Abstract
This paper shows a first approach in a diagnosis selecting the different features to classify measured of partial discharges (PD) activities into underlaying insulation defects or source that generate PD. The results present different patterns using a hibrid method with Self Organizing Maps (SOM) and Hierarchical clustering, this combination constitutes an excellent tool for exploration analysis of massive data like partial discharge on underground power cables. The SOM has been used for nonlinear feature extraction. Therefore, the clustering method has been fast, robust, and visually efficient.
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
IEC 60270 Ed. 2. High-voltage test techniques - Partial discharge measurements (2000)
Wills, L.: Electrical Power Cable Engineering. Marcel Dekker, Inc., New York (1999)
McGrail, A.J., Gulski, E.: Data mining techniques to assess the condition of high voltage electri-cal plant. In: CIGRÉ (2002)
Strachan, S.M., Stephen, B.: Practical applications of data mining in plant monitoring and diagnos-tics. IEEE Power Engineering Society General Meeting (2007)
Kantardzic, M.: Data Mining; Concepts, Methods and Algorithms. Wiley, New York (2003)
Johnson, R.A., Wichern, E.W.: Applied Multivariate Statistical Analysis, 5th edn. Prentice-Hall, Englewood Cliffs (2002)
Forssén, C. Modelling of cavity partial discharges at variable applied frequency. Sweden: Doctoral Thesis in Electrical Systems. KTH Electrical Engineering (2008)
Edin, H.: Partial discharge studies with variable frequency of the applied voltage. Sweden: Doctoral Thesis in Electrical Systems. KTH Electrical Engineering (2001)
Lai, K., Phung, B.: Descriptive Data Mining of Partial Discharge using Decision Tree with genetic algorithms. In: AUPEC (2008)
Salama, M.M.A.: PD pattern recognition with neural netwoks using the multilayer perception technique. IEEE Transactions on Electrical Insulation 28, 1082–1089 (1993)
Markalous, S.: Detection and location of Partial Discharges in Power Transformers using acoustic and electromagnetic signals. Stuttgart University: PhD Thesis (2006)
Kohonen, T.: Engineering Applications of Self Organizing Map. Proceedings of the IEEE (1996)
Rubio-Sánchez, M.: Nuevos Métodos para Análisis Visual de Mapas Auto-organizativos. PhD Thesis. Madrid Politechnic University (2004)
Vesanto, J., Alhoniemi, E.: Clustering of the Self Organizing Map. IEEE Transactions on Neural Networks 11(3), 1082–1089 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jaramillo-Vacio, R., Ochoa-Zezzatti, A., Jöns, S., Ledezma-Orozco, S., Chira, C. (2011). Diagnosis of Partial Discharge Using Self Organizing Maps and Hierarchical Clustering – An Approach. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21219-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-21219-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21218-5
Online ISBN: 978-3-642-21219-2
eBook Packages: Computer ScienceComputer Science (R0)