Title :
An artificial neural network approach to transformer fault diagnosis
Author :
Zhang, Y. ; Ding, X. ; Liu, Y. ; Griffin, P.J.
Author_Institution :
Bradley Dept. of Electr. Eng., Virginia Polytech. Inst. & State Univ., Blacksburg, VA, USA
fDate :
10/1/1996 12:00:00 AM
Abstract :
This paper presents an artificial neural network (ANN) approach to the diagnosis and detection of faults in oil-filled power transformers based on dissolved gas-in-oil analysis. A two-step ANN method is used to detect faults with or without cellulose involved. Good diagnosis accuracy is obtained with the proposed approach
Keywords :
automatic test equipment; automatic test software; fault diagnosis; insulation testing; neural nets; power engineering computing; power transformer insulation; power transformer testing; transformer oil; artificial neural network techniques; cellulose; diagnosis accuracy; dissolved gas-in-oil analysis; fault detection; oil-filled power transformers; power transformer fault diagnosis; test automation; two-step ANN method; Artificial neural networks; Dissolved gas analysis; Fault diagnosis; Gases; Oil insulation; Petroleum; Power transformer insulation; Power transformers; Temperature; Thermal stresses;
Journal_Title :
Power Delivery, IEEE Transactions on
