- Volume 14 Issue 9
DOI QR Code
Comparison of Artificial Neural Network for Partial Discharge Diagnosis
부분방전 진단을 위한 인공신경망 기법의 비교
- Chung, Gyo-Bum (Dept. of Electronic & Electrical Engineering, Hongik University) ;
- Kwack, Sun-Geun (COMMAX Co. Ltd)
- Received : 2013.06.04
- Accepted : 2013.09.06
- Published : 2013.09.30
This paper investigates the diagnosis performance of Artificial Neural Network (ANN) depending on the structure and the input vector type of ANN, which has been used to detect the partial discharge to lead to the electric machinery deterioration. The diagnosis performance of one hidden layer and two hidden layer in ANN are compared. The performance using the 2048 time-series data and the performance using the feature input vector are compared. For measuring the partial discharge signal, the tip-to-plate, the sphere-to-sphere, the tip-to-tip, the tip-to-sphere and the sphere-to-plate electrodes are used respectively. For ANN's learning, Matlab and C-code program are used. For evaluating the diagnosis performance of ANNs, the simulation studies are performed.
Artificial Neural Network;Feature Vector;Hidden Layer;Partial Discharge;Time Series Data
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