A Framework for National-Scale UAV Deployment in Power Infrastructure: Lessons from Developing Economies

This study proposes a comprehensive framework for the national-scale deployment of unmanned aerial vehicles (UAVs) in power infrastructure inspection and maintenance, drawing on lessons from developing economies where resource constraints, infrastructure deficits, and rapid urbanization shape operational realities. The framework integrates technical architecture, regulatory alignment, workforce development, and data governance to enable scalable, cost-effective, and resilient UAV programs across transmission and distribution networks. It emphasizes modular system design, including sensor selection, communication protocols, and edge analytics, to support real-time fault detection, predictive maintenance, and risk mitigation in geographically dispersed environments. The study further examines institutional readiness by outlining policy pathways for airspace integration, safety assurance, and public acceptance, particularly in regions with evolving aviation regulations and limited enforcement capacity. Drawing from case evidence in Africa, Asia, and Latin America, the framework identifies key success factors such as public–private partnerships, local capacity building, and adaptive financing models that reduce upfront capital burdens. It also addresses operational challenges including data interoperability, cybersecurity risks, and environmental constraints such as extreme weather and terrain variability. A multi-layer implementation roadmap is proposed, spanning pilot validation, phased scaling, and continuous performance optimization through data-driven feedback loops. The framework contributes to the discourse on digital transformation in energy systems by demonstrating how UAV-enabled inspection can enhance grid reliability, reduce outage durations, and improve asset lifecycle management in resource-limited settings. Ultimately, the study provides actionable guidance for policymakers, utilities, and technology providers seeking to institutionalize UAV operations at scale while aligning with sustainability, safety, and economic development objectives in developing economies. By integrating geospatial intelligence, machine learning models, and cloud-based platforms, the framework enables continuous monitoring and automated anomaly detection across vast grid assets. It further highlights the importance of standardized data pipelines and interoperable platforms to ensure seamless information exchange between field operations and control centers. Additionally, stakeholder engagement strategies are incorporated to foster trust, build regulatory confidence, and support long-term program sustainability. The proposed model offers a replicable blueprint adaptable to diverse national contexts, thereby accelerating the adoption of UAV-enabled inspection ecosystems globally. It also outlines measurable performance indicators for evaluating efficiency, safety, and scalability outcomes.