Data Mining Applications in the Electrical Industry

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Author(s): Rubén Jaramillo Vacio (CFE – LAPEM & CIATEC – CONACYT, Mexico), Carlos Alberto Ochoa Ortiz Zezzatti (Juarez City University, México)and Armando Rios (Institute Technologic of Celaya, Mexico)
Copyright: 2013
Pages: 17
Source title: Data Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-2455-9.ch084

Keywords: Data Mining / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

This chapter describes the experimental study partial discharges (PD) activities with artificial intelligent tools. The results present different patterns using a hybrid system with Self Organizing Maps (SOM) and Hierarchical clustering, this combination constitutes an excellent tool for exploration analysis of massive data such a partial discharge on underground power cables and electrical equipment. The SOM has been used for nonlinear feature extraction and the hierarchical clustering to visualization. The hybrid system is trained with different dataset using univariate phase-resolved distributions. The results show that the clustering method is fast, robust, and visually efficient.

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