Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. It is found that the proposed method gives a satisfactory accuracy, and will be particularly useful in a development of a modern differential relay for a transformer protection scheme.

• 한국콘텐츠학회논문지
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• 제7권3호
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• pp.25-33
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• 2007

전력계통의 갑작스런 고장은 막대한 경제적 손실을 초래함으로 이를 방지하기 위한 전력계통의 상태를 진단하는 모니터링은 무엇보다도 중요하다. 본 논문에서는 FCM과 SOM을 이용하여 다양한 전력설비 중에서 가장 중요한 역할을 담당하는 전력용 변압기의 고장진단 알고리즘을 개발한다. 즉, FCM은 효과적인 특징점을 선택과 학습시간을 줄이기 위해 수행하고, SOM에 의해 변압기의 고장진단이 이루어진다. 제안된 방법은 변압기의 고장진단 뿐만 아니라 열화진행추이 특성까지 분석한다. 제안된 방법은 다양한 사례 연구를 통해 우수성을 입증하였다.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제50권11호
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• pp.569-574
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• 2001

In this paper, we described the new IEC method with fuzzy theory for detecting abnormal causes within transformer. The proposed technique presented the solution of limitation in case of lying nearly boundary conditions and not having codes for measured gas values in IEC code. Also, we proved the confidence of diagnosed results in the use of the gases values in real fault transformers.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.616-619
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• 1995

This paper describes developement of on-line system for vibration signal analysis. In the power system, the main reason of transformer fault is due to a large amount of current by a short-circuit and a ground-fault. The electromagnetic force caused by fault-current deforms transformer windings and results in vibration pattern change. Therefore if the continuous on-line vibration monitoring on transformer is performed, an incipient failure can be detected. The developed system is composed of data acquisition devices, user interface program, signal processing program, diagnosis and trend analysis program, self diagnosis program and communication program.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.830-839
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• 2017

Support vector machine (SVM) is introduced as an effective fault diagnosis technique based on dissolved gases analysis (DGA) for oil-immersed transformers with maximum generalization ability; however, the applicability of the SVM is highly affected due to the difficulty of selecting the SVM parameters appropriately. Therefore, a novel approach combing SVM with improved imperialist competitive algorithm (IICA) for fault diagnosis of oil-immersed transformers was proposed in the paper. The improved ICA, which is proved to be an effective optimization approach, is employed to optimize the parameters of SVM. Cross validation and normalizations were applied in the training processes of SVM and the trained SVM model with the optimized parameters was established for fault diagnosis of oil-immersed transformers. Three classification benchmark sets were studied based on particle swarm optimization SVM (PSOSVM) and IICASVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis. The results show that the proposed model can obtain higher diagnosis accuracy than other methods. The comparisons confirm that the proposed model is an effective approach for classification problems.

• 대한전기학회논문지
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• 제41권4호
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• pp.331-338
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• 1992

The application of Connectionist expert system using neural network to fault diagnosis of power system is presented and compared with rule-based expert system. Also, the merits of Connectionist model using neural network is presented. In this paper, the neural network for fault diagnosis is hierarchically composed by 3 neural network classes. The whole power system is divided into subsystems, the neural networks (Class II) which take charge of each subsystem and the neural network (Class III) which connects subsystems are composed. Every section of power system is classified into one of the typical sections which can be applied with same diagnosis rules, as line-section, bus-section, transformer-section. For each typical section, only one neural network (Class I) is composed. As the proposed model has hierarchical structure, the great reduction of learning structure is achieved. With parallel distributed processing, we show the possibility of on-line fault diagnosis.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 추계학술대회 논문집 학회본부
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• pp.143-145
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• 1996

This paper presents the hybrid type based expert system for fault diagnosis in transformers. The proposed system uses the novel fault diagnostic technique based on dissolved gas analysis(DGA) in oil-immersed transformers. The uncertainty of key gas analysis, norm threshold, and gas ratio boundaries are managed by using a fuzzy set. Also, the uncertainty of the fault diagnostic rules are handled by using fuzzy measures. Finally, kohnen's feature map performs fault classification in transformers. To verify the effectiveness of the proposed diagnosis technique, the hybrid type based expert system for fault diagnosis has been tested by using KEPCO's transformer gas records.