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.
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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
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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
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