A Data-Driven Deep Learning-based Prognostication for Power Grid Stability

The rapid increase in renewable energy sources, known for their unpredictability, is challenging the stability of the electricity grid. In response, this study introduces a deeplearning-based forecasting method to maintain a sustainable grid operation, focusing on the New England IEEE 39-Bus Power System (NEPS). The proposed approach is meticulously compared with current methods, emphasizing system dynamics to ascertain its effectiveness. Further validation is provided through a model-in-the-loop analysis, ensuring the functionality of the forecasting strategy. Overall, this research highlights the potential of advanced data-driven techniques in enhancing grid reliability amidst the surge of renewable energy sources.