A hybrid neural network and expert system for monitoring fossil fuel power plants

Author

Kraft, Timothy ; Okagaki, Karen ; Ishii, Ron ; Surko, Pamela ; Brandon, Ann ; DeWeese, Alvah ; Peterson, Scott ; Bjordal, Robert

Author_Institution

Science Applications International Corp., San Diego, CA, USA

fYear

1991

fDate

23-26 Jul 1991

Firstpage

215

Lastpage

218

Abstract

A fully recurrent neural network and a rule-based expert system are combined in a hybrid architecture to provide power plant operators with an intelligent on-line advisory system. Its purpose is to alert the operator to impending or occurring abnormal conditions related to the plant´s boiler. The hybrid system is trained to provide a model of the boiler under normal operation, while the rules address a general set of diagnostic events. Deviation from normal conditions trigger rules to suggest corrective action. This system is intended to increase plant availability and efficiency by automatically deducing abnormal boiler conditions before they become critical

Keywords

boilers; expert systems; neural nets; power station computer control; thermal power stations; abnormal boiler conditions; availability; efficiency; fossil fuel power plants; hybrid architecture; intelligent on-line advisory system; neural network; power plant operators; rule-based expert system; Boilers; Data analysis; Expert systems; Fossil fuels; Hybrid intelligent systems; Intelligent networks; Monitoring; Neural networks; Power generation; Recurrent neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks to Power Systems, 1991., Proceedings of the First International Forum on Applications of

Conference_Location

Seattle, WA

Print_ISBN

0-7803-0065-3

Type

conf

DOI

10.1109/ANN.1991.213475

Filename

213475