Transient Stability

Transient stability analysis in power systems aims to predict whether a power grid will remain stable after a disturbance, such as a fault or sudden load change. Current research focuses on improving the accuracy and efficiency of these predictions using machine learning techniques, including deep neural networks (like Physics-Informed Neural Networks and others), graph neural networks, and hybrid models combining neural networks with traditional methods like regression trees. These advancements address challenges like data scarcity, imbalanced datasets, and the need for interpretable models, ultimately contributing to more robust and reliable grid operation and improved security.