Leveraging dense layer hybrid graph neural networks for managing overvoltage in PV-dominated distribution systems

The rapid integration of photovoltaic (PV) systems into electrical distribution networks poses significant challenges, particularly regarding the management of overvoltage issues when generation exceeds demand. Conventional mitigation methods often struggle to address the complex, dynamic nature of modern power systems. This study introduces a novel Hybrid Graph Neural Network with Dense Layer (HGNN-DL) approach to tackle these challenges, leveraging advanced machine learning techniques for accurate voltage prediction and grid management. By employing advanced graph neural network techniques, the research addresses the complicated graph-structured characteristics of modern power distribution networks with high PV penetration. Comprehensive validation using both synthetic IEEE datasets and real-world field measurements demonstrates the superior performance of the proposed method. Comparative analysis across multiple deep learning models reveals the HGNN-DL method achieved remarkable predictive accuracy, with the lowest Mean Absolute Error (0.15000), Mean Squared Error (0.00250), and Root Mean Square Error (0.00550), coupled with an exceptional R² value of 0.97000. These results not only highlight the potential of advanced graph neural network architectures but also provide a promising framework for more effective overvoltage mitigation in photovoltaic (PV)- dominated electrical grids.