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

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volumes 325-326)
• Pages: 424-430
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.325-326.424
• Online since: June 2013
• Authors: Zhong Jie Wang, Yong Chun Liang
• Keywords: BAM Neural Network, Fault Diagnosis, Power Transformer

References

[1] Liu Jun, Zhang Jing, She Zhiting, "Neural Network Based On-Line Detection of Dissolved Gas in Transformer Oil", Automation of Electric Power System, vol.25, no.4, pp.56-58, April (2001)

[2] Dengfu Zhao, "Application of Artificial Neural Network Based on Levenberg Marquardt Algorithm to Fault Diagnosis of Oil Filled Power Equipment", High Voltage Engineering, vol.30, no.7, pp.4-6,16, July (2004)

[3] Liang Yongchun, Li Yanming, "Application of Modified Combinatorial Radial Basis Function Neural Netowork in Fault Diagnosis of Power Transformer", High Voltage Engineering, vol.31, no.9, pp.31-33, September (2005)

[4] Yongchun Liang, Liuye Liu, "Power Transformer Fault Diagnosis Using SOM-Based RBF Neural Networks", ICMLC 2006 Dalian, pp.3140-3143, August (2006)

DOI: 10.1109/icmlc.2006.258406

[5] Yongchun Liang, Xiaoyun Sun, Donghui Liu, "Application of Combinatorial Probabilistic Neural Network in fault diagnosis of power transformer", ICMLC 2007 Hongkong, pp.1115-1118, August (2007)

DOI: 10.1109/icmlc.2007.4370311

[6] Yingjun Guo, Lihua Sun, Yongchun Liang, "The fault diagnosis of power transformer based on improved RBF neural network", ICMLC 2007 Hongkong, pp.1111-1114, August (2007)

DOI: 10.1109/icmlc.2007.4370310

[7] Neural Network and MATLAB7Application, Publishing House of Electronics Industry, Beijing, (2005)

[8] Cai Limei, Zhang Yuan, Li Shaohui, "Application of BAM neural network in the recognition of the automotive style", Jounal of Zhengzhou University of Light Industry (Natural Science Edition), 2002, no.2, pp.

[9] Wang, G.Y., H., Yang, D.C., " Decision Table Reduction based on Conditional Information Entropy," Chinese Journal of Computers, 2002, no.7,pp.759-766, (2002)

[10] Xiong Hao, Sun Cai-xin, Liao Rui-jin,et, "Study on Kernel-based Possibilistic Clustering and Dissolved Gas Analysis for Fault diagnosis of Power Transformer", Proceedings of the CSEE, vol.25,no.20,pp.162-166, October 2005.

DOI: 10.1109/icpst.2006.321491