Reconstruction and Intelligent Control for Power Plant

211 Pages·2022·9.097 MB·English

by  Chen Peng, Chuanliang Cheng, Ling Wang

Chen Peng
Chuanliang Cheng
Ling Wang
Reconstruction
and Intelligent
Control
for Power Plant
Reconstruction and Intelligent Control for Power
Plant
· ·
Chen Peng Chuanliang Cheng Ling Wang
Reconstruction
and Intelligent Control
for Power Plant
ChenPeng ChuanliangCheng
SchoolofMechatronicEngineering SchoolofMechatronicEngineering
andAutomation andAutomation
ShanghaiUniversity ShanghaiUniversity
Shanghai,China Shanghai,China
LingWang
SchoolofMechatronicEngineering
andAutomation
ShanghaiUniversity
Shanghai,China
ISBN 978-981-19-5573-0 ISBN 978-981-19-5574-7 (eBook)
https://doi.org/10.1007/978-981-19-5574-7
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Preface
Coal-firedpowerplantisahugeandcomplexplantthatproduceselectricenergyand
heatenergy.InChina,thecoal-firedpowerplantisnotonlythelargestproducerof
secondary energy, but also the largest consumer of primary energy. In addition to
coal, the coal-fired power plant consumes a certain amount of fuel oil and a large
amountoffreshwater.Atthesametime,itproducespulverizedcoalashequivalent
to about 20% of the total coal consumption and a large amount of sulfur dioxide,
carbondioxide,nitrogenoxide,andindustrialwastewater.Sofar,theproblemsofhigh
energyconsumptionandexcessivepollutionemissioninpowerproductionhavenot
beenfundamentallysolved.Theresearchtaskofenergyconservationandemission
reductionofcoal-firedpowerplantsisstillarduous.
The study of energy conservation and emission reduction for coal-fired power
plants can be classified into twofold: keeping the internal combustion field stable
in industrial furnaces and optimal control of the boiler-turbine unit. In the first
keeping the internal combustion field stable, by means of flame image detection,
imagesegmentation,andflamemodeling,thecombustionstatusinthefurnacecan
beunderstoodintuitivelyandaccurately.Thenthetemperaturefieldisreconstructed
bytemperaturemeasurementmethods,soastojudgewhetherthecombustioncondi-
tioninthefurnaceisstableintime.Inaddition,thereconstructedtemperaturefield
isintroducedintotheoptimalcontrolsystemofthecoal-firedboilerasaninterme-
diatevariabletooptimizethecombustioninthefurnace,sothatthecombustioncan
reach the ideal working condition as soon as possible. However, due to the high
complexityofworkingconditionsinthefurnace,therearestillsomeproblemswhen
usingthecommontechnologies,suchas(1)thecommonlyusedalgorithmscannot
getsatisfactorysegmentationresults.(2)Thecurrentsegmentationmodelhaspoor
generalizationability.(3)Thetraditionaltemperaturemeasurementmethodsbased
on flame model cannot obtain accurate fire radiant energy data. Therefore, how
to effectively keep the state of the combustion field stable inside the furnace has
stimulatedthefirstresearchlineofthismonograph.
Inthesecondoptimalcontroloftheboiler-turbineunit,themodelingandcontrol
methodsareanalyzed.Anaccuratemodelisthebasisofeffectivecontrolmethods.
The accuracy of a model determines the quality of control, because the optimal
v
vi Preface
controller of a system must include the mathematical model of the object. After
obtaining the model, it is necessary to introduce subsequent methods to form an
effectivecontrolstrategy.However,mostcoal-firedpowerplantsarecomplexmulti-
variablesystemswithseriousnonlinearity,uncertainty,andmulti-variablecoupling.
These characteristics will be more obvious when the system operates at a higher
level of energy conversion capability. Therefore, it is almost impossible to build a
mathematicalmodelfortheobjectusingtraditionalidentificationmethods.Besides,
most commonly used control methods have the disadvantages of long adjustment
time, large overshoot, inaccurate accuracy, and so on. Therefore how to design an
accuratemodeltorepresenttheoperatinginformationoftheobject,anddesignan
effectivecontrollerbasedontheobtainedmodelhavestimulatedthesecondresearch
lineofthismonograph.
