数字孪生辅助的变电站巡检任务分配算法
张乐霏
(三峡大学 电气与新能源学院，湖北 宜昌 443002)
摘 要 ：变电站巡检自动化、智能化需求增长，但现有的四足巡检机器人面临能耗、电池与充电问题， 影响巡检效率。对此提出了数字孪生辅助的多目标强化学习算法 (DT-PPO)，构建场景感知能耗模型量化动态 功耗，并引入动态权重迁移机制自适应调整策略。实验结果显示，相比传统方法，该算法显著提升任务完成率、 电池利用率，降低充电频次，表明DT-PPO在复杂动态环境下具有鲁棒性与实用性，实现了任务分配与能量 管理协同优化。
关键词 ： 数字孪生 ；强化学习 ；能量管理 ；多场景任务分配 ；变电站巡检
中图分类号 ：TM63 文献标识码 ：A 文章编号 ：1007-3175(2025)11-0035-06
Digital Twin-Assisted Substation Inspection Task Allocation Algorithm
ZHANG Le-fei
（College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China）
Abstract: With the increasing demand for automation and intelligence in substation inspections, existing quadruped inspection robots face problems such as energy consumption, battery and charging issues, which affect the inspection efficiency. To address this, a digital twin-assisted multi-objective reinforcement learning algorithm (DT-PPO) is proposed. This algorithm constructs a scenario-aware energy consumption model to quantify dynamic power consumption and introduces a dynamic weight transfer mechanism to adaptively adjust strategies. Experimental results show that compared with traditional methods, this algorithm significantly improves task completion rate and battery utilization and reducing charging frequency. These results indicate that DT-PPO has robustness and practicality in complex dynamic environments, achieving collaborative optimization of task allocation and energy management.
Key words: digital twin; reinforcement learning; energy management; multi-scene task allocation; substation inspection
参考文献
[1] HUTTER M, GEHRING C, LAUBER A, et al.ANYmaltoward legged robots for harsh environments[J]. Advanced Robotics，2017，31(17)：918-931.
[2] CHEN X, LIU Y, ZHANG Q.Energy management strategies for quadruped robots in dynamic environments[J].IEEE Transactions on Robotics， 2021，37(4)：1125-1137.
[3] 周立辉，张永生，孙勇，等 . 智能变电站巡检机器人研制及应用[J]. 电力系统自动化，2011，35(19)： 85-88.
[4] MA Y, CHEN H, WANG Z.Dynamic power consumption analysis for legged robots on variable terrains[J].IEEE Transactions on Robotics， 2017，33(4)：872-885.
[5] TAWFIK M, PATTERSON A, SMITH J.Nonlinear energy characterization in multi-task robotic operations[J].Journal of Field Robotics，2024， 41(1)：45-62.
[6] MOHAJER N, ABBASI R, MARTINS L.Quantitative analysis of lithium-ion battery degradation in robotic applications[J].Energy Storage Materials，2020，28： 132-144.
[7] LIU X, ZHANG K, WU Y. Peak-valle gap amplification caused by fixed-threshold charging in microgrids[J].Applied Energy，2022， 308：118324.
[8] JUAN E, GARCI A S, MARTI NEZ P.Real-world performance degradation of offline-trained RL agents in mobile robotics[J].Robotics and Autonomous Systems，2023，159：104288.
[9] ZHANG L, WANG H, CHEN Y.Digital twin-enhanced reinforcement learning for multi-objective optimization in smart grids[J].IEEE Transactions on Industrial Informatics，2023，19(5)：4321-4332.
[10] LI X, LIU S.Dynamic battery management in robotic systems using adaptive threshold control[J].Energy Conversion and Management， 2022，265：115741.
[11] 卢成，袁明辉 . 锂离子电池容量动态测量研究 [J]. 上 海理工大学学报，2013，35(2)：165-168.
[12] SHANNON C E. A mathematical theory of communication[J].The Bell System Technical Journal，1948，27(3)：379-423.
[13] 王清宇 . 基于电池老化的并联式混合动力汽车SOC估算及能量管理策略研究 [D]. 重庆：重庆交通大学，2021.
[14] 冯光.基于EKF的锂离子电池SOC估算的建模与仿真[D]. 武汉：武汉理工大学，2013.
[15] 周秀文 . 电动汽车锂离子电池健康状态估计及寿命预测方法研究 [D]. 吉林：吉林大学，2016.
