Application of BAM Network in Fault Diagnosis of Oil-Immerseed Transformer

2013 ◽  
Vol 325-326 ◽  
pp. 424-430
Author(s):  
Zhong Jie Wang ◽  
Yong Chun Liang
Keyword(s):  
Fault Diagnosis ◽  
Magnetic Circuit ◽  
Power Transformer ◽  
Rbf Neural Network ◽  
Partial Discharge ◽  
Electrical Circuit ◽  
Bidirectional Associative Memory ◽  
The Third ◽  
Neural Network Algorithm ◽  
Solid Insulation

Bidirectional Associative Memory (BAM) network is presented to analysis the fault of power transformer. In order to improve the classification accuracy, the conception of combination is introduced. The fault diagnosis of power transformer is consisted of 4 BAM networks. The first BAM network is used to classify the normal and fault. The second BAM network is used to classify the heat fault and partial discharge (PD) fault. The third BAM network is used to classify MC-overheating faults in magnetic circuit and EC-overheating faults in electrical circuit. The fourth BAM network is used to classify RSI-discharge faults related to solid insulation, USI-discharge faults unrelated to solid insulation. By comparing with the RBF neural network algorithm for the same 90 input set, we conclude that the BAM network a good classifier for the fault diagnosis of power transformer.

Assessing degradation of power transformer solid insulation considering thermal stress and moisture variation

2017 ◽  
Vol 151 ◽  
pp. 1-11 ◽  
Author(s):  
Ricardo D. Medina ◽  
Andrés A. Romero ◽  
Enrique E. Mombello ◽  
Giuseppe Rattá
Keyword(s):  
Thermal Stress ◽  
Power Transformer ◽  
Moisture Variation ◽  
Solid Insulation
Sign in / Sign up

Export Citation Format

Share Document