%0 Journal Article
%T bpneuralnetworkbasedonlinepredictionofsteamturbineexhaustdryness
%A xiedanmei
%A chenchang
%A xiongyangheng
%A gaoshang
%A wangchun
%J ÈÈÁ¦·¢µç
%P 43-47
%D 2014
%X inlargescalecondensingturbineunit,theexhauststatusalwaysliesinwetsteamarea.duetothelackofeffectivemeasuringmethod,theexhaustdrynessofthesteamturbineisdifficulttoobtaindirectly,whichhasbeenthedifficultprobleminonlineeconomicanalysisforthermalpowerunits.bytakingann1000-25/600/600ultra-supercriticalsteamturbineasanexample,thenonlinearmappingabilityofbpneuralnetworkwasusedtoestablishamodelwhichcanreflecttherelationshipbetweenexhaustdrynessandunitloadandexhaustpressure.afterlearningandtrainingundersometypicalconditions,thismodelwasusedforexhaustdrynessonlinecalculationunderfullcondition.theresultsshowthefinalerrorofthetrainingsamplesandverifyingsampleswerecontrolledwithin-0.0061and-0.0010,whichsatisfiestheaccuracyrequirementforengineeringcalculation,indicatingtheestablishedbpneuralnetworkcanbeusedinexhaustdrynessprediction.
%K exhaustdryness
%K bpneuralnetwork
%K onlineprediction
%U http://rlfd.paperopen.com//oa/darticle.aspx?type=view&id=201409009