摘要
【背景】随着电力巡检需求的日益增长,传统的巡检方式已无法满足现代电网的时效性要求。无人机电力巡检虽具备高效率,但续航能力有限,难以保障远距离杆塔巡检的及时性。【目标】提出一种新型的电力巡检模式,利用多个固定无人机机巢进行巡检,解决无人机续航不足和及时性问题,进一步提升电力巡检效率。【方法】基于总巡检路径最短的优化目标,考虑无人机能耗不确定性、杆塔巡检时间窗以及风速影响,构建结合任务分配与路径规划的组合决策模型。该模型采用改进自适应大邻域搜索算法(ALNS)进行大规模求解,并引入模拟退火准则避免陷入局部最优。【结果】基于实际杆塔数据进行小规模实例分析,将本文算法求解结果与Gurobi求解器结果进行对比,验证了算法的有效性。基于大规模算例结果,改进的ALNS算法能够比传统算法更快速地找到精确解,并显著提高巡检路径规划的效率与准确性,与蚁群算法和ALNS算法相比,改进ALNS在巡检路径距离上分别减少了13.8%和16.7%。DRO模型通过保守的路径规划和任务重分配实现了更均衡的负载分布,有效避免了能耗不确定性导致的任务失败风险。【应用】该巡检方式为电力企业提升巡检效率提供了新的解决思路,并为电力巡检路径规划的优化方案提供了有力的参考与指导。
[Background]With the growing demand for power inspection,traditional inspection methods can no longer satisfy the timeliness requirements of modern power grids.Although drone based power inspections are highly efficient,their limited endurance causes difficulty in guaranteeing the timeliness of long-distance tower inspections.[Objective]A new power inspection model using multiple fixed drone nests is proposed to address the issues of drone endurance and timeliness and further improve the efficiency of power inspections.[Method]A combined decision model was developed,focusing on minimizing the total inspection path length and integrating task allocation and path planning.The model considered the uncertainties in drone energy consumption,tower inspection time windows,and the impact of wind speed.An improved adaptive large neighborhood search(ALNS)algorithm was applied for large-scale solving,and simulated annealing criteria were introduced to avoid local optima.[Results]To validate the performance of the algorithm,the results of the proposed algorithm were compared with those of the Gurobi solver,and practical tower data were used for case analysis,confirming the effectiveness of the algorithm.The improved ALNS algorithm obtained precise solutions faster than traditional algorithms and significantly enhanced the efficiency and accuracy of inspection path planning.Compared with the ant colony optimization algorithm and ALNS,the improved ALNS reduced the inspection path distance by 13.8%and 16.7%,respectively.[Application]This inspection model offers a new solution for power companies to improve inspection efficiency and provides a valuable reference and guidance for optimizing power inspection path planning.
作者
黄祥
吴媚
王海楠
孟悦
HUANG Xiang;WU Mei;WANG Hainan;MENG Yue(Jiangsu Frontier Electric Power Technology Co.,Ltd.,Nanjing 211102,China)
出处
《交通运输工程与信息学报》
2025年第3期130-143,共14页
Journal of Transportation Engineering and Information
关键词
智能交通
路径规划
自适应大邻域搜索算法
无人机
电力巡检
组合决策模型
intelligent transportation
path planning
improved adaptive large neighborhood search algorithm
drone
transmission inspection
combined decision model
