• The Transactions of the Korean Institute of Electrical Engineers C
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• v.50 no.11
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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.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.3
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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.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.11a
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• pp.87-90
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• 2004

This paper describes development of power transformer maintenance system using intelligent dissolved gases in oil analysis. The used gases are acetylene(C2H2), hydrogen(H2), ethylene(C2H4), methane(CH4), ethane(C2H6), carbon monoxide(CO) and carbon dioxide(CO2). The rule and neural network based gas analysis methods are used for artificial intelligent diagnosis. It is indicated that this program is efficient for diagnosis of oil immersed transformers diagnosis from application of gas analysis data of serviced transformer which has local overheating

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.374-376
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• 2001

In this paper, we developed the fault diagnostic expert system of silicone-immersed transformer using dissolved gas analysis. The knowledge base module consists of the knowledge using the rule: if Then . The inference engine uses the fuzzy rule for the management of uncertainty of the boundary and rule and derivate the Belief and Plausibility of the normality and fault using Dempster-Shafer theory. The expert system is connected to the database and it can manages the history of gas-data of the transformer.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.451-457
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• 2019

In this study, the characteristics of coffee grounds were reviewed by making them from solid fuel through heat-drying and oil-drying method. The differences in the higher calorific power by each dried sample were compared. And industrial analysis using the thermogravimetric analyzer was considered for applicability to organic waste and oily samples. Before and after drying, the surface of the specimen was observed with SEM equipment and the ingredients were measured through the EDS equipment. As a result, no other hazardous substances, such as heavy metals, were measured. Next, The differences between thermal decomposition and combustion reactions were considered through the TG and DTG curves. As a result, it is that the oil-dried coffee grounds is longer to burn than the heat-dried coffee grounds. Finally, the combustion gases emitted through the thermogravimetric analyzer were collected and the carbon monoxide and carbon dioxide performed qualitative and quantitative analysis using GC over time.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.7
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• pp.75-83
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• 2007

IEC code based decision nile have been widely applied to detect incipient faults in power transformers. However, this method has a drawback to achieve the diagnosis with accuracy without experienced experts. In order to resolve this problem, we propose an artificial diagnosis algorithm to detect faults of power transformers using Self-Organizing Feature Map(SOM). The proposed method has two stages such as model construction and diagnostic procedure. First, faulty model is constructed by feature maps obtained by unsupervised learning for training data. And then, diagnosis is performed by compare feature map with it obtained for test data. Also the proposed method usぉms the possibility and degree of aging as well as the fault occurred in transformer by clustering and distance measure schemes. To demonstrate the validity of proposed method, various experiments are unformed and their results are presented.