Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power li...Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power line communication(DC-PLC)enables real-time data transmission on DC power lines.With traffic adaptation,DC-PLC can be integrated with other complementary media such as 5G to reduce transmission delay and improve reliability.However,traffic adaptation for DC-PLC and 5G integration still faces the challenges such as coupling between traffic admission control and traffic partition,dimensionality curse,and the ignorance of extreme event occurrence.To address these challenges,we propose a deep reinforcement learning(DRL)-based delay sensitive and reliable traffic adaptation algorithm(DSRTA)to minimize the total queuing delay under the constraints of traffic admission control,queuing delay,and extreme events occurrence probability.DSRTA jointly optimizes traffic admission control and traffic partition,and enables learning-based intelligent traffic adaptation.The long-term constraints are incorporated into both state and bound of drift-pluspenalty to achieve delay awareness and enforce reliability guarantee.Simulation results show that DSRTA has lower queuing delay and more reliable quality of service(QoS)guarantee than other state-of-the-art algorithms.展开更多
为解决工业现场通信网络普遍存在的协议碎片化、拓扑灵活性不足及实时性与带宽难以兼顾等问题,设计了一种基于可编程逻辑控制器(Programmable Logic Controller,PLC)与分布式控制系统(Distributed Control System,DCS)的工业现场通信网...为解决工业现场通信网络普遍存在的协议碎片化、拓扑灵活性不足及实时性与带宽难以兼顾等问题,设计了一种基于可编程逻辑控制器(Programmable Logic Controller,PLC)与分布式控制系统(Distributed Control System,DCS)的工业现场通信网络系统。该系统通过整合多协议网关与冗余控制架构,实现了异构协议的协同工作,并采用混合拓扑结构与优先级队列机制,有效提升了数据传输的可靠性与网络扩展能力。测试结果表明,该系统能够降低协议转换错误率和传输延迟,提高带宽利用率,为复杂工业环境下的设备互联与数据交互提供了技术支撑。展开更多
基金supported by the Science and Technology Project of State Grid Corporation of China under grant 52094021N010(5400-202199534A-0-5-ZN)。
文摘Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power line communication(DC-PLC)enables real-time data transmission on DC power lines.With traffic adaptation,DC-PLC can be integrated with other complementary media such as 5G to reduce transmission delay and improve reliability.However,traffic adaptation for DC-PLC and 5G integration still faces the challenges such as coupling between traffic admission control and traffic partition,dimensionality curse,and the ignorance of extreme event occurrence.To address these challenges,we propose a deep reinforcement learning(DRL)-based delay sensitive and reliable traffic adaptation algorithm(DSRTA)to minimize the total queuing delay under the constraints of traffic admission control,queuing delay,and extreme events occurrence probability.DSRTA jointly optimizes traffic admission control and traffic partition,and enables learning-based intelligent traffic adaptation.The long-term constraints are incorporated into both state and bound of drift-pluspenalty to achieve delay awareness and enforce reliability guarantee.Simulation results show that DSRTA has lower queuing delay and more reliable quality of service(QoS)guarantee than other state-of-the-art algorithms.
文摘为解决工业现场通信网络普遍存在的协议碎片化、拓扑灵活性不足及实时性与带宽难以兼顾等问题,设计了一种基于可编程逻辑控制器(Programmable Logic Controller,PLC)与分布式控制系统(Distributed Control System,DCS)的工业现场通信网络系统。该系统通过整合多协议网关与冗余控制架构,实现了异构协议的协同工作,并采用混合拓扑结构与优先级队列机制,有效提升了数据传输的可靠性与网络扩展能力。测试结果表明,该系统能够降低协议转换错误率和传输延迟,提高带宽利用率,为复杂工业环境下的设备互联与数据交互提供了技术支撑。
