A Comparative Analysis of Deep Learning Models for Predicting Power System Failures - Dimas Aditya; Devina Putri; Nanda Asyifa (International Journal of Electrical Engineering, Mathematics and Computer Science, 2024)
Power systems are critical infrastructure that face significant challenges due to increasing demand and inherent complexity. Predicting failures in power systems is crucial for enhancing grid reliability, minimizing downtime, and optimizing maintenance processes. This study evaluates various deep learning models, specifically convolutional neural networks (CNN), recurrent neural networks (RNN), and transformer models, for predicting power system failures. By analyzing these models’ performance metrics on historical power grid data, the study provides insights into the strengths and weaknesses of each approach. The findings contribute to the development of more robust predictive models for power system reliability.