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A Two-Stage Location-Allocation Optimization Method for Fixed UAV Nests in Power Inspection Considering Node Failure

Zheng Huang¹, Hongxing Wang¹, Yiming Tang¹

¹State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, China.

Sensors (Basel, Switzerland)

|February 26, 2025

Summary

This study introduces an optimized method for deploying Unmanned Aerial Vehicle (UAV) nests in power inspection, significantly cutting costs and reducing the number of nests needed. The approach enhances the reliability and efficiency of power grid maintenance operations.

Keywords:

UAV nest location problem integer linear programming power facilities inspection robustness unmanned aerial vehicles

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Area of Science:

Operations Research
Robotics
Electrical Engineering

Background:

Power inspection operations increasingly rely on Unmanned Aerial Vehicles (UAVs).
Current UAV nest deployment strategies face challenges with reliability and cost-efficiency.
Potential nest failures can disrupt critical inspection tasks.

Purpose of the Study:

To develop an optimized two-stage location-allocation method for UAV nests in power inspection.
To minimize the number of UAV nests and associated deployment costs.
To improve the overall reliability of UAV-based power inspection systems.

Main Methods:

A two-stage location-allocation model was proposed.
The first stage identified optimal nest locations using Integer Linear Programming (ILP) and the Gurobi solver.
The second stage employed an ILS-SA heuristic algorithm for UAV nest type selection and task allocation.

Main Results:

The proposed method reduced total costs by 33.9%.
The number of required UAV nests decreased by 32% compared to the current greedy deployment method.
A case study in China validated the effectiveness and practicality of the approach.

Conclusions:

The developed method offers a significant improvement in cost-efficiency for UAV-based power inspection.
The approach enhances the reliability of power inspection systems by optimizing nest configuration and deployment.
This research provides a practical solution for power grid companies seeking to optimize their UAV operations.