TASK ALLOCATION FOR UAV SWARM INSPECTION IN WIND FARMS BASED ON IMPROVED CBBA ALGORITHM

Jiao Songming; Chen Yuxi

doi:10.19912/j.0254-0096.tynxb.2023-0947

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (10) : 554-565. DOI: 10.19912/j.0254-0096.tynxb.2023-0947

TASK ALLOCATION FOR UAV SWARM INSPECTION IN WIND FARMS BASED ON IMPROVED CBBA ALGORITHM

Jiao Songming, Chen Yuxi

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Abstract

Wind turbines are widely distributed and located in complex environments, which are more suitable for autonomous inspection by UAV swarms, where the multi-UAV task allocation problem needs to be solved. In this paper, we propose a multi-UAV wind farm inspection task allocation method based on improved CBBA algorithm. Firstly, based on the basic information of wind turbines, the K-means algorithm is used to determine the number and placement of UAVs, and the value assessment function of wind turbine inspection is constructed to more reasonably evaluate the inspection revenue of each wind turbine; then, a two-way construction strategy of task package based on CBBA algorithm is proposed for the homing and returning problem of UAVs; Finally, to overcome the issue of individual "greed" reducing overall benefits in the traditional CBBA algorithm, a category-based reward system is designed to enhance the rationality of task allocation. The experiments show that the proposed method can develop a corresponding inspection task allocation scheme according to the changes of wind speed and wind direction, and provide a referenceable scheme for the task allocation of autonomous inspection of wind farms by UAV swarms.

Key words

wind turbines / inspection / unmanned aerial vehicles / task allocation / consensus-based bundle algorithm

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Jiao Songming, Chen Yuxi. TASK ALLOCATION FOR UAV SWARM INSPECTION IN WIND FARMS BASED ON IMPROVED CBBA ALGORITHM[J]. Acta Energiae Solaris Sinica. 2024, 45(10): 554-565 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0947

