This study presents an improved data-driven Model-Free Adaptive Control(MFAC)strategy for attitude stabilization of a partially constrained combined spacecraft with external disturbances and input saturation. First, a...This study presents an improved data-driven Model-Free Adaptive Control(MFAC)strategy for attitude stabilization of a partially constrained combined spacecraft with external disturbances and input saturation. First, a novel dynamic linearization data model for the partially constrained combined spacecraft with external disturbances is established. The generalized disturbances composed of external disturbances and dynamic linearization errors are then reconstructed by a Discrete Extended State Observer(DESO). With the dynamic linearization data model and reconstructed information, a DESO-MFAC strategy for the combined spacecraft is proposed based only on input and output data. Next, the input saturation is overcome by introducing an antiwindup compensator. Finally, numerical simulations are carried out to demonstrate the effectiveness and feasibility of the proposed controller when the dynamic properties of the partially constrained combined spacecraft are completely unknown.展开更多
A combined system including a solid oxide fuel cell(SOFC)and an internal combustion engine(ICE)is proposed in this paper.First,a 0-D model of SOFC and a 1-D model of ICE are built as agent models.Second,parameter anal...A combined system including a solid oxide fuel cell(SOFC)and an internal combustion engine(ICE)is proposed in this paper.First,a 0-D model of SOFC and a 1-D model of ICE are built as agent models.Second,parameter analysis of the system is conducted based on SOFC and ICE models.Results show that the number of cells,current density,and fuel utilization can influence SOFC and ICE.Moreover,a deep neural network is applied as a data-driven model to conduct optimized calculations efficiently,as achieved by the particle swarm optimization algorithm in this paper.The results demonstrate that the optimal system efficiency of 51.8%can be achieved from a 22.4%/77.6%SOFC-ICE power split at 6000 kW power output.Furthermore,promising improvements in efficiency of 5.1%are achieved compared to the original engine.Finally,a simple economic analysis model,which shows that the payback period of the optimal system is 8.41 years,is proposed in this paper.展开更多
To manage a large amount of flexible distributed energy resources(DERs)in the distribution networks,the virtual power plant(VPP)is introduced into the industry.The VPP can optimally dispatch these resources in a clust...To manage a large amount of flexible distributed energy resources(DERs)in the distribution networks,the virtual power plant(VPP)is introduced into the industry.The VPP can optimally dispatch these resources in a cluster manner and provide flexibility for the power system operation as a whole.Most existing studies formulate the equivalent power flexibility of the aggregating DERs as deterministic optimization models without considering their uncertainties.In this paper,we introduce the stochastic power flexibility range(PFR)and timecoupling flexibility(TCF)to describe the power flexibility of VPP.In this model,both operational constraints and the randomness of the DERs’output are incorporated,and a combined model and data-driven solution is proposed to obtain the stochastic PFR,TCF,and cost function of VPP.The aggregating model can be easily incorporated into the optimization model for the power system operator or market bidding,considering uncertainties.Finally,a numerical test is performed.The results show that the proposed model not only has higher computational efficiency than the scenario-based methods but also achieves more economic benefits.展开更多
The rapid development of digital technolo-gy has fundamentally changed the ways we live,work,and study.Digital education has gradually emerged under the influence of social change,technological advancements,global com...The rapid development of digital technolo-gy has fundamentally changed the ways we live,work,and study.Digital education has gradually emerged under the influence of social change,technological advancements,global competition,and innovative ed-ucational practice.Digital education is not just a sim-ple application of digital technology in education but a new educational paradigm.It builds a more equitable,higher-quality,environmentally friendly,and openly cooperative new education system through data-driven methods,human-technology integration,the combi-nation of virtual and real elements,and open sharing.Developing digital education involves focusing on sce-narios,resources,models,evaluation,and digital litera-cy.China has made significant progress in developing digital education,accumulating valuable experience that can inform the continued and prosperous growth of digital education worldwide.While acknowledg-ing the advantages that digitalization brings to teach-ing,evaluation,and management,we also need to be aware of the risks and challenges it brings to data secu-rity,privacy protection,ethical issues,and humanistic concerns.展开更多
基金supported by National Natural Science Foundation of China(Nos.61603114,61673135)the Fundamental Research Funds for the Central Universities of China(No.HIT.NSRIF.201826)
文摘This study presents an improved data-driven Model-Free Adaptive Control(MFAC)strategy for attitude stabilization of a partially constrained combined spacecraft with external disturbances and input saturation. First, a novel dynamic linearization data model for the partially constrained combined spacecraft with external disturbances is established. The generalized disturbances composed of external disturbances and dynamic linearization errors are then reconstructed by a Discrete Extended State Observer(DESO). With the dynamic linearization data model and reconstructed information, a DESO-MFAC strategy for the combined spacecraft is proposed based only on input and output data. Next, the input saturation is overcome by introducing an antiwindup compensator. Finally, numerical simulations are carried out to demonstrate the effectiveness and feasibility of the proposed controller when the dynamic properties of the partially constrained combined spacecraft are completely unknown.
