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Component stress evaluation in an electrical power distribution system using neural networks

M.A. Sanz-Bobi, R.J. Andrade Vieira, C. Brighenti, R. Palacios, G. Nicolau, P. Ferrarons, P. Vieira Junior

23rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems - IEA/AIE 2010, Córdoba (España). 01-04 junio 2010


Resumen:

This paper presents a procedure that permits a qualitative evaluation of the stress in components of an electric power distribution system. The core of this procedure is the development of a set of models based on neural networks that are able to represent and to predict the normal behavior expected of the components under different working conditions. The paper includes the application to the characterization of the thermal behavior of power transformers and the operation time of circuit breakers..


Palabras clave: diagnosis - multi-layer perceptron - normal behavior models - self-organised map - anomaly detection - component stress


DOI: DOI icon https://doi.org/10.1007/978-3-642-13033-5_3

Publicado en Trends in Applied Intelligent Systems, pp: 21-30, ISBN: 978-3-642-13032-8

Fecha de publicación: 2010-06-01.



Cita:
M.A. Sanz-Bobi, R.J. Andrade Vieira, C. Brighenti, R. Palacios, G. Nicolau, P. Ferrarons, P. Vieira Junior, Component stress evaluation in an electrical power distribution system using neural networks, 23rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems - IEA/AIE 2010, Córdoba (España). 01-04 junio 2010. En: Trends in Applied Intelligent Systems: Proceedings of the 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems - IEA/AIE 2010, vol. Part III, ISBN: 978-3-642-13032-8


    Líneas de investigación:
  • *Predicción y Análisis de Datos
  • *Modelado, Simulación y Optimización
  • *Inteligencia artificial aplicada al mantenimiento, diagnostico y fiabilidad
  • *Robots móviles y visión artificial
  • *Análisis de Seguridad, Estudios RAMS y Control de Calidad

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