Abstract
The reliability of the power transformer operation is directly related to the security of power system and the reliability of power supply. In order to improve the diagnosis accuracy of internal insulation fault in transformer, this paper proposes an algorithm of transformer internal insulation fault diagnosis which is based on RBF neural network evolved by immune particle swarm optimization by analyzing the internal insulation fault type of transformer and the content of dissolved gas in transformer oil composition. The paper focuses on the composition principle of transformer fault diagnosis based on RBF neural network. The method of determining the number of hidden layer of network center and the initial position based on artificial immune network algorithm is given. The method of network weight optimization based on particle swarm optimization algorithm is developed. And the simulation experiment is also given. The simulation results show that the proposed algorithm can effectively diagnose the transformer fault types and the diagnosis accuracy can reach above 90 %.
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References
Zheng R, Zhao J, Zhao T et al (2011) Power transformer fault diagnosis based on genetic support vector machine and gray artificial immune algorithm. Proc CSEE 31(7):57–63
Wu X, Liu J, Liang Y et al (2007) Application of support vector machine in transformer fault diagnosis. J Xi’an Jiongtong Univ 41(6):722–726
Xiang W, Zhang H, Wang Y et al (2011) Application of BP neural network with L-M algorithm in power transformer fault diagnosis. Power Syst Protect Control 39(8)
Wang X, Li W, Zhang Y (2005) Application study of BP network used in the fault diagnosis of power transformer. High Voltage Eng 31(7):12–14
Chen J, Wen X, Lan L et al (2007) Fault diagnosis of power transformer by novel radial basis function neural network approach. High Voltage Eng 33(3):140–143
Zhou A, Zhang B, Zhang H (2007) Power transformer fault diagnosis by using the artificial immune classification algorithm. High Voltage Eng 33(8):77–80
Dong M, Y Meng, Xu C et al (2003) Fault diagnosis model for power transformer based on support vector machine and dissolved gas analysis. Proc CSEE 23(7):88–92
Li Q, He Y, Bao W (2010) Immune-ant algorithm based RBFNN for fault diagnosis of analog circuits. Chin J Sci Instrum 31(6):1255–1261
Ren J, Huang J (2010) Transformer fault diagnosis based on immune RBF neural network. Power Syst Protect Control 38(11):6–9
Xiong H, Sun C, Chen W et al (2006) Artificial immune network classification algorithm for fault diagnosis of power transformers. Autom Electr Power Syst 30(6):57–60
Zhao X, Tang X, Wang E et al (2013) Quantitative analysis of transformer oil dissolved gas using FTIR. Spectros Spectr Anal 33(9):2407–2410
Wang X, Wang T (2008) Power transformer fault diagnosis based on neural network evolved by particle swarm optimization. High Voltage Eng 34(11):2362–2367
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© 2016 Springer Science+Business Media Singapore
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Li, H., Wang, F., Wang, R. (2016). Transformer Internal Insulation Fault Diagnosis Based on RBF Neural Network Evolved by Immune Particle Swarm Optimization. In: Jia, Y., Du, J., Zhang, W., Li, H. (eds) Proceedings of 2016 Chinese Intelligent Systems Conference. CISC 2016. Lecture Notes in Electrical Engineering, vol 404. Springer, Singapore. https://doi.org/10.1007/978-981-10-2338-5_9
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DOI: https://doi.org/10.1007/978-981-10-2338-5_9
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Online ISBN: 978-981-10-2338-5
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