In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,e...In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,emergency communication,and real-time scheduling,demands advanced capabilities in real-time perception,automated driving,and digitized services,which accelerate the integration and application of Artificial Intelligence(AI)in the HSR system.This paper first provides a brief overview of AI,covering its origin,evolution,and breakthrough applications.A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system:mechanical manufacturing and electrical control,communication and signal control,and transportation management.The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components,forecast of railroad maintenance,optimization of energy consumption in railroads and trains,communication security,communication dependability,channel modeling and estimation,passenger scheduling,traffic flow forecasting,high-speed railway smart platform.Finally,challenges associated with the application of AI are discussed,offering insights for future research directions.展开更多
Microgrids are gaining popularity by facilitating distributed energy resources(DERs)and forming essential consumer/prosumer centric integrated energy systems.Integration,coordination and control of multiple DERs and m...Microgrids are gaining popularity by facilitating distributed energy resources(DERs)and forming essential consumer/prosumer centric integrated energy systems.Integration,coordination and control of multiple DERs and managing the energy transition in this environment is a strenuous task.Classical control techniques are not enough to support dynamic microgrid environments.Implementation of Artificial Intelligence(AI)techniques seems to be a promising solution to enhance the control and operation of microgrids in future smart grid networks.Therefore,this paper briefly reviews the control architectures,existing conventional controlling techniques,their drawbacks,the need for intelligent controllers and then extensively reviews the possibility of AI implementation in different control structures with a special focus on the hierarchical control layers.This paper also investigates the AI-based control strategies in networked/interconnected/multi-microgrids environments.It concludes with the summary and future scopes of AI implementation in hierarchical control layers and structures including single and networked microgrids environments.展开更多
Reinforcement learning(RL)has shown significant success in sequential decision making in fields like autonomous vehicles,robotics,marketing and gaming industries.This success has attracted the attention to the RL cont...Reinforcement learning(RL)has shown significant success in sequential decision making in fields like autonomous vehicles,robotics,marketing and gaming industries.This success has attracted the attention to the RL control approach for building energy systems which are becoming complicated due to the need to optimize for multiple,potentially conflicting,goals like occupant comfort,energy use and grid interactivity.However,for real world applications,RL has several drawbacks like requiring large training data and time,and unstable control behavior during the early exploration process making it infeasible for an application directly to building control tasks.To address these issues,an imitation learning approach is utilized herein where the RL agents starts with a policy transferred from accepted rule based policies and heuristic policies.This approach is successful in reducing the training time,preventing the unstable early exploration behavior and improving upon an accepted rule-based policy-all of these make RL a more practical control approach for real world applications in the domain of building controls.展开更多
The advancement in power distribution system poses a great challenge to power engineering researchers on how to best monitor and estimate the state of the distribution network. This paper is exe,：uted in two stage pr...The advancement in power distribution system poses a great challenge to power engineering researchers on how to best monitor and estimate the state of the distribution network. This paper is exe,：uted in two stage processes. The first stage is to identify the optimal location for installation of monitoring instrument with minimal investment cost. The second stage is to estimate the bus voltage magnitude, where real time measurement is conducted and measured through identified meter location which is more essential for decision making in distribution supervisory control and data acquisition system （DSCADA）. The hybrid intelligent technique is applied to execute the above two algorithms. The algorithms are tested with institute of electrical and electronics engineers （IEEE） and Tamil Nadu electricity board （TNEB） bench- mark systems. The simulated results proves that the swarm tuned artificial neural network （ANN） estimator is best suited for accurate estimation of voltage with different noise levels.展开更多
基金supported by the National Natural Science Foundation of China(62172033).
文摘In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,emergency communication,and real-time scheduling,demands advanced capabilities in real-time perception,automated driving,and digitized services,which accelerate the integration and application of Artificial Intelligence(AI)in the HSR system.This paper first provides a brief overview of AI,covering its origin,evolution,and breakthrough applications.A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system:mechanical manufacturing and electrical control,communication and signal control,and transportation management.The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components,forecast of railroad maintenance,optimization of energy consumption in railroads and trains,communication security,communication dependability,channel modeling and estimation,passenger scheduling,traffic flow forecasting,high-speed railway smart platform.Finally,challenges associated with the application of AI are discussed,offering insights for future research directions.
文摘Microgrids are gaining popularity by facilitating distributed energy resources(DERs)and forming essential consumer/prosumer centric integrated energy systems.Integration,coordination and control of multiple DERs and managing the energy transition in this environment is a strenuous task.Classical control techniques are not enough to support dynamic microgrid environments.Implementation of Artificial Intelligence(AI)techniques seems to be a promising solution to enhance the control and operation of microgrids in future smart grid networks.Therefore,this paper briefly reviews the control architectures,existing conventional controlling techniques,their drawbacks,the need for intelligent controllers and then extensively reviews the possibility of AI implementation in different control structures with a special focus on the hierarchical control layers.This paper also investigates the AI-based control strategies in networked/interconnected/multi-microgrids environments.It concludes with the summary and future scopes of AI implementation in hierarchical control layers and structures including single and networked microgrids environments.
基金This work was authored in part by the National Renewable Energy Laboratory,United States,operated by Alliance for Sustainable Energy,LLC,for the U.S.Department of Energy(DOE)under Contract No.DE-AC36-08GO28308.
文摘Reinforcement learning(RL)has shown significant success in sequential decision making in fields like autonomous vehicles,robotics,marketing and gaming industries.This success has attracted the attention to the RL control approach for building energy systems which are becoming complicated due to the need to optimize for multiple,potentially conflicting,goals like occupant comfort,energy use and grid interactivity.However,for real world applications,RL has several drawbacks like requiring large training data and time,and unstable control behavior during the early exploration process making it infeasible for an application directly to building control tasks.To address these issues,an imitation learning approach is utilized herein where the RL agents starts with a policy transferred from accepted rule based policies and heuristic policies.This approach is successful in reducing the training time,preventing the unstable early exploration behavior and improving upon an accepted rule-based policy-all of these make RL a more practical control approach for real world applications in the domain of building controls.
文摘The advancement in power distribution system poses a great challenge to power engineering researchers on how to best monitor and estimate the state of the distribution network. This paper is exe,：uted in two stage processes. The first stage is to identify the optimal location for installation of monitoring instrument with minimal investment cost. The second stage is to estimate the bus voltage magnitude, where real time measurement is conducted and measured through identified meter location which is more essential for decision making in distribution supervisory control and data acquisition system （DSCADA）. The hybrid intelligent technique is applied to execute the above two algorithms. The algorithms are tested with institute of electrical and electronics engineers （IEEE） and Tamil Nadu electricity board （TNEB） bench- mark systems. The simulated results proves that the swarm tuned artificial neural network （ANN） estimator is best suited for accurate estimation of voltage with different noise levels.
