Electric Grid Resilience Enhancement During Natural Disasters: An Optimization-Based UAV Inspection and Dynamic Crew Dispatch Model

Abstract

A Master of Science thesis in Electrical Engineering by Yousef Serag entitled, “Electric Grid Resilience Enhancement During Natural Disasters: An Optimization-Based UAV Inspection and Dynamic Crew Dispatch Model”, submitted in April 2025. Thesis advisor is Dr. Mostafa Shaaban and thesis co-advisor is Dr. Mahmoud Ibrahim. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Natural disasters pose significant challenges to power grid resilience, often resulting in prolonged outages and substantial economic losses due to inefficient restoration processes. Traditional methods primarily focus on optimizing repair crew (RC) sequences while neglecting the critical inspection phase, leading to delayed fault detection and increased costs of interruption . This thesis introduces a holistic, UAV-assisted framework that integrates unmanned aerial vehicle (UAV) inspections, dynamic RC dispatch, and strategic charger placement to address these shortcomings. The approach leverages probabilistic failure analysis to prioritize high-risk lines, optimizes UAV inspection sequences with battery-aware path planning, and dynamically coordinates repair efforts to minimize COI. The framework’s efficacy is evaluated using three distinct methods: Optimization based Approach, (GA), and Deep Learning (DL). OPTIMIZATION BASED APPROACH provides high accuracy in simplified scenarios but lacks scalability for real-time applications. GA offers a balanced trade-off between accuracy and computational efficiency, while DL delivers rapid, scalable solutions with acceptable accuracy, making it ideal for urgent disaster response. Tested on a 33-bus system, the framework achieves a 56.34% reduction in COI compared to conventional strategies, demonstrating its superiority in reducing downtime and enhancing resilience. The novelty of this work lies in its comprehensive integration of inspection and repair processes, utilizing advanced technologies for real-time adaptability. By addressing the overlooked inspection phase and optimizing resource allocation, this thesis presents a scalable, data-driven solution that significantly advances post-disaster grid restoration, offering a practical approach to mitigate the socio-economic impacts of power outages in large-scale disaster scenarios.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE