针对现代电力和能源系统中无人机检查任务的可靠性导向框架
Luttfi A Al-Haddad1, Wissam Khalid2, Sarmad Ziyad Tariq2
1College of Mechanical Engineering, University of Technology- Iraq, Baghdad, Iraq. Luttfi.a.alhaddad@uotechnology.edu.iq.
Scientific reports
|December 4, 2025
概括
这项研究开发了一种数据驱动的方法来分类无人机 (UAV) 任务可靠性,用于电网检查. 该方法准确地确定合适的,有风险的或不可行的位置,增强自主部署.
科学领域:
- 机器人和自动化 机器人和自动化
- 人工智能的人工智能
- 电力系统工程 电力系统工程
背景情况:
- 自主无人飞行器 (UAV) 的部署对现代动力和能源系统至关重要,面临着空间和操作限制.
- 可靠性评估对于关键基础设施检查任务的任务成功至关重要.
- 现有的方法可能无法完全解决现实世界的操作约束和边缘场景的复杂性.
研究的目的:
- 提出一个数据驱动的分类方法来评估无人机检查任务的可靠性.
- 根据空间和操作参数,将任务地点分类为合适,有风险或不可行.
- 支持智能任务规划,增强电力和能源部门的运营弹性.
主要方法:
- 使用累积无人机路由问题 (CUAVRP) 基准与四个不同的任务场景.
- 引入了合成应激节点,以模拟基础设施检查中的边缘情况.
- 采用渐变增强分类模型,对空间和操作特征进行训练,以分类节点状态 (任务可行性,覆盖可靠性,部署适用性).
主要成果:
- 在所有分析的场景中,分类方法实现了高性能.
- 在 cuavrp_d9_k6_r800 场景表现出极好的结果,准确率为 97.05%,精度为 96.33%,回忆率为 97.72%,F1 评分为 97.02%.
- 该框架有效地支持用于关键检查环境的自动化无人机部署策略.
结论:
- 拟议的数据驱动分类框架提高了无人机检查任务的可靠性评估.
- 该方法为智能任务规划和作战弹性提供了强有力的方法.
- 未来的工作将纳入物理层降解因素,以进一步提高评估现实性和分类稳定性.
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