智能电网技术革新：人工智能和数字孪生的新机遇与挑战

香港理工大學電纲現代化研究中心電機及電子工程學系孪生的新机遇与挑战)(公布温室气体减排目标)(数字孪生的应用)(挑战与展望)SmartGrid(智能电网)controllability(集中式大型电厂，具有完全的控制能力)demand(不灵活的负路停运)(智能电网概念及其挑战)SmartGrid(智能电网)(挑战与展望)(大数据分析+云计算+AI)(实时测量数据)NaturalGas(通讯通道)(自动化)(自动化)EQ\*jc3\*hps14\o\al(\s\up2(RO),及)EQ\*jc3\*hps14\o\al(\s\up2(NGI),工)·SmartGrid(智能电网).DigitalDigitalTwinApplicaExtremeweatherandclimatTechnologicalchallenges:Sensors;ICTs(5G&CybersecuAdvancedoptimization;AutomationandRobotics.(技术挑战：传感器；ICT(5G和网络安全)、物联Threemajortrends:Electrification;DecentralizatOtherkeytechnologieInteractionwithotherinf(数字孪生)EQ\*jc3\*hps13\o\al(\s\up6(ELECTRONI),電棚及電)EQ\*jc3\*hps13\o\al(\s\up6(ENGINEERI),子工程翠)AI寒冬“达特茅斯会议”SmartGrid(智能电网)(AI和数字孪生)大数据“DartmouthConference”power.poortechniques,big(AI在智能电网中的应用第五代计算机tAlphaCio...(什么是AI?)machines,especiallycomputersystems.(由机器Focusesoncognitiveskills:Learning;Reasoning;Examples:rules-basedsystems,neuraStrongAIvs.WeakAI(强人工智能VS弱人工智能)Centralprocessingunits(Cunits(TPUs)(中央处理器(CPUs);图形处理i)Supervisedlearning;(监督学习)ii)Unsupervisedlearning;and(无监督学习)iii)Reinforcementlearning(强化学习)EQ\*jc3\*hps13\o\al(\s\up3(NG),工)(AI在智能电网中的应用convolutionalneuralnetworks,sreinforcementlearning)arecombinedwithtreeseartechniques.(先进的机器学习技术(如卷积神经网络、监督学习、强化学习等)与树搜索算法相结合)Hui(2p)werebeatenbyAlphaGo.(人类选石(9段)、范辉(2段))被击败)Machineleaming(人类专家及自生成对局)的辅助决策)Machinelearning-basedreducuttingoffnumerousleavesofthetreemaintainingvisualqualitymaintainingvisualqualityandadherencetotheuser'sprompt(于2024年发布的一种文本转视频模型，能AlanguagemodelreleasedbyOpenAIin2022,capableofinteractinAnswerquestions(e.g.,factualquestion)(问题答复(例如，现实问题))Generatetexts(e.g.,studentessays)(文档生产(例如，学生课题)(系统模拟(例如，Linux系统.))EQ\*jc3\*hps20\o\al(\s\up0(st),…)(系统模拟(例如，Linux系统.))·DigitalTwin(数字孪生)DigitalDigitalTwinApplicaThesuccessofChatGPT/SoracomesfromAItechniques(e.g.,deepneuralnetworksandreinforcemeDeepLearningMethods(深度学习方法)AdvantagesAdvantages(优势):(深度神经网络)(深度神经网络)(新兴人工智能)(经济与金融)(医疗保健)(联邦学习)(边缘计算)(物理信息神经网络)(快速求解微分方程)EQ\*jc3\*hps13\o\al(\s\up7(EL),電)EQ\*jc3\*hps13\o\al(\s\up7(CTRONI),楼及重)EQ\*jc3\*hps13\o\al(\s\up7(E),子)EQ\*jc3\*hps13\o\al(\s\up7(NGI),工)EQ\*jc3\*hps13\o\al(\s\up7(NE),程)EQ\*jc3\*hps13\o\al(\s\up7(ERI),学)EQ\*jc3\*hps13\o\al(\s\up7(NG),系)(2021年，谷歌宣布推出供应链孪生，这是一个新的谷歌云EQ\*jc3\*hps21\o\al(\s\up7(决),A)EQ\*jc3\*hps21\o\al(\s\up7(案),pre)EQ\*jc3\*hps21\o\al(\s\up7(构),tat)EQ\*jc3\*hps21\o\al(\s\up7(建),ion)EQ\*jc3\*hps21\o\al(\s\up7(数),of)EQ\*jc3\*hps21\o\al(\s\up7(字孪生),theirph)AIandDigitalTwinByorganizingd(AI和数字孪生)inventories,andevent(AI在智能电网中的应用(挑战与展望)1)(挑战与展望)1)Real-timeReplication(实withouttherisksassociatedwithtestingintherealworld.Th·DigitalTwin(数字孪生)integratingandprovidingthecloud-basedservices(数字maintenance,real-tiANALYSTPhasorMeasurement·ExtractingpowerdisaggregationofappliancesandtheironsetsmeasurementdataandAImethods.