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A Review of Asset Management using Artificial Intelligence based Machine Learning models with applications for the Electric Power and Energy System
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  • GOPAL LAL RAJORA,
  • Miguel A. Sanz-Bobi,
  • Lina Tjernberg,
  • José Eduardo Urrea Cabus
GOPAL LAL RAJORA
Comillas Pontifical University

Corresponding Author:glrajora@comillas.edu

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Miguel A. Sanz-Bobi
Comillas Pontifical University
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Lina Tjernberg
KTH Royal Institute of Technology School of Electrical Engineering and Computer Science
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José Eduardo Urrea Cabus
KTH Royal Institute of Technology School of Electrical Engineering and Computer Science
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Abstract

Power system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as Artificial Intelligence (AI), machine learning (ML), and Deep Learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large-scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, we provide insights into the transformative potential of ML in shaping the future of power system asset management.
20 Nov 2023Submitted to IET Generation, Transmission & Distribution
22 Feb 2024Review(s) Completed, Editorial Evaluation Pending
24 Mar 2024Submission Checks Completed
24 Mar 2024Assigned to Editor
24 Mar 2024Review(s) Completed, Editorial Evaluation Pending
07 Apr 2024Reviewer(s) Assigned
10 May 2024Editorial Decision: Accept