I.I NTRODUCTION W ITH the advent of low-carbon economy,there has been a growing interest in harnessing renewable energy resources particularly for electricity generation.Renewable energy resources are advocated for th...I.I NTRODUCTION W ITH the advent of low-carbon economy,there has been a growing interest in harnessing renewable energy resources particularly for electricity generation.Renewable energy resources are advocated for the economic and environ-展开更多
At present, large centrifugal compressors are widely used in domestic petrochemical plants. This paper describes the control status of compressor control system in domestic petrochemical plants, the optimization schem...At present, large centrifugal compressors are widely used in domestic petrochemical plants. This paper describes the control status of compressor control system in domestic petrochemical plants, the optimization scheme to realize energy-saving control and the economic benefits brought to enterprises after control optimization.展开更多
This paper considers minimization of resistive and frictional power dissipation in a separately excited DC motor based incremental motion drive （IMD）. The drive is required to displace a given, fixed load through a ...This paper considers minimization of resistive and frictional power dissipation in a separately excited DC motor based incremental motion drive （IMD）. The drive is required to displace a given, fixed load through a definite angle in specified time, with minimum energy dissipation in the motor windings and minimum frictional losses. Accordingly, an energy optimal （EO） control strategy is proposed in which the motor is first accelerated to track a specific speed profile for a pre-determined optimal time period. Thereafter, both armature and field power supplies are disconnected, and the motor decelerates and comes to a halt at the desired displacement point in the desired total displacement time. The optimal time period for the initial acceleration phase is computed so that the motor stores just enough energy to decelerate to the final position at the specified displacement time. The parameters, such as the moment of inertia and coefficient of friction, which depend on the load and other external conditions, have been obtained using system identification method. Comparison with earlier control techniques is included. The results show that the proposed EO control strategy results in significant reduction of energy losses compared to the existing ones.展开更多
Aiming at the problem of insufficient endurance performance of flapping wing aircraft,a stable attitude control algorithm based on energy optimization and ESO(extended state observer)is designed,which effectively redu...Aiming at the problem of insufficient endurance performance of flapping wing aircraft,a stable attitude control algorithm based on energy optimization and ESO(extended state observer)is designed,which effectively reduces the energy consumption in cruise phase.Firstly,the longitudinal dynamic model of flapping wing aircraft is established,and then the uncertain part of the system and various unknown external disturbances are taken as the total disturbance.ESO module is introduced to observe and track the total disturbance in real time.Therefore,the system is transformed into a series integral system through the total disturbance feedback,and then the energy optimal control law is designed on the base of the transformed system.The numerical simulation results show that,compared with the traditional PID control method,the designed energy optimal control method reduces the average energy consumption by 35.28%.展开更多
Buildings are a major energy consumer and carbon emitter,therefore it is important to improve building energy efficiency to achieve our sustainable development goal.Deep reinforcement learning(DRL),as an advanced buil...Buildings are a major energy consumer and carbon emitter,therefore it is important to improve building energy efficiency to achieve our sustainable development goal.Deep reinforcement learning(DRL),as an advanced building control method,demonstrates great potential for energy efficiency optimization and improved occupant comfort.However,the performance of DRL is highly sensitive to hyper-parameters,and selecting inappropriate hyper-parameters may lead to unstable learning or even failure.This study aims to investigate the design and application of DRL in building energy system control,with a specific focus on improving the performance of DRL controllers through hyper-parameter optimization(HPO)algorithms.It also aims to provide quantitative evaluation and adaptive validation of these optimized controllers.Two widely used algorithms,deep deterministic policy gradient(DDPG)and soft actor-critic(SAC),are used in the study and their performance is evaluated in different building environments based on the BOPTEST virtual testbed.One of the focuses of the study is to compare various HPO techniques,including tree-structured Parzen estimator(TPE),covariance matrix adaptation evolution strategy(CMA-ES),and combinatorial optimization methods,to determine the efficacy of different hyper-parameter optimization methods for DRL.The study enhances HPO efficiency through parallel computation and conducts a comprehensive quantitative assessment of the optimized DRL controllers,considering factors such as reduced energy consumption and improved comfort.The results show that the HPO algorithms significantly improve the performance of the DDPG and SAC controllers.A reduction of 56.94%and 68.74%in thermal discomfort is achieved,respectively.Additionally,the study demonstrates the applicability of the HPO-based approach for enhancing DRL controller performance across diverse building environments,providing valuable insights for the design and optimization of building DRL controllers.展开更多
Improvements in fuel consumption and emissions of hybrid electric vehicle(HEV)heavily depend upon an efficient energy management strategy(EMS).This paper presents an optimizing fuzzy control strategy of parallel hybri...Improvements in fuel consumption and emissions of hybrid electric vehicle(HEV)heavily depend upon an efficient energy management strategy(EMS).This paper presents an optimizing fuzzy control strategy of parallel hybrid electric vehicle em-展开更多
文摘I.I NTRODUCTION W ITH the advent of low-carbon economy,there has been a growing interest in harnessing renewable energy resources particularly for electricity generation.Renewable energy resources are advocated for the economic and environ-
文摘At present, large centrifugal compressors are widely used in domestic petrochemical plants. This paper describes the control status of compressor control system in domestic petrochemical plants, the optimization scheme to realize energy-saving control and the economic benefits brought to enterprises after control optimization.
