Diagnosis of partial discharge using self organizing maps and hierarchical clustering: an approach

Authors:
Rubé/n Jaramillo-Vacio;Alberto Ochoa-Zezzatti;S. Jö/ns;Sergio Ledezma-Orozco;Camelia Chira
Affiliations:
CFE-LAPEM and Centro de Innovació/n Aplicada en Tecnologí/as Competitivas;Universidad Autó/noma de Ciudad Juá/rez;CFE-LAPEM;Universidad de Guanajuato - Divisió/n de Ingenierí/as;Babes-Bolyai University, Cluj-Napoca/ Romania
Venue:
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Year:
2011

Citing 2
Cited 0

Data Mining: Concepts, Models, Methods and Algorithms

Data Mining: Concepts, Models, Methods and Algorithms
Clustering of the self-organizing map

IEEE Transactions on Neural Networks

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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.