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A Fault Diagnosis Method of Oil-Filled Power Transformers Using IEC Code based Neuro-Fuzzy Model
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 Title & Authors
A Fault Diagnosis Method of Oil-Filled Power Transformers Using IEC Code based Neuro-Fuzzy Model
Seo, Myeong-Seok; Ji, Pyeong-Shik;
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 Abstract
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 IEC code based neuro-fuzzy model. The proposed method proceeds two steps. First, IEC 60599 method is applied to diagnosis. If IEC code can't determine the fault type, neuro-fuzzy model is applied to effectively classify the fault type. To demonstrate the validity of the proposed method, experiment is performed and its results are illustrated.
 Keywords
Power transformers;Neuro-Fuzzy model;ANFIS;DGA;IEC 60599;
 Language
Korean
 Cited by
 References
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