Deep Learning Proactive Approach to Blackout Prevention in Smart Grids: An Early Warning System

Deep Learning Proactive Approach to Blackout Prevention in Smart Grids: An Early Warning System

Blog Article

Blackout events in smart grids can have significant impacts on individuals, communities and businesses, 4 Inch as they can disrupt the power supply and cause damage to the grid.In this paper, a new proactive approach to an early warning system for predicting blackout events in smart grids is presented.The system is based on deep learning models: convolutional neural networks (CNN) and deep self-organizing maps (DSOM), and is designed to analyse data from various sources, such as power demand, generation, transmission, distribution and weather forecasts.The system performance is evaluated using a dataset of time windows and labels, where the labels indicate whether a blackout event occurred within a given time window.It is found that the system is able to achieve an accuracy of 98.

71% and a precision of 98.65% in predicting blackout events.The results suggest that the early warning system presented in this paper is a promising tool for improving the resilience and reliability of electrical grids and for mitigating the impacts of blackout Coaster Holder events on communities and businesses.