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

Abstract

Blackout events in smart grids can have significant impacts on individuals, communities and businesses, 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 events on communities and businesses.

Keywords

• alert generation
• blackout events
• smart grids
• early warning system
• deep self-organizing map
• convolutional neural networks