| Title |
Application of LVQ3 for Dissolved Gas Analysis for Power Transformer |
| Authors |
전영재(Jeon, Yeong-Jae) ; 김재철(Kim, Jae-Cheol) |
| Keywords |
Transformer ; Fault Diagnosis ; Dissolved Gas Analysis ; Learning Vector Quantization |
| Abstract |
To enhance the fault diagnosis ability for the dissolved gas analysis(DGA) of the power transformer, this paper proposes a learning vector quantization(LVQ) for the incipient fault recognition. LVQ is suitable expecially for pattern recognition such as fault diagnosis of power transformer using DGA because it improves the performance of Kohonen neural network by placing emphasis on the classification around the decision boundary. The capabilities of the proposed diagnosis system for the transformer DGA decision support have been extensively verified through the practical test data collected from Korea Electrical Power Corporation. |