Structure and readership. This book is structured into three parts. Part I is
devoted to introduce an overview of recent developments of coal-fired plants, and
provide a summary of image detection, information processing, modeling, and
control strategy used in the derivation of the main results of this book (Chap. 1),
they are the premises of the following two parts. Part II is devoted to analyze
the characteristics of internal combustion in industrial furnace, and apply relevant
methodstodetectandprocessflameimagesforestablishinganewtemperaturefield
model.PartIIIisdevelopedtodesignanaccuratemathematicalmodeltoexpressthe
runninginformationoftheboiler-turbineunit.Inaddition,onthisbasis,researchthe
controlstrategytoadapttothedesignedmodelforachievingthesatisfactorycontrol
performance.
PartII:Forlarge-scalecoal-firedpowerplants,maintainingthestabilityoffurnace
internalcombustionisveryimportantforthesafety,reliability,andeconomyofpower
plants. Pulverized coal combustion is a very complex chemical process, and the
stabilityofcombustionflamedirectlydeterminesthecombustionefficiencyofboiler.
Therefore,inChap.2,anewgrayconversionmethodandanadaptivehybridedge
detectionalgorithmareproposedtothegettheedgeofflameregioninfurnaceflame
image. Based on the obtained edge information of flame image, Chap. 3 proposes
a furnace flame recognition method based on improved particle swarm optimiza-
tion(IPSO)algorithm.Themethodfirstlyusesred-green-blue(RGB)colorspaceto
designtheextractionmodelofflameimage,thenusestheproposedIPSOalgorithm
andOstualgorithmtosolvetheoptimalsegmentationthresholdinvolvedinthemodel.
Comparedwiththepreviousresearchresults,therecognitionrateoftheextraction
model designed in this book is greatly improved. A temperature field reconstruc-
tionmethodbasedontheimprovedtwo-colortemperaturemeasurementmethodis
proposedinChap.4.Thismethodsolvestheproblemthattraditionalfurnacetemper-
ature field reconstruction algorithms cannot obtain accurate radiation energy data,
whichmakesthemeasurementandcalculationofthismethodmoresimplified,and
the accuracy is improved. To effectively predict the furnace temperature, Chap. 5
putsforwardanovelfurnacetemperaturepredictionmethodusingoptimizedkernel
extreme learning machine, which outperforms state-of-the-art furnace temperature
predictionapproaches,providinghighpredictionaccuracyandlowfalseprediction
error.
Preface vii
PartIII:Inlargecoal-firedpowerplants,theboiler-turbineunitconvertsthechem-
ical energy into electric energy. For achieving the plant-wide operating, accurate
modeling and effective coordinated control are necessary based on the distributed
control system with powerful computer, big data acquisition system, and big data
communication function. Therefore, in Chap. 6, a fuzzy particle swarm optimiza-
tion algorithm with the structure of the fuzzy K-means network is proposed and
applied as themodelidentification method, aiming atexploring thecharacteristics
ofoperationregionofpowerplants.Bytheproposedmethod,theplant-wideoper-
ating information is accurately represented through a dynamical linear model. To
improve the boiler combustion efficiency, a fuzzy K-means cluster-based general-
izedpredictive control method (FKNGPC) isintroduced inChap. 7,achieving the
satisfactoryperformanceinthecaseofloadtrackingandgridloadfrequencydistur-
bances.Then,aimingatsolvingtheproblemofslowadjustmentspeedofGPC,PID,
orothermethods,Chap.8proposesanovelcontrolstrategywhichconsidersinternal
modelcontrol(IMC),intelligentmodeling,andGPCtogether.Chapter9proposes
animprovedintelligentdata-drivencontrolmethodtodesigntheoptimalcontroller
forthepulverizingsystem,whichisbasedonintelligentvirtualreferencefeedback
tuningandanewadaptivehumanlearningoptimizationalgorithm.Comparedwith
traditionalmodel-basedmethods,itismucheasiertoimplementandcanavoidthe
influenceoferrorofmodeling.
