Manufacturers are striving to achieve higher energy efficiency without compromising production performance and quality standards.Parallel-serial structures,commonly found in modern production systems,offer a unique ba...Manufacturers are striving to achieve higher energy efficiency without compromising production performance and quality standards.Parallel-serial structures,commonly found in modern production systems,offer a unique balance of flexibility and efficiency by combining parallel processes with sequential workflows.However,their inherent complexity poses significant challenges,particularly in optimizing energy efficiency and ensuring consistent product quality.In data-driven manufacturing environments,it is not clear how to leverage production data to enhance the energy efficiency of production systems.Therefore,this paper studied a data-driven approach to improving energy efficiency in parallel-serial production lines with product quality issues.Firstly,the authors developed a data-driven performance analysis method to evaluate the effects of disruption events,such as energy-saving control actions,machine breakdowns,and product quality failures,on system throughput and energy consumption.Secondly,a periodic energy-saving control method was developed to enhance system energy efficiency using a non-linear programming model.To reduce complexity and improve computational efficiency,the model was simplified by leveraging the intrinsic properties of parallel-serial production lines and solved using an adaptive genetic algorithm.Finally,the effectiveness of the proposed data-driven approach was validated through case studies,providing actionable insights into achieving data-driven energy efficiency optimization in complex production systems.展开更多
The development of non-linear loads at consumers has significantly impacted power supply systems.Since,the poor power quality has been found in the three-phase distribution system due to unbalanced loads,harmonic curre...The development of non-linear loads at consumers has significantly impacted power supply systems.Since,the poor power quality has been found in the three-phase distribution system due to unbalanced loads,harmonic current,undesired voltage regulation,and extreme reactive power demand.To overcome this issue,Distributed STATicCOMpensator(DSTATCOM)is implemented.DSTATCOM is a shunt-connected Voltage Source Converter(VSC)that has been utilized in distribution networks to balance the bus voltage in terms of enhancing reactive power control and power factor.DSTATCOM can provide both rapid and continuous capacitive and inductive mode compensation.A rectified resistive and inductive load eliminates current harmonics in a three-phase power supply.The synchronous fundamental DQ frame is a time-domain approach developed from three-phase system space vector transformations has been designed using MATLAB/Simulink.The DQ theory is used to produce the reference signal for the Pulse Width Modulation(PWM)generator.In addition,a traditional Propor-tional Integral Derivative(PID)controller is designed and compared with pro-posed soft computing approaches such as Fuzzy–PID and Artificial Neural Network(ANN-PID)and compared accurate reference current determination for Direct Current(DC)bus through DC link.An Analytical explores the pro-posed control strategies given to establish superior outcomes.Finally,total harmo-nic distortion analysis should be taken for performance analysis of the proposed system with IEEE standards.展开更多
Objective To solve the problem of the incomplete relief model for vaccine damage events in China,multiple models for large-scale vaccine damage events were established based on the cooperation of product liability,com...Objective To solve the problem of the incomplete relief model for vaccine damage events in China,multiple models for large-scale vaccine damage events were established based on the cooperation of product liability,compulsory insurance and rescue funds.Methods Through case analysis and comparative research,the situation of liability sharing and relief of mass tort of vaccines was analyzed.Results and Conclusion At present,China should construct a multi-relief model of vaccine damage from the aspects of tort liability and social relief.In addition,after the construction of the relief system,it is necessary to distinguish between emergency situations and general situations to apply the priority of tort compensation and social relief systems in the specific implementation process.展开更多
More and more distributed generations (DGs)such as solar,wind,and energy storage,etc,inject into the power grid mainly through the power electronics equipments.Large-scale power electronic equipments connected to the ...More and more distributed generations (DGs)such as solar,wind,and energy storage,etc,inject into the power grid mainly through the power electronics equipments.Large-scale power electronic equipments connected to the grid bring new challenges for traditional power quality.Power quality analysis theory and control technology for smart grid will provide the comprehensive and systematic solutions and approaches in the展开更多
Online demand prediction plays an important role in transport network services from operations,controls to management,and information provision.However,the online prediction models are impacted by streaming data quali...Online demand prediction plays an important role in transport network services from operations,controls to management,and information provision.However,the online prediction models are impacted by streaming data quality issues with noise measurements and missing data.To address these,we develop a robust prediction method for online network-level demand prediction in public transport.It consists of a PCA method to extract eigen demand images and an optimization-based pattern recognition model to predict the weights of eigen demand images by making use of the partially observed real-time data up to the prediction time in a day.The prediction model is robust to data quality issues given that the eigen demand images are stable and the predicted weights of them are optimized using the network level data(less impacted by local data quality issues).In the case study,we validate the accuracy and transferability of the model by comparing it with benchmark models and evaluate the robustness in tolerating data quality issues of the proposed model.The experimental results demonstrate that the proposed Pattern Recognition Prediction based on PCA(PRP-PCA)consistently outperforms other benchmark models in accuracy and transferability.Moreover,the model shows high robustness in accommodating data quality issues.For example,the PRP-PCA model is robust to missing data up to 50%regardless of the noise level.We also discuss the hidden patterns behind the network level demand.The visualization analysis shows that eigen demand images are significantly connected to the network structure and station activity variabilities.Though the demand changes dramatically before and after the pandemic,the eigen demand images are consistent over time in Stockholm.展开更多
基金supported in part by Major Project of the National Social Science Fund of China,under Grant No.23&ZD050in part by National Natural Science Foundation of China(NSFC),under Grant Nos.72402031 and 71971052+2 种基金in part by Open Project Program of State Key Laboratory of Massive Personalized Customization System and Technology,under Grant No.H&C-MPC-2023-04-03in part by the Fundamental Research Funds for the Central Universities,under Grant No.N25ZJL015the Joint Funds of the Natural Science Foundation of Liaoning,under Grant No.2023-BSBA-139.