文摘This paper considers minimization of resistive and frictional power dissipation in a separately excited DC motor based incremental motion drive （IMD）. The drive is required to displace a given, fixed load through a definite angle in specified time, with minimum energy dissipation in the motor windings and minimum frictional losses. Accordingly, an energy optimal （EO） control strategy is proposed in which the motor is first accelerated to track a specific speed profile for a pre-determined optimal time period. Thereafter, both armature and field power supplies are disconnected, and the motor decelerates and comes to a halt at the desired displacement point in the desired total displacement time. The optimal time period for the initial acceleration phase is computed so that the motor stores just enough energy to decelerate to the final position at the specified displacement time. The parameters, such as the moment of inertia and coefficient of friction, which depend on the load and other external conditions, have been obtained using system identification method. Comparison with earlier control techniques is included. The results show that the proposed EO control strategy results in significant reduction of energy losses compared to the existing ones.
文摘Aiming at the problem of insufficient endurance performance of flapping wing aircraft,a stable attitude control algorithm based on energy optimization and ESO(extended state observer)is designed,which effectively reduces the energy consumption in cruise phase.Firstly,the longitudinal dynamic model of flapping wing aircraft is established,and then the uncertain part of the system and various unknown external disturbances are taken as the total disturbance.ESO module is introduced to observe and track the total disturbance in real time.Therefore,the system is transformed into a series integral system through the total disturbance feedback,and then the energy optimal control law is designed on the base of the transformed system.The numerical simulation results show that,compared with the traditional PID control method,the designed energy optimal control method reduces the average energy consumption by 35.28%.
基金supported by the National Natural Science Foundation of China(No.72371072)Jiangsu Association for Science&Technology Youth Science&Technology Talents Lifting Project(No.JSTJ-2023-JS001).
文摘Buildings are a major energy consumer and carbon emitter,therefore it is important to improve building energy efficiency to achieve our sustainable development goal.Deep reinforcement learning(DRL),as an advanced building control method,demonstrates great potential for energy efficiency optimization and improved occupant comfort.However,the performance of DRL is highly sensitive to hyper-parameters,and selecting inappropriate hyper-parameters may lead to unstable learning or even failure.This study aims to investigate the design and application of DRL in building energy system control,with a specific focus on improving the performance of DRL controllers through hyper-parameter optimization(HPO)algorithms.It also aims to provide quantitative evaluation and adaptive validation of these optimized controllers.Two widely used algorithms,deep deterministic policy gradient(DDPG)and soft actor-critic(SAC),are used in the study and their performance is evaluated in different building environments based on the BOPTEST virtual testbed.One of the focuses of the study is to compare various HPO techniques,including tree-structured Parzen estimator(TPE),covariance matrix adaptation evolution strategy(CMA-ES),and combinatorial optimization methods,to determine the efficacy of different hyper-parameter optimization methods for DRL.The study enhances HPO efficiency through parallel computation and conducts a comprehensive quantitative assessment of the optimized DRL controllers,considering factors such as reduced energy consumption and improved comfort.The results show that the HPO algorithms significantly improve the performance of the DDPG and SAC controllers.A reduction of 56.94%and 68.74%in thermal discomfort is achieved,respectively.Additionally,the study demonstrates the applicability of the HPO-based approach for enhancing DRL controller performance across diverse building environments,providing valuable insights for the design and optimization of building DRL controllers.
基金supported by the Natural Science Foundation of Hubei Province(Grant No.2015CFB586)
文摘Improvements in fuel consumption and emissions of hybrid electric vehicle(HEV)heavily depend upon an efficient energy management strategy(EMS).This paper presents an optimizing fuzzy control strategy of parallel hybrid electric vehicle em-
