Hybrid Machine Learning Approaches for Enhanced Grid Stability Prediction in Modern Energy Systems

Strategic maintenance of power grids guarantees reliable power delivery because grids are expanding their complexity with renewable energy sources integration. This work implements three hybrid prediction systems for grid stability assessment which include Graph Convolutional Network (GCN) + Transformer, Tabular Neural Network (TabNet) with Light Gradient Boosting Machine (LightGBM), and Stacked Autoencoder with Multilayer Perceptron (MLP). These models leverage complementary strengths: The combination of GCN + Transformer acknowledges grid structure while TabNet + LightGBM recognizes important features and Stacked Autoencoder + MLP discovers deep patterns to support decision making. The model based on Stacked Autoencoder + MLP showed the optimum performance for distinguishing between stable and unstable grid conditions. The predictive model successfully forecasts electric grid behavior, which allows operators to take preventative measures to prevent stability incidents. Such a method combination proves beneficial for achieving greater accuracy and practicality when dealing with stability management in modern dynamic energy systems.