Unified resilience model using deep learning for assessing power system performance

Jamil Alsayaydeh, Jamil Abedalrahim and Artemchuk, Volodymyr and Garbuz, Iurii and Shkarupylo, Vadym and Oliinyk, Andrii and Yusof, Mohd Faizal and Herawan, Safarudin Gazali (2025) Unified resilience model using deep learning for assessing power system performance. Heliyon, 11 (e42802). pp. 1-16. ISSN 2405-8440

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Abstract

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model’s fidelity in enhancing power system performance, with a specific focus on the impact of weather factors.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, Energy resilience, Fidelity, Weather impact
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	17 Jul 2026 07:53
Last Modified:	17 Jul 2026 07:53
URI:	http://eprints.utem.edu.my/id/eprint/29993
Statistic Details:	View Download Statistic

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