| Title |
PNN based Rogers Diagnosis Method for Fault Classification of Oil-filled Power Transformer |
| Authors |
임재윤(Lim, Jae-Yoon) ; 이대종(Lee, Dae-Jong) ; 지평식(Ji, Pyeong-Shik) |
| DOI |
https://doi.org/10.5370/KIEEP.2016.65.4.280 |
| Keywords |
Fault diagnosis ; Power transformer ; DGA(Dissolved Gas Analysis) ; Rogers ; PNN(Probability Neural Network) |
| Abstract |
Stability and reliability of a power system in many respects depend on the condition of power transformers. Essential devices as power transformers are in a transmission and distribution system. Being one of the most expensive and important elements, a power transformer is a highly essential element, whose failures and damage may cause the outage of a power system. To detect the power transformer faults, dissolved gas analysis (DGA) is a widely-used method because of its high sensitivity to small amount of electrical faults. Among the various diagnosis methods, Rogers diagonsis method 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 PNN(Probability Neural Network) based Rogers diagnosis method. The test result show better performance than conventional Rogers diagnosis method. |