Abstract
This study proposes a conceptual framework for integrating Unmanned Aerial Vehicles (UAVs) into national power grid inspection programs to enhance reliability, safety, and operational efficiency. Traditional inspection methods, including manual patrols and helicopter surveys, are costly, hazardous, and often limited in coverage and data granularity. The proposed framework outlines a multi-layered architecture that combines UAV platforms, sensor payloads, communication networks, and data analytics systems within existing grid management infrastructures. At the acquisition layer, UAVs equipped with high-resolution cameras, LiDAR, and thermal sensors capture real-time asset condition data across transmission and distribution networks. The transmission layer ensures secure data transfer through edge computing and cloud-based platforms, enabling near real-time processing. The analytics layer leverages artificial intelligence and machine learning algorithms to detect faults, predict failures, and optimize maintenance scheduling. Integration with supervisory control and data acquisition systems enhances situational awareness and supports data-driven decision-making. The framework also addresses regulatory compliance, cybersecurity, workforce training, and cost-benefit considerations essential for large-scale adoption. A phased implementation strategy is proposed, starting with pilot deployments and scaling through standardized protocols and interoperability guidelines. The study highlights the potential of UAV-enabled inspection systems to reduce downtime, improve asset lifespan, and enhance grid resilience in both developed and developing energy markets. By providing a structured and adaptable approach, this framework contributes to the advancement of smart grid technologies and supports the transition toward more sustainable and efficient power systems. Furthermore the framework emphasizes interoperability with legacy systems and integration with geographic information systems to support spatial analysis and asset mapping. It incorporates risk-based prioritization models that allocate inspection resources based on asset criticality, environmental exposure, and historical failure patterns. Economic evaluation components assess lifecycle costs, return on investment, and performance improvements relative to conventional methods. Standardization of data formats, communication protocols, and safety procedures is recommended to facilitate national scale deployment. Stakeholder collaboration among utilities, regulators, and technology providers is identified as critical for governance, policy alignment, and long-term sustainability. Continuous feedback mechanisms enable iterative improvement and innovation in inspection practices and supports resilient infrastructure modernization across regions.
Keywords
UAV, Power Grid Inspection, Smart Grid, Predictive Maintenance, Artificial Intelligence
Citations
IRE Journals:
Delali Dagodzo "A Conceptual Framework for UAV Integration into National Power Grid Inspection Programs" Iconic Research And Engineering Journals Volume 2 Issue 5 2018 Page 391-412 https://doi.org/10.64388/IREV2I5-1716082
IEEE:
Delali Dagodzo
"A Conceptual Framework for UAV Integration into National Power Grid Inspection Programs" Iconic Research And Engineering Journals, 2(5) https://doi.org/10.64388/IREV2I5-1716082
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