| Title |
A Study on the Automatic PD Diagnosis Method for Gas Insulated Load Break Switches |
| Authors |
전시식(Si-Shik Jeon) ; 차동욱(Dong-wook Cha) ; 김영달(Young-Dal Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.1.267 |
| Keywords |
Partial discharge; Distribution system; Neural Network; Automatic Diagnosis |
| Abstract |
Partial discharge diagnosis is being carried out to prevent failure of ground switch of distribution facilities. However, the reliability of partial discharge diagnosis is insufficient because the results are different depending on how the experts classify the partial discharge and noise signals. Therefore, in this paper, a method to automatically classify partial discharge and noise signals was studied in order to improve the reliability of the partial discharge diagnosis results of ground switch. Partial discharge and noise signal are obtained through the High Frequency Current Transformer(HFCT). And the features were extracted based on the pulse shape analysis, the standard deviation and gravity center of signal. The features are used as input to neural networks and learned using back-propagation. In addition, the structure of neural networks was optimized through genetic algorithm. |