Machine learning models for bulk electric system well-being assessment

• Rocco S., Claudio M. ^[1] ; Muselli, Marco ^[2]
  1. [1] Universidad Central de Caracas
  2. [2] CNR (Consiglio Nazionale delle Ricerche)
• Localización: XII Conferencia de la Asociación Española para la Inteligencia Artificial: (CAEPIA 2007). Actas / coord. por Daniel Borrajo Millán , Luis Castillo Vidal , Juan Manuel Corchado Rodríguez , Vol. 1, 2007, ISBN 978-84-611-8847-5, págs. 297-306
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • In this paper we compare two machine learning algorithms (Support Vector Machine (SVM) and Hamming Clustering (HC)) to perform a reliability assessment of an electric power system. Bulk electric system well-being analysis, which corresponds to the classification of the possible state of an electric power system as Healthy, Marginal or At Risk is properly emulated by training multi-class SVM and HC models, with a small amount of information. The experiments show that although both models produce reasonable predictions, HC accuracy is greater than the SVM one.