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
Materias
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
Recurso de información
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
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