针对新能源汇集站跟踪计划出力误差大导致调度性差的问题,提出在新能源汇集站(new energy collection station,NECS)配置共享电池储能(energy storage,ES),并与新能源场站储能协调调控,提高风光集中接入区域整体跟踪计划出力能力。首先...针对新能源汇集站跟踪计划出力误差大导致调度性差的问题,提出在新能源汇集站(new energy collection station,NECS)配置共享电池储能(energy storage,ES),并与新能源场站储能协调调控,提高风光集中接入区域整体跟踪计划出力能力。首先,利用各新能源场站时空互补特性求出新能源汇集站联络线出力计划跟踪误差。然后,基于场站储能状态对各自误差进行一定程度的修正。其次,基于场站修正后的汇集站出力偏差,通过汇集站储能与场站储能协调调用提升计划跟踪能力;此过程中,优先调用汇集站储能,若储能容量不足,联合调用有剩余裕度的场站储能进行二次协调。旨在提升新能源汇集站跟踪计划出力能力的同时,降低弃风弃光率。最后,依据新能源跟踪计划性能指标,通过算例应用验证了所提储能协调运行策略对新能源汇集站计划出力跟踪的有效性。展开更多
Gravity/inertial combination navigation is a leading issue in realizing passive navigation onboard a submarine. A new rotation-fitting gravity matching algorithm, based on the Terrain Contour Matching (TERCOM) algorit...Gravity/inertial combination navigation is a leading issue in realizing passive navigation onboard a submarine. A new rotation-fitting gravity matching algorithm, based on the Terrain Contour Matching (TERCOM) algorithm, is proposed in this paper. The algorithm is based on the principle of least mean-square-error criterion, and searches for a certain matched trajectory that runs parallel to a trace indicated by an inertial navigation system on a gravity base map. A rotation is then made clockwise or counterclockwise through a certain angle around the matched trajectory to look for an optimal matched trajectory within a certain angle span range, and through weighted fitting with another eight suboptimal matched trajectories, the endpoint of the fitted trajectory is considered the optimal matched position. In analysis of the algorithm reliability and matching error, the results from simulation indicate that the optimal position can be obtained effectively in real time, and the positioning accuracy improves by 35% and up to 1.05 nautical miles using the proposed algorithm compared with using the widely employed TERCOM and SITAN methods. Current gravity-aided navigation can benefit from implementation of this new algorithm in terms of better reliability and positioning accuracy.展开更多
In recent years,real-time video streaming has grown in popularity.The growing popularity of the Internet of Things(IoT)and other wireless heterogeneous networks mandates that network resources be carefully apportioned...In recent years,real-time video streaming has grown in popularity.The growing popularity of the Internet of Things(IoT)and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience(QoE)and performance objectives.Most researchers focused on Forward Error Correction(FEC)techniques when attempting to strike a balance between QoE and performance.However,as network capacity increases,the performance degrades,impacting the live visual experience.Recently,Deep Learning(DL)algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks.But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time.To address the previous challenge,this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics.The proposed framework contains modules such as Intelligent Content Extraction Module(ICEM),Channel Status Monitor(CSM),and Adaptive FEC(AFEC).This framework adopts the Cognitive Learning-based Scheduling(CLS)Module,which works on the deep Reinforced Gated Recurrent Networks(RGRN)principle and embeds them along with the FEC to achieve better performances.The complete framework was developed using the Objective Modular Network Testbed in C++(OMNET++),Internet networking(INET),and Python 3.10,with Keras as the front end and Tensorflow 2.10 as the back end.With extensive experimentation,the proposed model outperforms the other existing intelligentmodels in terms of improving the QoE,minimizing the End-to-End Delay(EED),and maintaining the highest accuracy(98%)and a lower Root Mean Square Error(RMSE)value of 0.001.展开更多
文摘针对新能源汇集站跟踪计划出力误差大导致调度性差的问题,提出在新能源汇集站(new energy collection station,NECS)配置共享电池储能(energy storage,ES),并与新能源场站储能协调调控,提高风光集中接入区域整体跟踪计划出力能力。首先,利用各新能源场站时空互补特性求出新能源汇集站联络线出力计划跟踪误差。然后,基于场站储能状态对各自误差进行一定程度的修正。其次,基于场站修正后的汇集站出力偏差,通过汇集站储能与场站储能协调调用提升计划跟踪能力;此过程中,优先调用汇集站储能,若储能容量不足,联合调用有剩余裕度的场站储能进行二次协调。旨在提升新能源汇集站跟踪计划出力能力的同时,降低弃风弃光率。最后,依据新能源跟踪计划性能指标,通过算例应用验证了所提储能协调运行策略对新能源汇集站计划出力跟踪的有效性。
基金supported by National Natural Science Foundation of China (Grant Nos. 41074051, 41021003 and 40874037)
文摘Gravity/inertial combination navigation is a leading issue in realizing passive navigation onboard a submarine. A new rotation-fitting gravity matching algorithm, based on the Terrain Contour Matching (TERCOM) algorithm, is proposed in this paper. The algorithm is based on the principle of least mean-square-error criterion, and searches for a certain matched trajectory that runs parallel to a trace indicated by an inertial navigation system on a gravity base map. A rotation is then made clockwise or counterclockwise through a certain angle around the matched trajectory to look for an optimal matched trajectory within a certain angle span range, and through weighted fitting with another eight suboptimal matched trajectories, the endpoint of the fitted trajectory is considered the optimal matched position. In analysis of the algorithm reliability and matching error, the results from simulation indicate that the optimal position can be obtained effectively in real time, and the positioning accuracy improves by 35% and up to 1.05 nautical miles using the proposed algorithm compared with using the widely employed TERCOM and SITAN methods. Current gravity-aided navigation can benefit from implementation of this new algorithm in terms of better reliability and positioning accuracy.
文摘In recent years,real-time video streaming has grown in popularity.The growing popularity of the Internet of Things(IoT)and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience(QoE)and performance objectives.Most researchers focused on Forward Error Correction(FEC)techniques when attempting to strike a balance between QoE and performance.However,as network capacity increases,the performance degrades,impacting the live visual experience.Recently,Deep Learning(DL)algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks.But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time.To address the previous challenge,this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics.The proposed framework contains modules such as Intelligent Content Extraction Module(ICEM),Channel Status Monitor(CSM),and Adaptive FEC(AFEC).This framework adopts the Cognitive Learning-based Scheduling(CLS)Module,which works on the deep Reinforced Gated Recurrent Networks(RGRN)principle and embeds them along with the FEC to achieve better performances.The complete framework was developed using the Objective Modular Network Testbed in C++(OMNET++),Internet networking(INET),and Python 3.10,with Keras as the front end and Tensorflow 2.10 as the back end.With extensive experimentation,the proposed model outperforms the other existing intelligentmodels in terms of improving the QoE,minimizing the End-to-End Delay(EED),and maintaining the highest accuracy(98%)and a lower Root Mean Square Error(RMSE)value of 0.001.
