Efficient unmanned aerial vehicle paths design for post-disaster damage assessment of overhead transmission lines

IF 2.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IET Smart Grid Pub Date : 2023-07-13 DOI:10.1049/stg2.12120
Rachad Atat, Mostafa F. Shaaban, Muhammad Ismail, Erchin Serpedin
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Abstract

The widespread distribution of overhead transmission lines increases the vulnerability of power grids to failures. Thus, power lines need to be timely inspected, especially before or during emergency-related situations to ensure stable operation of the power grid. Traditional methods of visual inspection (satellites and helicopters) are inconvenient, often cannot be deployed and if they are deployed present a slow response time and high cost, which is very critical for fast post-disaster damage identification. On the other hand, employing an unmanned aerial vehicle (UAV) offers a more efficient, reliable, and faster means for the assessment process. This article proposes a novel approach for the post-disaster UAV-based damage assessment of overhead power lines. In the proposed approach, the UAVs paths over the most critical loads are formulated as an optimisation problem with the objective of minimising the total inspection time while considering the recharging of the UAVs' batteries. To solve the problem, an efficient framework that optimises the UAVs flight paths is proposed to inspect the critical loads in an efficient order, while accounting for the UAV recharging. This guarantees that the UAVs complete the assessment tasks unlike existing benchmarks.

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架空输电线路灾后破坏评估的高效无人机路径设计
架空输电线路的广泛分布增加了电网对故障的脆弱性。因此,需要及时检查电力线路,特别是在紧急情况发生前或发生时,以确保电网的稳定运行。传统的目视检查方法(卫星和直升机)不方便,往往无法部署,即使部署也存在响应时间慢和成本高的问题,这对快速识别灾后损害至关重要。另一方面,使用无人驾驶飞行器(UAV)为评估过程提供了更有效、可靠和更快的手段。本文提出了一种基于无人机的架空电力线灾后损伤评估新方法。在提出的方法中,考虑无人机电池的充电,将无人机在最关键负载上的路径制定为一个优化问题,目标是最小化总检查时间。为了解决这一问题,提出了一种优化无人机飞行路径的有效框架,在考虑无人机充电的情况下,以有效的顺序检查关键载荷。这保证了无人机完成不同于现有基准的评估任务。
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来源期刊
IET Smart Grid
IET Smart Grid Computer Science-Computer Networks and Communications
CiteScore
6.70
自引率
4.30%
发文量
41
审稿时长
29 weeks
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