文摘Manufacturers are striving to achieve higher energy efficiency without compromising production performance and quality standards.Parallel-serial structures,commonly found in modern production systems,offer a unique balance of flexibility and efficiency by combining parallel processes with sequential workflows.However,their inherent complexity poses significant challenges,particularly in optimizing energy efficiency and ensuring consistent product quality.In data-driven manufacturing environments,it is not clear how to leverage production data to enhance the energy efficiency of production systems.Therefore,this paper studied a data-driven approach to improving energy efficiency in parallel-serial production lines with product quality issues.Firstly,the authors developed a data-driven performance analysis method to evaluate the effects of disruption events,such as energy-saving control actions,machine breakdowns,and product quality failures,on system throughput and energy consumption.Secondly,a periodic energy-saving control method was developed to enhance system energy efficiency using a non-linear programming model.To reduce complexity and improve computational efficiency,the model was simplified by leveraging the intrinsic properties of parallel-serial production lines and solved using an adaptive genetic algorithm.Finally,the effectiveness of the proposed data-driven approach was validated through case studies,providing actionable insights into achieving data-driven energy efficiency optimization in complex production systems.
文摘The development of non-linear loads at consumers has significantly impacted power supply systems.Since,the poor power quality has been found in the three-phase distribution system due to unbalanced loads,harmonic current,undesired voltage regulation,and extreme reactive power demand.To overcome this issue,Distributed STATicCOMpensator(DSTATCOM)is implemented.DSTATCOM is a shunt-connected Voltage Source Converter(VSC)that has been utilized in distribution networks to balance the bus voltage in terms of enhancing reactive power control and power factor.DSTATCOM can provide both rapid and continuous capacitive and inductive mode compensation.A rectified resistive and inductive load eliminates current harmonics in a three-phase power supply.The synchronous fundamental DQ frame is a time-domain approach developed from three-phase system space vector transformations has been designed using MATLAB/Simulink.The DQ theory is used to produce the reference signal for the Pulse Width Modulation(PWM)generator.In addition,a traditional Propor-tional Integral Derivative(PID)controller is designed and compared with pro-posed soft computing approaches such as Fuzzy–PID and Artificial Neural Network(ANN-PID)and compared accurate reference current determination for Direct Current(DC)bus through DC link.An Analytical explores the pro-posed control strategies given to establish superior outcomes.Finally,total harmo-nic distortion analysis should be taken for performance analysis of the proposed system with IEEE standards.
文摘Objective To solve the problem of the incomplete relief model for vaccine damage events in China,multiple models for large-scale vaccine damage events were established based on the cooperation of product liability,compulsory insurance and rescue funds.Methods Through case analysis and comparative research,the situation of liability sharing and relief of mass tort of vaccines was analyzed.Results and Conclusion At present,China should construct a multi-relief model of vaccine damage from the aspects of tort liability and social relief.In addition,after the construction of the relief system,it is necessary to distinguish between emergency situations and general situations to apply the priority of tort compensation and social relief systems in the specific implementation process.
文摘More and more distributed generations (DGs)such as solar,wind,and energy storage,etc,inject into the power grid mainly through the power electronics equipments.Large-scale power electronic equipments connected to the grid bring new challenges for traditional power quality.Power quality analysis theory and control technology for smart grid will provide the comprehensive and systematic solutions and approaches in the
文摘Online demand prediction plays an important role in transport network services from operations,controls to management,and information provision.However,the online prediction models are impacted by streaming data quality issues with noise measurements and missing data.To address these,we develop a robust prediction method for online network-level demand prediction in public transport.It consists of a PCA method to extract eigen demand images and an optimization-based pattern recognition model to predict the weights of eigen demand images by making use of the partially observed real-time data up to the prediction time in a day.The prediction model is robust to data quality issues given that the eigen demand images are stable and the predicted weights of them are optimized using the network level data(less impacted by local data quality issues).In the case study,we validate the accuracy and transferability of the model by comparing it with benchmark models and evaluate the robustness in tolerating data quality issues of the proposed model.The experimental results demonstrate that the proposed Pattern Recognition Prediction based on PCA(PRP-PCA)consistently outperforms other benchmark models in accuracy and transferability.Moreover,the model shows high robustness in accommodating data quality issues.For example,the PRP-PCA model is robust to missing data up to 50%regardless of the noise level.We also discuss the hidden patterns behind the network level demand.The visualization analysis shows that eigen demand images are significantly connected to the network structure and station activity variabilities.Though the demand changes dramatically before and after the pandemic,the eigen demand images are consistent over time in Stockholm.
