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
Author(s): Abderrazak Khediri, Ayoub Yahiaoui, Mohamed Ridda Laouar, Yacine BelhocineSubject(s): Energy and Environmental Studies, Governance, Public Administration, Environmental and Energy policy, Security and defense, Methodology and research technology, ICT Information and Communications Technologies
Published by: Vysoká škola ekonomická v Praze
Keywords: Alert generation; Blackout events; Smart grids; Early warning system; Deep self-organizing map; Convolutional neural networks
Summary/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.
Journal: Acta Informatica Pragensia
- Issue Year: 13/2024
- Issue No: 2
- Page Range: 273-287
- Page Count: 15
- Language: English
