Title :
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
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;
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
DOI :
10.1109/ANN.1991.213475
