面向智能电力巡检的高可靠低时延移动边缘计算技术  

Ultra-reliable and Low-latency Mobile Edge Computing Technology for Intelligent Power Inspection

作　　者：周振宇[1] 陈亚鹏[1] 潘超[1] 赵雄文[1] 张磊 汪中原 ZHOU Zhenyu;CHEN Yapeng;PAN Chao;ZHAO Xiongwen;ZHANG Lei;WANG Zhongyuan(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,North China Electric Power University,Beijing 102206,China;State Grid Shandong Electric Power Research Institute,Jinan 250003,China;CSG Smart Science&Technology Co.,Ltd.,Shanghai 300222,China)

机构地区：[1]华北电力大学新能源电力系统国家重点实验室,北京102206 [2]国网山东省电力公司电力科学研究院,济南250003 [3]科大智能科技股份有限公司,上海300222

出　　处：《高电压技术》2020年第6期1895-1902,共8页High Voltage Engineering

基　　金：国家自然科学基金(61971189);国家电网有限公司科技项目(SGSDDK00KJJS1900405);新能源电力系统国家重点实验室探索项目(LAPS2019-12)。

摘　　要：移动边缘计算为满足巡检机器人爆发式增长的通信和计算需求提供了一种有前景的架构,巡检机器人可将采集的高清视频传输到临近的边缘服务器进行数据处理和设备状态研判。然而,全局信息缺失、电池容量受限、高可靠低时延通信约束等对任务卸载优化提出了挑战。考虑对任务卸载而言至关重要的信道选择问题。基于强化学习和李雅普诺夫优化,提出了一种联合能量感知、高可靠低时延通信感知和任务优先级感知的信道选择算法。该算法在全局信息未知的情况下,动态优化信道选择策略,在最大程度满足长期能耗与高可靠低时延通信约束的同时实现巡检机器人效用最大化。并利用变电站实测数据得到的信道模型和电磁干扰模型对所提算法进行性能评估,其结果验证了该算法在真实场景中的有效性和可靠性。Mobile edge computing provides a promising paradigm to satisfy the explosively growing communication and computational demands of power inspection robot,where the collected high-definitional videos are offloaded to the powerful edge servers for data processing and equipment state judgment.However,the lack of global state information(GSI),capacity-constrained battery,and ultra-reliable and low-latency communications(URLLC)constraint have posed new challenges to task offloading optimization.In this paper,we consider the channel selection problem which is critical to task offloading.With the combined power of reinforcement learning and Lyapunov optimization,we propose a channel selection algorithm combining an energy-awareness,URLLC-awareness,and task-priority-awareness.Without perfect GSI,the algorithm can dynamically optimize channel selection strategies to maximize the utility under the long-term constraints of energy budget and URLLC in a best-effort way.The channel model and electromagnetic interference model derived by measured data in substation are adopted for performance evaluation,and the simulation results verify the effectiveness and reliability of the proposed algorithm.

关 键 词：电力巡检 移动边缘计算 高可靠低时延通信 强化学习 信道选择 

分 类 号：TM75[电气工程—电力系统及自动化]