| Title |
Development of Artificial Diagnosis Algorithm for Dissolved Gas Analysis of Power Transformer |
| Authors |
Jae-Yoon LIm ; Dae-Jong Lee ; Jong-Pil Lee ; Pyeong-Shik Ji |
| Keywords |
IEC code ; Dissolved gas analysis ; Self-Organizing Feature Map(SOM) ; Aging |
| Abstract |
IEC code based decision rule have been widely applied to detect incipient faults in power transformers. Howerver, 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 measures the possibility and degree of aging as well as the fault occurred in transformer by clustering and distance treasure schemes, To demonstrate the validity of proposed method, various experiments are performed and their results are presented. |