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Graph-Theoretic and Geospatial Intelligence Framework for Optimizing Smart Grid Structures Using Hybrid Drones

Graph-Theoretic and Geospatial Intelligence Framework for Optimizing Smart Grid Structures Using Hybrid Drones
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Author(s): R. Deepa (SRM Institute of Science and Technology, India), A. Bennet Prabhu (Congruent Solutions, India)and G. Niranjana (SRM Institute of Science and Technology, India)
Copyright: 2026
Pages: 20
Source title: Advancing Environmental Research Through Applied GIS and Remote Sensing
Source Author(s)/Editor(s): Jamal Al Karkouri (Ibn Tofail University, Morocco), Adil Moumane (Ibn Tofail University, Morocco), Abdessamad Elmotawakkil (Ibn Tofail University, Morocco)and Mouhcine Batchi (Ibn Tofail University, Morocco)
DOI: 10.4018/979-8-3373-6608-1.ch012

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Abstract

This paper proposes G-O-HA, a graph theory-based framework integrating heuristic algorithms and hybrid drones to optimize smart grid performance. Smart grids are modelled as dynamic graphs, with Particle Swarm Optimization (PSO) identifying efficient routing paths and enhancing energy distribution. Hybrid drones equipped with multispectral sensors and LiDAR conduct aerial inspections and real-time fault localization. Geospatial data from drones continuously updates the optimization model, enabling adaptive maintenance and network reconfiguration. Applied to real-world scenarios, G-O-HA reduces fault recovery time by 35% and improves communication efficiency and throughput. The study demonstrates how combining graph theory, heuristic optimization, and drone intelligence can build resilient, responsive power systems.

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