自适应控制算法在水电站机组运行优化中的应用

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中图分类号：TV213.4 文献标志码：A 文章编号:2095-2945(2026)08-0177-0

Abstract:Inthecontextofthetransformationofnewpowersystemsdrivenbythe"doublecarbon"goal，theoperation optimizationofhydropowerstationunitsfacescorechalengessuchasstrongnon-linearitytime-varyingparametersandmultiple constraints.Tosolvetheseproblems,thispaperproposesamulti-layercolaborativeadaptivecontrolalgorithm.Thealgorithm inovatively introducesadual-timescaleadaptivemechanism.Thelocaladaptivemodelpredictivecontrol(LAMPC)onthefast timescale(10ms)isesponsibleforhighfrequencypowerrackingandosclltionsuppresson，andtheglobaladaptiveffcency optimizationontheslowtimescale(1s).(GAEO)isbasedonalightweightdigitaltwinforonlineeficiencyoptimization.Inorder toovercomethelimitationsoftraditionaldigialtwinswithlargecomputingvolume,thispaperbuildsareduced-orderdigtaltwin modelcoupledwithhdraulic-electrical-mechanicalthreedomain.Thestatedimensionisefectivelycompresedto21diesions throughMOC,Kronreductionandmodalsynthesismethods.CombinedwithdeepkernelleamingandresidualKalmanfltering, onlineadaptationanderrorcoectionofmodelparametersareachieved.Inaddition,inordertoensurethesafetyofthsystem undermodeluncertaintyorextremedisturbances，thispaperdesignsanadaptiveswitchingstrategyundertheconstraintof Safe ReinforcementLearning(Safe-RL).Byembedding thecontrolbarierfunction(CBF)asahardconstraintintotheActor-Cric framework，thestudyrealizesundisturbedswitching betwen LAMPCandSafe-RL whenthemodelconfidence drops.Simulation resultsshowthattheproposedadaptivecontrolalgorithmissuperiortotraditionalcontrolmethodsintermsofpowertracking performance，erorconvergencespeedcontrolinputsmothnessandsystemfrequencydomainstabilityffectivelyenhancingthe robustness,efciency，and safety of hydropower units:in complex operating environments.

Keywords:adaptivecontrol;dual timescale;digital twins;safetyreinforcement learning;model predictivecontrol

在“双碳”目标驱动下，我国新型电力系统正加速向高比例可再生能源演进。（剩余7853字）