Smart Grids based on Quantum Computing in the present-day Energy Systems for Fault Diagnosis

This paper's goal is to investigate how quantum computing might be used to optimize power systems and to address some of the difficulties that quantum computers may encounter, along with solutions. The fundamental ideas of quantum computing are also covered, along with how they differ from classical computations. Within the framework of smart grids, quantum computing (QC) presents itself as a next-generation alternative approach to address impending computational difficulties. QC is a relatively new but exciting technology that uses the special properties of quantum mechanics to analyze data and perform calculations. This new paradigm can solve optimization, simulation, and machine learning issues more effectively and quickly than ever before by overcoming the obstacle of computational constraints. Recent significant advancements in the development of sophisticated quantum hardware and software techniques have increased the viability of applying QC in a variety of research fields, including smart grids. It is clear that a great deal of research has already been done, and that research is remarkably ongoing. As a result, this article further defines the prospective smart grid applications and presents the research findings of the most current articles, emphasizing their recommendations for applying QC approaches for diverse smart grid applications. It states their plans, methods, and outcomes. The limits of the most recent quantum computers are also discussed in this research, along with how they might significantly affect the optimization of energy systems.

Authors

Publication Details

Journal
Advances in Nonlinear Variational Inequalities
Published
2024-10-09
DOI
https://doi.org/10.52783/anvi.v28.1851
Primary Topic
Smart Grid Security and Resilience
Type
article
Field-Weighted Citation Impact
0.00
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article

Smart Grids based on Quantum Computing in the present-day Energy Systems for Fault Diagnosis

Sumit Kumar
Advances in Nonlinear Variational Inequalities
Smart Grid Security and Resilience
article

Smart Grids based on Quantum Computing in the present-day Energy Systems for Fault Diagnosis

Sumit Kumar
article en

Abstract

This paper's goal is to investigate how quantum computing might be used to optimize power systems and to address some of the difficulties that quantum computers may encounter, along with solutions. The fundamental ideas of quantum computing are also covered, along with how they differ from classical computations. Within the framework of smart grids, quantum computing (QC) presents itself as a next-generation alternative approach to address impending computational difficulties. QC is a relatively new but exciting technology that uses the special properties of quantum mechanics to analyze data and perform calculations. This new paradigm can solve optimization, simulation, and machine learning issues more effectively and quickly than ever before by overcoming the obstacle of computational constraints. Recent significant advancements in the development of sophisticated quantum hardware and software techniques have increased the viability of applying QC in a variety of research fields, including smart grids. It is clear that a great deal of research has already been done, and that research is remarkably ongoing. As a result, this article further defines the prospective smart grid applications and presents the research findings of the most current articles, emphasizing their recommendations for applying QC approaches for diverse smart grid applications. It states their plans, methods, and outcomes. The limits of the most recent quantum computers are also discussed in this research, along with how they might significantly affect the optimization of energy systems.

Advances in Nonlinear Variational InequalitiesVol. 28(2)
Openalex Percentile: Top 76%
Smart Grid Security and Resilience
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Smart Grids based on Quantum Computing in the present-day Energy Systems for Fault Diagnosis — Sumit Kumar · Advances in Nonlinear Variational Inequalities (2024) | TGRS Research Map | TGRS