Application of Deep Learning and Neural Networks for Smart Grid Stability Predictions
Efficient and sustainable electrical grids are crucial for energy management in modern society and industry. Govern-ments recognize this and prioritize energy management in their plans, alongside significant progress made in theory and practice over the years. The complexity of power systems determines the unique nature of power communication networks, and most researches have been focusing on the dynamic nature of voltage stability, which led to the need for dynamic models of power systems. Control strategies based on stability assessments have become essential for managing grid stability, diverging from traditional methods and often leveraging advanced computational techniques based on deep learning algorithms and neural networks. This way, researchers can develop predictive models capable of forecasting voltage stability and detecting potential instability events in real-time, whereas neural networks can also optimize control strategies based on wide-area information and grid response, enabling more effective stability control measures, as well as detecting and classifying disturbances or faults in the grid. This paper explores the use of predictive models to assess smart grid stability, examining the benefits, risks, and comparing results to determine the most effective approach.