Reference
J. Fu, A. Núñez, and B. De Schutter, "Real-time UAV routing
strategy for monitoring and inspection for postdisaster restoration of
distribution networks,"
IEEE Transactions on Industrial
Informatics, vol. 18, no. 4, pp. 2582-2592, Apr. 2022.
Abstract
After a natural disaster, a quick inspection of all damaged components is
crucial to recover the functionality of distribution networks. Unmanned aerial
vehicles (UAVs) can perform inspection tasks, particularly for damages that are
difficult to access for human repair crews. Additionally, UAVs can monitor the
transmission lines to find potential dangers and early-stage damages, and to
monitor the road infrastructure to provide real-time information about traffic
conditions so that repair crews can select the best ways to reach damages.
Besides, due to unpredictable events during restoration, the UAV routing
strategy (UAVRS) needs to be updated in real time. Thus, the proposed UAVRS in
this paper determines the optimal routes for the UAVs allocated to inspect
damages as well as the optimal routes for the UAVs to monitor transmission
lines and roads in real-time for distribution networks. To tackle the
multi-time-scale characteristic of the proposed UAVRS, a two-layer
decision-making architecture is proposed. A bi-level programming problem is
solved in the first layer for the large time scale problem and a mixed-integer
linear programming (MILP) problem is solved for the small time scale problem in
the second layer. A case study based on the distribution network in Zaltbommel
and its neighbor areas, in the Netherlands, illustrates the effectiveness of
our real-time method compared to the off-line methods. Furthermore, different
solvers are studied and compared in view of the real-time requirement.
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BibTeX
@article{FuNun:22-019,
author = {Fu, Jianfeng and N{\'{u}}{\~{n}}ez, Alfredo and De Schutter,
Bart},
title = {Real-Time {UAV} Routing Strategy for Monitoring and Inspection
for Postdisaster Restoration of Distribution Networks},
journal = {IEEE Transactions on Industrial Informatics},
volume = {18},
number = {4},
pages = {2582--2592},
month = apr,
year = {2022}
}