Abstract
Power systems monitoring is particularly challenging due to the presence of dynamic load changes in normal operation mode of network nodes, as well as the presence of both continuous and discrete variables, noisy information and lack or excess of data. In this domain, the need to develop more powerful approaches has been recognized, and hybrid techniques that combine several reasoning methods start to be used. The present work is a variant of a proposal made by the author. This paper proposes a fault diagnosis framework that is able to locate the set of nodes involved in multiple fault events. The proposal is a methodology based on the system history data. It detects the faulty nodes, the type of fault in those nodes and the time when it is present. The framework is composed of two phases: In the first phase a probabilistic neural network is trained with the eigenvalues of voltage data collected during normal operation, symmetrical and asymmetrical fault disturbances. The second phase uses an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to give the final diagnosis. A set of simulations are carried out over an electrical power system proposed by the IEEE. To show the performance of the approach, a comparison is made against two different diagnostic systems.
This is a preview of subscription content, log in via an institution to check access.
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
Hartstein, R., Rezende, K., Suman, A.: Fault Detection in Primary Distribution Systems using Wavelets. In: International Conference on Power Systems Transients (ISPT 2007), Lyon, France, pp. 1–6 (2007)
Hartstein, R., Rezende, K., Daros, A., Resener, M., Suman, A.: Hybrid Fault Diagnosis Scheme Implementation for Power Distribution System Automation. IEEE Transactions on Power Delivery 23(4), 1846–1856 (2008)
Liao, Z., Wen, F., Guo, W., He, X., Jiang, W., Dong, T., Liang, J., Xu, B.: An Analytical Model and Optimization Technique Based Methods for Fault Diagnosis in Power Systems. In: IEEE Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, Nanjing, China, pp. 1388–1393 ( April 2008)
Flauzino, R.A., Ziolkowski, V., da Silva, I.N., de Souza, D.M.B.: Hybrid Intelligent Architecture for Fault Identification in Power Distribution Systems. In: IEEE Power & Energy Society General Meeting, PES 2009, pp. 1–6 (2009)
Alsafasfeh, Q., Abdel-Qader, I., Harb, A.: Symmetrical Pattern and PCA Based Framework for Fault Detection and Classification in Power Systems. In: Conference on Electro Information Technology, pp. 1–5 (2010)
Nieto, J.P., Garza, L.E., Morales, R.: Multiple Fault Diagnosis in Electrical Power Systems with Dynamic Load Changes Using Probabilistic Neural Networks. Computación y Sistemas Magazine 14(1), 17–30 (2010)
Qianwen, N., Youyuan, W.: An Augmented Naive Bayesian Power Network Fault Diagnosis Method based on Data Mining. In: Asia-Pacific Power and Energy Engineering Conference (APEEC), pp. 1–4 (2011)
Jiang, J.A., Chuang, C.L., Wang, Y.C., Hung, C.H., Wang, J.Y., Lee, C.H., Hsiao, Y.T.: A Hybrid Framework for Fault Detection, Classification, and Location Part I: Concept, Structure, and Methodology. IEEE, Transactions on Power Delivery 26(3), 1988–1998 (2011)
Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.N.: A Review of Process Fault Detection and Diagnosis Part I: Quantitative Model-Based Methods. Computers and Chemical Eng. 27(3), 293–311 (2003)
Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn (2001) ISBN 0-0471-05669-3
Johnson, R., Wichern, D.: Applied Multivariate Statistical Analysis, 5th edn. Prentice Hall (2002)
Stuart, R., Peter, N.: Artificial Intelligence a Modern Approach, 2nd edn. McGraw-Hill (2003)
Garza, L.: Hybrid Systems Fault Diagnosis with a Probabilistic Logic Reasoning Framework. Ph.D. Thesis, Instituto Tecnológico y de Estudios Superiores de Monterrey, Campus Monterrey (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
González, J.P.N. (2013). Multiple Fault Diagnosis in Electrical Power Systems with Dynamic Load Changes Using Soft Computing. In: Batyrshin, I., Mendoza, M.G. (eds) Advances in Computational Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37798-3_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-37798-3_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37797-6
Online ISBN: 978-3-642-37798-3
eBook Packages: Computer ScienceComputer Science (R0)