A Novel Smart Grid Stability Prediction Method Based Ensemble Learning in Virtual Power Plants

Stability prediction for smart grids is a critical issue in the energy sector, with significant implications for national energy security and daily life. As power systems evolve toward more intelligent and automated frameworks, such as virtual power plants (VPPs), predicting when the grid may become unstable has become an urgent challenge. To address this, this paper proposes a stability prediction method that combines deep snapshot stacking ensemble learning. The method utilizes cosine annealing techniques to train several distinct deep neural network models, which are then combined using a support vector machine (SVM) as a meta-learner from the stacking ensemble algorithm to further enhance prediction performance. Simulation results demonstrate that this approach significantly outperforms traditional machine learning algorithms and common ensemble schemes.