Shanghai,China ChenPeng
June2022 ChuanliangCheng
LingWang
Acknowledgements
We would like to acknowledge the collaborations with Professor Tengfei Zhang
on the work of image detection, information processing; Professors Minrui Fei
and Deliang Zeng on the work of process modeling and control reported in the
monograph; and PhD candidates Wei Wang, Tao Wang, Hanyu Mi, Yusen Gang,
and Dr. Pinggai Zhang for their great help in this monograph. The supports from
theNationalNaturalScienceFoundation ofChinaunderGrant61833011, andthe
International Corporation Project of Shanghai Science and Technology Commis-
sion under Grant 21190780300. Finally, the close cooperation with Springer as
publisherandparticularlywithDr.JasmineDouasresponsibleeditorisgratefully
acknowledged.
ix
Contents
PartI IntroductionandPreliminariesforPowerPlant
1 Introduction ................................................... 3
1.1 TheResearchBackground ................................... 3
1.2 ResearchStatusofFlameDetectionSystem .................... 4
1.3 ResearchStatusofFlameImageProcessing .................... 6
1.4 ResearchStatusofTemperatureFieldReconstruction ............ 8
1.5 Research Status of Optimal Control for the Coal-Fired
Boiler-TurbinePowerPlant .................................. 11
1.6 MainContentsofthisMonograph ............................. 14
References ..................................................... 16
PartII DetectionofFurnaceFlameImageandReconstruction
ofTemperatureField
2 AdaptiveMixedEdgeDetectionofFurnaceFlameImage .......... 23
2.1 MethodsforConvertingColorImagetoGrayImage ............. 24
2.1.1 CommonImageConversionAlgorithms ................. 24
2.1.2 NewGrayConversionMethod ......................... 25
2.2 ImagePreprocessingandEdgeComputing ..................... 28
2.2.1 Preprocessing ....................................... 28
2.2.2 EdgeComputing ..................................... 29
2.3 AdaptiveEdgeSelectionAlgorithm ........................... 30
2.4 SimulationandResultsAnalysis .............................. 30
2.4.1 GrayImageConversionExperiment .................... 30
2.4.2 EdgeDetectionExperiment ........................... 34
2.5 Conclusion ................................................ 37
References ..................................................... 37
3 IntelligentSegmentationofFurnaceFlameImage ................. 39
3.1 SpatialDistributionCharacteristicsofFlameImage ............. 40
3.2 ExtractionModelofFlameImage ............................. 42
xi
xii Contents
3.3 OptimalSegmentationThreshold ............................. 45
3.3.1 FlameImageSegmentationThresholdExpression ........ 45
3.3.2 OptimalSegmentationThresholdExpression ............. 46
3.3.3 ParticleSwarmOptimizationAlgorithm ................. 47
3.3.4 ImprovedPSOAlgorithm ............................. 48
3.4 SimulationandResultsAnalysis .............................. 53
3.4.1 VerificationofImprovedPSO .......................... 54
3.4.2 VerificationofFlameIdentification ..................... 57
3.5 Conclusion ................................................ 61
References ..................................................... 62
4 ReconstructionofTemperatureFieldBasedonLimitedFlame
ImageInformation ............................................. 65
4.1 CombustionCharacteristicsofBoilerSystem ................... 66
4.2 FlameTemperatureMeasurementAlgorithmBasedonDigital
ImageProcessing ........................................... 67
4.2.1 Two-ColorMethodforTemperatureMeasurement ........ 68
4.2.2 Single-ColorMethodforTemperatureMeasurement ...... 69
4.2.3 Full-ColorMethodforTemperatureMeasurement ........ 71
4.2.4 Two-Color Temperature Measurement Based
onDigitalImageProcessing ........................... 72
4.3 TemperatureFieldReconstructionBasedonLeastSquare
Method ................................................... 75
4.4 Temperature Field Reconstruction Based on Intelligent
Algorithm ................................................. 78
4.5 SimulationandResultsAnalysis .............................. 81
4.5.1 CandleFlameReconstruction .......................... 84
4.5.2 FurnaceFlameReconstruction ......................... 87
4.6 Conclusion ................................................ 89
References ..................................................... 90
5 FurnaceTemperaturePredictionBasedonOptimizedKernel
ExtremeLearningMachine ..................................... 91
5.1 Prediction Model by Using Optimized Kernel Extreme
LearningMachine .......................................... 92
5.1.1 OptimizedKernelExtremeLearningMachine ............ 92
5.1.2 ObjectiveFunctionoftheOptimizedKernelExtreme
LearningMachinePredictionModel .................... 94
5.2 HumanLearningOptimization ............................... 95
5.2.1 Binary-Coded Human Learning Optimization
Algorithm ........................................... 95
5.2.2 ContinuousHumanLearningOptimizationAlgorithm ..... 97
5.2.3 Hybrid-Coded Human Learning Optimization
AlgorithmwithReasoningLearning .................... 102
5.3 ImplementationofOKELMBasedonHcHLORLAlgorithm ..... 105
5.4 SimulationandResultsAnalysis .............................. 107