References

[1] 崔文倩, 魏军强, 赵云灏, 等. 双碳目标下含重力储能的配电网多目标运行优化[J]. 电力建设, 2023, 44(4): 45-53.
CUI W Q, WEI J Q, ZHAO Y H, et al.Multi-objective operation optimization of distribution network with gravity energy storage under double carbon target[J]. Electric power construction, 2023, 44(4): 45-53.
[2] 刘福才, 王向东. 风电机械设备故障诊断中振动分析的应用探究[J]. 太阳能学报, 2023, 44(7): 552.
LIU F C, WANG X D.Application of vibration analysis in fault diagnosis of wind power machinery and equipment[J]. Acta energiae solaris sinica, 2023, 44(7): 552.
[3] 王煜东, 王俊杰, 沈璐. 风电场道路加宽设计的运动学理论模型[J]. 太阳能学报, 2024, 45(5): 44-50.
WANG Y D, WANG J J, SHEN L.Kinematic theoretical model for road widening design in wind farms[J]. Acta energiae solaris sinica, 2024, 45(5): 44-50.
[4] 刘奇. 无人机在风电巡检的应用[J]. 电子元器件与信息技术, 2020, 4(11): 76-77.
LIU Q.Application of UAV in wind power patrol inspection[J]. Electronic component and information technology, 2020, 4(11): 76-77.
[5] 段慧云, 汪洋青. 人工智能技术在风电机组智能巡检中的应用[J]. 科学技术创新, 2019(30): 155-156.
DUAN H Y, WANG Y Q.Application of artificial intelligence technology in intelligent inspection of wind turbine[J]. Scientific and technological innovation, 2019(30): 155-156.
[6] 焦嵩鸣, 白健鹏, 首云锋. 风机叶片精准巡视的无人机控制策略研究[J]. 中国电机工程学报, 2023, 43(10): 3822-3832.
JIAO S M, BAI J P, SHOU Y F.Research on UAV control strategy for accurate inspection of wind turbine blades[J]. Proceedings of the CSEE, 2023, 43(10): 3822-3832.
[7] 何芳. 基于无人机的风机叶片损伤监测系统研究[J]. 中国高新科技, 2022(9): 34-35.
HE F.Research on UAV based wind turbine blade damage monitoring system[J]. China high tech, 2022(9): 34-35.
[8] CAO P, LIU Y, YANG C, et al.MEC-driven UAV-enabled routine inspection scheme in wind farm under wind influence[J]. IEEE access, 2019, 7: 179252-179265.
[9] GUO H W, CUI Q Q, WANG J W, et al.Detecting and positioning of wind turbine blade tips for UAV-based automatic inspection[C]//IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019: 1374-1377.
[10] 宋薇, 高原, 沈林勇, 等. 一种基于近场子集划分的多机器人任务分配算法[J]. 机器人, 2021, 43(5): 629-640.
SONG W, GAO Y, SHEN L Y, et al.A multi-robot task allocation algorithm based on near-field subset partition[J]. Robot, 2021, 43(5): 629-640.
[11] 陈南凯, 王耀南, 贾林. 基于改进生物激励神经网络算法的多移动机器人协同变电站巡检作业[J]. 控制与决策, 2022, 37(6): 1453-1459.
CHEN N K, WANG Y N, JIA L.Multi-mobile robot cooperative inspection operation based on improved biological excitation neural network algorithm in substation[J]. Control and decision, 2022, 37(6): 1453-1459.
[12] 徐思雅, 邢逸斐, 郭少勇, 等. 基于深度强化学习的能源互联网智能巡检任务分配机制[J]. 通信学报, 2021, 42(5): 191-204.
XU S Y, XING Y F, GUO S Y, et al.Deep reinforcement learning based task allocation mechanism for intelligent inspection in energy Internet[J]. Journal on communications, 2021, 42(5): 191-204.
[13] HUANG Z, WANG H X, ZHANG T P, et al.The collaborative power inspection task allocation method of “unmanned aerial vehicle and operating vehicle”[J]. IEEE access, 2021, 9: 62926-62934.
[14] 丁滢颍, 何衍, 蒋静坪. 基于蚁群算法的多机器人协作策略[J]. 机器人, 2003, 25(5): 414-418.
DING Y Y, HE Y, JIANG J P.Multi-robot cooperation method based on the ant algorithm[J]. Robot, 2003, 25(5): 414-418.
[15] 朱晓宇, 何兵, 刘刚, 等. 基于一致性差分进化的分布式任务分配[J]. 电光与控制, 2021, 28(9): 20-24, 38.
ZHU X Y, HE B, LIU G, et al.Consensus-based differential evolution for decentralized task allocation[J]. Electronics optics & control, 2021, 28(9): 20-24, 38.
[16] 翟政, 何明, 徐鹏, 等. 基于市场机制的无人集群任务分配研究综述[J]. 计算机应用研究, 2023, 40(7) :1921-1928.
ZHAI Z, HE M, XU P, et al.Research review of task allocation for unmanned swarm based on market mechanism[J]. Application research of computers, 2023, 40(7) :1921-1928.
[17] 张梦颖, 王蒙一, 王晓东, 等. 基于改进合同网的无人机群协同实时任务分配问题研究[J]. 航空兵器, 2019, 26(4): 38-46.
ZHANG M Y, WANG M Y, WANG X D, et al.Cooperative real-time task assignment of UAV group based on improved contract net[J]. Aero weaponry, 2019, 26(4): 38-46.
[18] BERTSEKAS D P.A new algorithm for the assignment problem[J]. Mathematical programming, 1981, 21(1): 152-171.
[19] CHOI H L, BRUNET L, HOW J P.Consensus-based decentralized auctions for robust task allocation[J]. IEEE transactions on robotics, 2009, 25(4): 912-926.
[20] RINALDI M, PRIMATESTA S, GUGLIERI G, et al.Auction-based task allocation for safe and energy efficient UAS parcel transportation[J]. Transportation research procedia, 2022, 65: 60-69.
[21] ZHANG Z Y, WANG J, XU D, et al.Task allocation of multi-AUVs based on innovative auction algorithm[C]//2017 10th International Symposium on Computational Intelligence and Design(ISCID), Hangzhou, China, 2017: 83-88.
[22] 李亚, 刘丽平, 李柏青, 等. 基于改进K-Means聚类和BP神经网络的台区线损率计算方法[J]. 中国电机工程学报, 2016, 36(17): 4543-4552.
LI Y, LIU L P, LI B Q, et al.Calculation of line loss rate in transformer district based on improved K-Means clustering algorithm and BP neural network[J]. Proceedings of the CSEE, 2016, 36(17): 4543-4552.
[23] 高程, 都延丽, 步雨浓, 等. 基于顺序扩展一致性包算法的多无人机分布式任务分配[J]. 控制与决策, 2023, 38(11): 3242-3250.
GAO C, DU Y L, BU Y N, et al.Distributed task allocation of multiple UAVs based on sequential extended consensus based bundle algorithm[J]. Control and decision, 2023, 38(11): 3242-3250.
[24] 李富强. 基于一致性包算法的多机器人任务分配研究[D]. 大连: 大连理工大学, 2022.
LI F Q.Research on multi-robot task assignment based on consistent packet algorithm[D]. Dalian: Dalian University of Technology, 2022.