Abstract
The power transformer as the key equipment in electrical power systems, its operation reliability directly influences security of electrical power systems. Three-ratio method based on the Dissolved Gases Analysis is most widely used for transformer fault diagnosis currently. Considering the incomplete encoding and the over absolute faults classification zone of three-ratio method, this paper proposes no-code ratio method and Bayesian Network to diagnose the faults of transformer. The Bayesian Network diagnostic model is built by Bayesian Network Tool in MATLAB, and the simulation result shows the validity of this method.
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References
China Electricity Council: GB / T 7252-2001 transformer oil dissolved gas analysis and determination guidelines. Chinese Standard Press, Beijing (2001)
Xiaoxia, W., Tao, W.: Power Transformer Fault Diagnosis Based on Neural Network Evolved by Particle Swarm Optimizaion. High Voltage Engineering 34(11), 2362–2367 (2008)
Weigen, C., Chong, P., Yuxin, Y., Youyuan, W., Caixin, S.: Fault diagnostic method for power transformer based on improved wavelet neural network algorithm. Chinese Journal of Scientific Instrument 29(7), 1490–1493 (2008)
Jian, L., Caixin, S., Weigen, C., Guoqing, C., Xuemei, C.: Study on Fault Diagnosis of Insulation of Oil-immersed Transformer Based on Grey Cluster Theory. Transactions of China Electrotechnical Soceity 17(4), 80–83 (2002)
Jianyuan, W., Yanchao, J.: Application of fuzzy Petri nets knowledge representation in electric power transformer fault diagnosis. Proceedings of the Csee 23(1), 121–125 (2003)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian Network classifiers. Machine Learning 29(3), 131–163 (1997)
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© 2011 Springer-Verlag Berlin Heidelberg
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Li, Q., Li, Z., Zhang, Q., Zeng, L. (2011). Research of Bayesian Networks Application to Transformer Fault Diagnosis. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_47
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DOI: https://doi.org/10.1007/978-3-642-23896-3_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23895-6
Online ISBN: 978-3-642-23896-3
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