文摘A combined system including a solid oxide fuel cell(SOFC)and an internal combustion engine(ICE)is proposed in this paper.First,a 0-D model of SOFC and a 1-D model of ICE are built as agent models.Second,parameter analysis of the system is conducted based on SOFC and ICE models.Results show that the number of cells,current density,and fuel utilization can influence SOFC and ICE.Moreover,a deep neural network is applied as a data-driven model to conduct optimized calculations efficiently,as achieved by the particle swarm optimization algorithm in this paper.The results demonstrate that the optimal system efficiency of 51.8%can be achieved from a 22.4%/77.6%SOFC-ICE power split at 6000 kW power output.Furthermore,promising improvements in efficiency of 5.1%are achieved compared to the original engine.Finally,a simple economic analysis model,which shows that the payback period of the optimal system is 8.41 years,is proposed in this paper.
基金supported in part by the National Natural Science Foundation of China under Grant U2066601,51725703Southern Power Grid Technical Project GDKJXM20185069(032000KK52180069).
文摘To manage a large amount of flexible distributed energy resources(DERs)in the distribution networks,the virtual power plant(VPP)is introduced into the industry.The VPP can optimally dispatch these resources in a cluster manner and provide flexibility for the power system operation as a whole.Most existing studies formulate the equivalent power flexibility of the aggregating DERs as deterministic optimization models without considering their uncertainties.In this paper,we introduce the stochastic power flexibility range(PFR)and timecoupling flexibility(TCF)to describe the power flexibility of VPP.In this model,both operational constraints and the randomness of the DERs’output are incorporated,and a combined model and data-driven solution is proposed to obtain the stochastic PFR,TCF,and cost function of VPP.The aggregating model can be easily incorporated into the optimization model for the power system operator or market bidding,considering uncertainties.Finally,a numerical test is performed.The results show that the proposed model not only has higher computational efficiency than the scenario-based methods but also achieves more economic benefits.
基金supported by“An International Comparative Study on the Digital Transformation of Education”,a Major Program of the National Social Science Fund of the Ministry of Education of the People’s Republic of China for the year 2022(No.22JZD045).
文摘The rapid development of digital technolo-gy has fundamentally changed the ways we live,work,and study.Digital education has gradually emerged under the influence of social change,technological advancements,global competition,and innovative ed-ucational practice.Digital education is not just a sim-ple application of digital technology in education but a new educational paradigm.It builds a more equitable,higher-quality,environmentally friendly,and openly cooperative new education system through data-driven methods,human-technology integration,the combi-nation of virtual and real elements,and open sharing.Developing digital education involves focusing on sce-narios,resources,models,evaluation,and digital litera-cy.China has made significant progress in developing digital education,accumulating valuable experience that can inform the continued and prosperous growth of digital education worldwide.While acknowledg-ing the advantages that digitalization brings to teach-ing,evaluation,and management,we also need to be aware of the risks and challenges it brings to data secu-rity,privacy protection,ethical issues,and humanistic concerns.
