The Transactions of the Korean Institute of Electrical Engineers P (전기학회논문지P)

The Korean Institute of Electrical Engineers (대한전기학회)
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A Fault Diagnosis Method of Oil-Filled Power Transformers Using IEC Code based Neuro-Fuzzy Model

IEC 코드 기반의 뉴로-퍼지모델을 이용한 유입변압기 고장진단 기법

  • Received : 2016.02.10
  • Accepted : 2016.02.26
  • Published : 2016.03.01
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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.

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References

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