Título

SEDRET an intelligent system for the diagnosis and prediction of events in power plants

Autor

GUSTAVO ARROYO FIGUEROA

LUIS ENRIQUE SUCAR SUCCAR

Nivel de Acceso

Acceso Abierto

Resumen o descripción

Artificial Intelligence applications in large-scale industry, such as fossil power plants, require the ability to manage uncertainty and time.

In this paper, we present an intelligent system to assist an operator of a power plant. This system, called SEDRET, is based on a novel

knowledge representation of uncertainty and time, called Temporal Nodes Bayesian Networks (TNBN), a type of Probabilistic Temporal

Network. A set of temporal nodes and a set of edge define a TNBN, each temporal node is defined by a value of a variable and a time interval

associate to the change of variable value. A TNBN generates a formal and systematic structure for modeling the temporal evolution of a

process under uncertainty. The inference mechanism is based on probabilistic reasoning. A TNBN can be used to recognize events and state

variables with respect to current plant conditions and predict the future propagation of disturbances. SEDRET was validated with the

diagnosis and prediction of events in a steam generator with a power plant training simulator. The results performed in this work indicate that

SEDRET can potentially improve plant availability through early diagnosis and prediction of disturbances that could lead to plant shutdown.

Fecha de publicación

febrero de 2000

Tipo de publicación

Artículo

Versión de la publicación

Versión publicada

Formato

application/pdf

Fuente

ISSN 0930-1984

Idioma

Inglés

Repositorio Orígen

Repositorio Institucional de Acceso Abierto de Información Científica, Tecnológica y de Innovación del INEEL

Descargas

239

Comentarios



Necesitas iniciar sesión o registrarte para comentar.