(基于测量数据和人工智能方法提取设备的功率分解及其开启和关闭时间)Monitoring(ILM).(非侵入式负载监测(NILM),与侵入式负载监测(ILM)相对)DigitalTwinApplica(挑战与展望)Approximate(功率预测)EquipmentMonitoring(设备监测)(稳定性分析)residents'behaviorsresidents'behaviors(居存储需求))模型在香港或亚洲可澳大利亚或欧洲(共29个模型在香港或亚洲可DigitalTwinApplica(挑战与展望)来源)bottoms(independentexperts)ins容易升级，因为它利用了多个共享底层(独立专家),而不是使用单一架构)State:On/State:On/OffExpert:DNNTrmTrmLiang,Z,Yang,H,Zhang.W.,andChungC.Y(2023)AHeterogeneous-of-ExpertApproachforLow-FrequencyNon-IntTestingCases (UK-DALE(英国家庭电器级电力):五个房间)(挑战与展望)(挑战与展望)X√Multi-steppredictions.√Multi-steppredictions.√Hierarchicalpredic√Probabilisticpredictions.(挑战与展望)·TheBayesianlearningst的不确定性并改进概率预测)aaWang.B.Mazhan,M,&Chung.C.YnovehybidmetodforshorttemprobabisicloadforWang.B.Mazhan,M,&Chung.C.YEQ\*jc3\*hps13\o\al(\s\up10(ELECTR),ELECTR)EQ\*jc3\*hps13\o\al(\s\up10(CA),N)EQ\*jc3\*hps13\o\al(\s\up10(LAND),CENG)>Opportunity2:Transferlearningfornewloadsandrenewables(机遇2:用·ThetransferlearningstrategycDigitalTwinApplicadataofDigitalTwinApplicaChallenges&ProspectsChallenges&ProspectsI.PrepreloFeanreexractorml×nLfonandpobocspop(ondpodocdoninfiossamples.Trainsegiuence-tosequnezceachersarialdznaznackuplatiowranchiechareⅢLGeneratequantilepredhetions.yLayerclasificationzDemcrndprecdictr:n×nDomainchassifier:m²×nReversalFullyConnectProbablisnicnewDowamwwXXAIMethods(AIMethods(AI方法)(电动汽车时空充电需求)的时间需求)andweatherconditions(orphysicalpro预测可再生能源发电(例如，风能和太阳能))√Numericalweatherpredictions√Numericalweatherpredictions.(数值天气预测)predictions.(发电预测)powerpowersolarpowerwindDistributedsolarpower(挑战与展望)EQ\*jc3\*hps13\o\al(\s\up3(EL),電)EQ\*jc3\*hps13\o\al(\s\up3(CTRONICENG),機及電子工)EQ\*jc3\*hps13\o\al(\s\up3(NEER),程攀)EQ\*jc3\*hps13\o\al(\s\up3(NG),系)(挑战与展望)(边缘计算中的污垢功率损失估计)(AI在智能电网中的应用i8blackofhildesDatasetofDatasetof(可再生能源预测和发电调度)EQ\*jc3\*hps13\o\al(\s\up3(EC),棚)EQ\*jc3\*hps13\o\al(\s\up3(RONICENG),及電子工)EQ\*jc3\*hps13\o\al(\s\up3(NEER),程攀)EQ\*jc3\*hps13\o\al(\s\up3(NG),系)(挑战与展望)(AI在智能电网中的应用)(储能设备(电池、燃料电池、超级电容器等))(储能设备(电池、燃料电池、超级电容器等))(电力电子设备)(电力电子设备)(挑战与展望)(太阳能电池板)(风力涡轮机)(太阳能电池板)EQ\*jc3\*hps31\o\al(\s\up8(ePredictionofEQ\*jc3\*hps31\o\al(\s\up8(ePredictionofPo),力电子寿命预测)powerPDFof(循环次数)(RUL预测)(监测时间)(历史数据)L(挑战与展望)(挑战与展望)EQ\*jc3\*hps13\o\al(\s\up9(TR),T及R)EQ\*jc3\*hps13\o\al(\s\up9(CA),罩N)可以与先进的传感器结合，分析多维信可以与先进的传感器结合，分析多维信号以进行光伏电池板的异常检测)CurrentsensorVoltagese稳态湿度)Steadyhumidity(挑战与展望)ResearchCentrefor(故障检测准确率>96%)DigitalTwinApplicaMillionsofMillionsof((AI在智能电网中的应用(Expensiveandscarce)(efficent(b)TransferfearninEQ\*jc3\*hps11\o\al(\s\up3(C),s)EQ\*jc3\*hps11\o\al(\s\up3(h),0)EQ\*jc3\*hps11\o\al(\s\up3(a),g)EQ\*jc3\*hps11\o\al(\s\up3(rg),m)EQ\*jc3\*hps11\o\al(\s\up3(ng),nt)TianJ,MaL,ZhangT,HanT,MaiW,reducedataTianJ,MaL,ZhangT,HanT,MaiW,DigitalTwinApplicaConnectionConnectionmatrix(连接矩阵)>Cyber>CyberAttacksDetection(网络攻击检测)(挑战与展望)·Environmentalimpact:tracki(环境影响：追踪并减少电网运作中的温室气体排放)·Extremeweathercondi(灾中紧急资源分配)EQ\*jc3\*hps13\o\al(\s\up6(NGI),工)(AI在智能电网中的应用(数字孪生的应用)Substantialintegrationofcomplexandelectronicdevices,EVsandsoon.(大量集成复杂且新兴的组设备、电动车等)Determineon/offstateofunits(确定机f(机组的功率调整)(引言)(引言)(AI在智能电网中的应用(AI在智能电网中的应用口Selflearning(自我学习)口Doesnotrequiredata(不需要数据)DeepLearningDeepLearning(DL)(深度学习)(功率预测)(设备监测)(常规运行)(稳定性分析)(物理信息神经网络(PINN)):(挑战与展望)(挑战与展望)Physics-informedPhysics-informedneuralnetworks(PINN)(物理信息神经网络(PINN)EEQ\*jc3\*hps13\o\al(\s\up7(GI),工)RIG(常规运行)LLossoptimizationtasksusingphysics-informedneuralcomputingScienificReports,14(1),202.byextremeweathereventslikehurricanes,wildfipoweroutagesaffectmillionsofpeopleeachyear.PowerfailureshavecascadiMmeasures.SustainableEnergyResearch,11(4),1-28.FourFourPhasesofEmergencyManagement(紧急管理的四个阶段)Emergencyresponseofelectricgridsencompassesfouprediction,hardeningandresilienceinvestment,correctiveactionsandemergeResillenceandEmergflracihgtotionmeMobieT &Operational&OperationalEfficiencyImprovement(紧急运行)NecessityforAIapplication(稳定性分析中应用AI的必要性) TominethedatacorrelationintransientproblemsusingAIistorevealthepCharacteristicofthetransientprobleDigitalTwinApplica(物理机制)dMethod(方法)Information(相关信息)Application(应用)Challenges(挑战)(AI在智能电网中的应用)EL,DL..Reducethenegativeimpacto(大样本数据，多场景，多参数，thepresetdata(减少实际数据btainingdata(数据预处理和多代理)预设数据不一致时分析精度的后处理，为获取数据提供适负面影响)当的环境建模)(功率预测)(设备监测)FaststabilityeEQ\*jc3\*hps21\o\al(\s\up7(S),S)EQ\*jc3\*hps21\o\al(\s\up7(e),a)EQ\*jc3\*hps21\o\al(\s\up7(th),法)EQ\*jc3\*hps21\o\al(\s\up11(l),s)EQ\*jc3\*hps21\o\al(\s\up10(mo),no)EQ\*jc3\*hps21\o\al(\s\up9(nt),暂略)EQ\*jc3\*hps21\o\al(\s\up12(o),o)(系统运行)(稳定性分析)仿真)equations(系统响应基于数值和微分方程)methods(主流方法，其他方法的criterion(模拟精度、速度和准确性标准)暂态不稳定性标准)统建模，数据空缺和不准确，计算负担)边界)Usethedata(voltage,rotoforbefore-,during-,andThesefeaturesmaynolongerNetworkNetworkSgenerators(重要同(WPPs(WPPs的等效电路)(稳定性分析)DigitalTwinApplicaSA(挑战与展望)(挑战与展望)EQ\*jc3\*hps13\o\al(\s\up3(NGI),工)个渗透率模拟7000个案例)ComparisonsofComparisonsoftheaccuracyofdifferentfeatures(不同特征准确性的对比)DigitalTwinApplica面积准则(EEAC)指数(SI))WIC:WindpowerInstalledCapacityratio(*W