Asset Management in Smart Grids: A Review

Niaki, Amir Hossein Mohammadzadeh

doi:10.1109/sgc54087.2021.9664019

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…ML needs to be carefully designed and programmed in order to accomplish various capabilities, such as classification, sorting, and analysis. ML and deep learning [37,38] as a specialized field have shown promise in numerous fields of engineering and study during the past ten years. Moreover, the ML techniques [16,39] are categorized into the following types:…”

Section: Models Used For Am In Power Systemsmentioning

confidence: 99%

A Review of Asset Management using Artificial Intelligence based Machine Learning models with applications for the Electric Power and Energy System

RAJORA,

Sanz-Bobi,

Tjernberg

et al. 2024

Preprint

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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.

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Section: Models Used For Am In Power Systemsmentioning

confidence: 99%

A Review of Asset Management using Artificial Intelligence based Machine Learning models with applications for the Electric Power and Energy System

RAJORA,

Sanz-Bobi,

Tjernberg

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…ML must be carefully designed and programmed to accomplish various capabilities, such as classification, sorting, and analysis. ML and deep learning [41, 42] as a specialized field have shown promise in numerous fields of engineering and study during the past ten years. Moreover, the ML techniques [17, 43] are categorized into the following types: Supervised learning Unsupervised learning Reinforced learning Ensemble learning …”

Section: Models Used For Am In Power Systemsmentioning

confidence: 99%

A review of asset management using artificial intelligence‐based machine learning models: Applications for the electric power and energy system

Rajora,

Sanz‐Bobi,

Tjernberg

et al. 2024

IET Generation Trans & Dist

View full text Add to dashboard Cite

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, insights into the transformative potential of ML in shaping the future of power system asset management are provided.

show abstract

Asset Management in Smart Grids: A Review

Cited by 2 publications

References 7 publications

A Review of Asset Management using Artificial Intelligence based Machine Learning models with applications for the Electric Power and Energy System

A Review of Asset Management using Artificial Intelligence based Machine Learning models with applications for the Electric Power and Energy System

A review of asset management using artificial intelligence‐based machine learning models: Applications for the electric power and energy system

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