Developing Efficient Sparse Graph Neural Architectures for Dynamic Topological Adaptation in Smart Power Grids

The increasing complexity of smart power grids demands advanced computational frameworks capable of managing dynamic topological changes in real time. Graph Neural Networks (GNNs) have recently emerged as a promising paradigm for modelling power grid components and their interconnections due to their ability to capture relational dependencies. However, conventional dense GNN architectures often face scalability challenges when applied to large-scale grids with heterogeneous and evolving structures. This paper proposes the development of efficient sparse graph neural architectures designed specifically for dynamic topological adaptation in smart power grids. The sparse design reduces computational overhead by selectively learning from critical nodes and edges, while adaptive mechanisms ensure real-time responsiveness to topology reconfigurations such as line failures, renewable integration, and load fluctuations. The framework combines spectral graph convolution, topologyaware attention modules, and reinforcement-based learning strategies to balance accuracy with efficiency. Experimental evaluations on benchmark power grid datasets demonstrate significant improvements in computational speed, memory efficiency, and predictive accuracy compared to traditional dense GNNs. Moreover, the proposed model enables proactive fault detection, adaptive load management, and resilience enhancement under uncertain grid conditions. By integrating sparsity-driven learning with dynamic adaptability, the research highlights the potential of next-generation GNN architectures to accelerate smart grid digital ransformation and contribute to sustainable energy systems.