Graph Neural Network- Enhanced Power Flow Adjustment
Gowtham I1, Perumal B2
1Gowtham I, Department of Electrical and Electronics, Engineering, Adhiyamaan College of Engineering, Hosur (Tamil Nadu), India.
2Dr. Perumal B, Assistant Professor, Department of Electrical and Electronics Engineering, Adhiyamaan College of Engineering, Hosur (Tamil Nadu), India.
Manuscript received on 30 January 2026 | Revised Manuscript received on 08 February 2026 | Manuscript Accepted on 15 March 2026 | Manuscript published on 30 March 2026 | PP: 21-26 | Volume-13 Issue-3, March 2026 | Retrieval Number: 100.1/ijies.B114213020226 | DOI: 10.35940/ijies.B1142.13030326
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Modern power grids are facing unprecedented operational complexity due to the surge in distributed energy resources (DERs), intermittent renewables, and electric vehicle (EV) charging demands. While traditional methods like Newton Raphson are computationally precise, their iterative nature often fails to meet the sub-second latency requirements of dynamic smart grids. This research proposes a Graph Neural Network (GNN) framework designed to model electrical networks as high dimensional graphs. By capturing the inherent topological relationships among buses (nodes) and transmission lines (edges), the GNN-based approach provides a scalable, data-driven alternative for real-time power-flow estimation. The framework effectively processes non-linear grid behaviours and uncertainties, ensuring stable and efficient grid management, congestion control, and optimal power dispatch in rapidly evolving electrical environments.
Keywords: Distributed Energy Resources, Electric Vehicle, Graph Neural Network, Non-Linear Grid
Scope of the Article: Electrical Engineering