📅 30 March 2024
DOI: 10.62951/ijeemcs.v1i1.68
A Comparative Analysis of Deep Learning Models for Predicting Power System Failures
International Journal of Electrical Engineering, Mathematics and Computer Science
Asosiasi Riset Teknik Elektro dan Informatika Indonesia (ARTEII)
📄 Abstract
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.
🔖 Keywords
#predictive modeling
ℹ️ Informasi Publikasi
Tanggal Publikasi
30 March 2024
Volume / Nomor / Tahun
Volume 1, Nomor 1, Tahun 2024
📝 HOW TO CITE
Dimas Aditya; Devina Putri; Nanda Asyifa, "A Comparative Analysis of Deep Learning Models for Predicting Power System Failures," International Journal of Electrical Engineering, Mathematics and Computer Science, vol. 1, no. 1, Mar. 2024.
ACM
ACS
APA
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
0
Sitasi
Cek Google Scholar