# Unified resilience model using deep learning for assessing power system performance

## TL;DR

This paper introduces a deep learning model to improve power system resilience, especially for renewable energy components like batteries.

## Contribution

The novel Unified Resilience Model (URM) uses deep learning to enhance power system performance by analyzing environmental and operational factors.

## Key findings

- The URM model effectively analyzes environmental factors affecting battery and energy storage resilience.
- Deep learning training on low resilience data improves system performance and drain mitigation.
- Weather factors significantly impact power system resilience, verified through model validation.

## 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.