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Reliability-oriented framework for UAV-based inspection missions in modern power and energy systems.

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.

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Summary

This study developed a data-driven method to classify unmanned aerial vehicle (UAV) mission reliability for power grid inspections. The approach accurately identifies suitable, at-risk, or infeasible locations, enhancing autonomous deployment.

Keywords:
CUAVRP datasetCatboostCommunication reliabilityMission planningUAV

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Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Power Systems Engineering

Background:

  • Autonomous unmanned aerial vehicle (UAV) deployment is crucial for modern power and energy systems, facing spatial and operational limitations.
  • Reliability assessment is vital for mission success in critical infrastructure inspection tasks.
  • Existing methods may not fully address the complexities of real-world operational constraints and edge-case scenarios.

Purpose of the Study:

  • To present a data-driven classification method for assessing the reliability of UAV inspection missions.
  • To categorize mission locations as suitable, at risk, or infeasible based on spatial and operational parameters.
  • To support intelligent mission planning and enhance operational resilience in the power and energy sector.

Main Methods:

  • Utilized the Cumulative UAV Routing Problem (CUAVRP) benchmark with four distinct mission scenarios.
  • Introduced synthetic stress nodes to simulate edge-case conditions in infrastructure inspection.
  • Employed a gradient boosting classification model trained on spatial and operational features to classify node status (Mission Feasibility, Coverage Reliability, Deployment Suitability).

Main Results:

  • The classification method achieved high performance across all analyzed scenarios.
  • The cuavrp_d9_k6_r800 scenario demonstrated excellent results with 97.05% accuracy, 96.33% precision, 97.72% recall, and 97.02% F1-score.
  • The framework effectively supports automated UAV deployment strategies for critical inspection environments.

Conclusions:

  • The proposed data-driven classification framework enhances the reliability assessment of UAV inspection missions.
  • The method provides a robust approach for intelligent mission planning and operational resilience.
  • Future work will incorporate physical-layer degradation factors to further improve assessment realism and classification robustness.