Abstract
This paper presents an innovative method based on Artificial Neural Network (ANN) and multi-layer Support Vector Machine (SVM) for the purpose of fault diagnosis of power transformers. A clonal selection algorithm (CSA) based encoding technique is applied to improve the accuracy of classification, which demonstrated in the literature for the first time. With features and RBF kernel parameters selection to predict incipient fault of power transformer improve the accuracy of classification systems and the generalization performance. The proposed approach is distinguished by removing redundant input features that may be confusing the classifier and optimizing the selection of kernel parameters. Simulation results of practice data demonstrate the effectiveness and high efficiency of the proposed approach, which makes operation faster and also increases the accuracy of the classification.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Vapnik, V., Golowich, S., Smola, A.: Support Vector Method for Function Approximation, Regression Estimation, And Signal Processing [A]. In: Mozer, M., Jordan, M., Petsche, T. (eds.) Neural Information Processing Systems [M], p. 9. MIT Press, Cambridge (1997)
Chapelle, O., Vapnik, V., Bousqet, O., Mukherjee, S.: Choosing Multiple Parameters for Support Vector Machines. Machine Learning 46(1), 131–159 (2002)
IEC Publication 599, Interpretation of The Analysis of Gases in Transformers And Other Oil-Filled Electrical Equipment In Service, 1st edn. (1978)
IEEE Guide for The Interpretation of Gases Generated In Oil-Immersed Transformers, IEEE Std.C57.104-1991
Rogers, R.R.: IEEE And IEC Codes to Interpret Faults in Transformers Using Gas in Oil Analysis. IEEE Trans Electron. Insulat. 13(5), 349–354 (1978)
Wang, M.H.: A Novel Extension Method for Transformer Fault Diagnosis. IEEE Trans. Power Deliv. 18(1), 164–169 (2003)
Wang, N.C.: Development of Monitoring and On-line Diagnosing System for Large Power Transformers, Power Reserch Institute, TPC
Yuanping, N.: One Intelligent Method for Fault Diagnosis and Its Application. IEEE Trans. Power Deliv., 755–758 (2000)
Lv., G., Cheng, H., Zhai, H., Dong, L.: Electric Power Systems Research 75, pp. 9–15. Elsevier B.V, Amsterdam (2005)
Jack, L.B., Nandi, A.K.: Fault Detection Using Support Vector Machines and Artificial Neural Networks: Augmented by Genetic Algorithms. Mech. Syst. Signal Process. 16(2–3), 373–390 (2002)
Chan, W.C., Chan, C.W., Cheung, K.C., Harris, C.J.: On The Modeling of Nonlinear Dynamic Systems Using Support Vector Neural Networks. Eng. Appl. Artif. Intell. 14, 105–113 (2001)
Frohlich, H.: Feature Selection for Support Vector Machines by Means of Genetic Algorithms. Master’s thesis, University of Marburg (2002)
Dong, M.: Fault Diagnosis Model Power Transformer Based on Statistical Learning Theory and Dissolved Gas Analysis. In: IEEE 2004 Internation Symposium on Electrical Insulation, pp. 85–88 (2004)
Huang, Y.C., Yang, H.T., Huang, C.L.: Developing a new transformer fault diagnosis system through evolutionary fuzzy logic. IEEE Trans. Power Deliv. 12(2), 761–767 (1997)
Cortes, C., Vapnik, V.: Support-vector Networks. Machine Learn. 20(3), 273–295 (1995)
Tay Francis, E.H., Cao, L.J.: Application of support vector machines in financial time series forecasting. Omega 29(4), 309–317 (2001)
”BECTA”, Power Station Magazine in Russia, No.6 (1998)
Yan, W.W., Shao, H.H.: Application of Support Vector Machine Nonlinear Classifier to Fault Diagnoses. In: Proceedings of the Fourth World Congress Intelligent Control and Automation, 10–14 Shanghai, China, June 2002, pp. 2670–2697 (2002)
Lee, J.H., Lin, C.J.: Automatic model selection for support vector machines, Technical Report, Dept. of Computer Science and Information Engineering, Taipei, Taiwan (November 2000)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Duval, M.: Interpretation of Gas-In-Oil Analysis Using New IEC Publication 60599 and IEC TC 10 Databases. IEEE Electrical Insulation Magazine 17(2) (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cho, MY., Lee, TF., Gau, SW., Shih, CN. (2006). Power Transformer Fault Diagnosis Using Support Vector Machines and Artificial Neural Networks with Clonal Selection Algorithms Optimization. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_22
Download citation
DOI: https://doi.org/10.1007/11892960_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46535-5
Online ISBN: 978-3-540-46536-2
eBook Packages: Computer ScienceComputer Science (R0)