Load Flow Studies Using Intelligent Techniques: Review

Ganesh Kumar Jaiswal; Uma Nangia; Narender Kumar Jain

doi:doi:10.11648/j.ajnc.20211002.11

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Load Flow Studies Using Intelligent Techniques: Review

Ganesh Kumar Jaiswal, Uma Nangia, Narender Kumar Jain

Published in American Journal of Networks and Communications (Volume 10, Issue 2)

Received: 22 June 2021 Accepted: 28 July 2021 Published: 25 November 2021

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Abstract

Load flow studies are a vital tool for investigating best-operating conditions and proper future planning of power system network. All power system analysis calculations include load flow studies, which are probably the most significant and common. The load flow analysis is used to determine the magnitude and phase angle of the voltage at each bus, as well as the amount of real and reactive power flowing through each transmission system line. In order to solve non-linear load flow problems considering different constraints, several conventional and intelligent techniques have been developed. While conventional methods usually find a solution in a decent amount of time, they frequently involve numerical robustness issues, such as a narrow convergence region and an ill-conditioned system. The load flow analysis methods based on intelligent techniques do not rely on the starting values of the variables and perform better than conventional methods when the power system becomes highly stressed. This paper provides a comprehensive review of various intelligent techniques, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Fuzzy Logic (FL), Ant Colony Optimization (ACO), Artificial Neural Network (ANN), Artificial Bee Colony (ABC), and others, that are used under various defined conditions for load flow studies of various power system networks.

Published in	American Journal of Networks and Communications (Volume 10, Issue 2)
DOI	10.11648/j.ajnc.20211002.11
Page(s)	13-19
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Fuzzy Logic (FL), Ant Colony Optimization (ACO), Artificial Neural Network (ANN), Artificial Bee Colony (ABC)

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APA Style

Ganesh Kumar Jaiswal, Uma Nangia, Narender Kumar Jain. (2021). Load Flow Studies Using Intelligent Techniques: Review. American Journal of Networks and Communications, 10(2), 13-19. https://doi.org/10.11648/j.ajnc.20211002.11

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ACS Style

Ganesh Kumar Jaiswal; Uma Nangia; Narender Kumar Jain. Load Flow Studies Using Intelligent Techniques: Review. Am. J. Netw. Commun. 2021, 10(2), 13-19. doi: 10.11648/j.ajnc.20211002.11

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AMA Style

Ganesh Kumar Jaiswal, Uma Nangia, Narender Kumar Jain. Load Flow Studies Using Intelligent Techniques: Review. Am J Netw Commun. 2021;10(2):13-19. doi: 10.11648/j.ajnc.20211002.11

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@article{10.11648/j.ajnc.20211002.11,
  author = {Ganesh Kumar Jaiswal and Uma Nangia and Narender Kumar Jain},
  title = {Load Flow Studies Using Intelligent Techniques: Review},
  journal = {American Journal of Networks and Communications},
  volume = {10},
  number = {2},
  pages = {13-19},
  doi = {10.11648/j.ajnc.20211002.11},
  url = {https://doi.org/10.11648/j.ajnc.20211002.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20211002.11},
  abstract = {Load flow studies are a vital tool for investigating best-operating conditions and proper future planning of power system network. All power system analysis calculations include load flow studies, which are probably the most significant and common. The load flow analysis is used to determine the magnitude and phase angle of the voltage at each bus, as well as the amount of real and reactive power flowing through each transmission system line. In order to solve non-linear load flow problems considering different constraints, several conventional and intelligent techniques have been developed. While conventional methods usually find a solution in a decent amount of time, they frequently involve numerical robustness issues, such as a narrow convergence region and an ill-conditioned system. The load flow analysis methods based on intelligent techniques do not rely on the starting values of the variables and perform better than conventional methods when the power system becomes highly stressed. This paper provides a comprehensive review of various intelligent techniques, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Fuzzy Logic (FL), Ant Colony Optimization (ACO), Artificial Neural Network (ANN), Artificial Bee Colony (ABC), and others, that are used under various defined conditions for load flow studies of various power system networks.},
 year = {2021}
}

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TY  - JOUR
T1  - Load Flow Studies Using Intelligent Techniques: Review
AU  - Ganesh Kumar Jaiswal
AU  - Uma Nangia
AU  - Narender Kumar Jain
Y1  - 2021/11/25
PY  - 2021
N1  - https://doi.org/10.11648/j.ajnc.20211002.11
DO  - 10.11648/j.ajnc.20211002.11
T2  - American Journal of Networks and Communications
JF  - American Journal of Networks and Communications
JO  - American Journal of Networks and Communications
SP  - 13
EP  - 19
PB  - Science Publishing Group
SN  - 2326-8964
UR  - https://doi.org/10.11648/j.ajnc.20211002.11
AB  - Load flow studies are a vital tool for investigating best-operating conditions and proper future planning of power system network. All power system analysis calculations include load flow studies, which are probably the most significant and common. The load flow analysis is used to determine the magnitude and phase angle of the voltage at each bus, as well as the amount of real and reactive power flowing through each transmission system line. In order to solve non-linear load flow problems considering different constraints, several conventional and intelligent techniques have been developed. While conventional methods usually find a solution in a decent amount of time, they frequently involve numerical robustness issues, such as a narrow convergence region and an ill-conditioned system. The load flow analysis methods based on intelligent techniques do not rely on the starting values of the variables and perform better than conventional methods when the power system becomes highly stressed. This paper provides a comprehensive review of various intelligent techniques, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Fuzzy Logic (FL), Ant Colony Optimization (ACO), Artificial Neural Network (ANN), Artificial Bee Colony (ABC), and others, that are used under various defined conditions for load flow studies of various power system networks.
VL  - 10
IS  - 2
ER  -

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Author Information

Ganesh Kumar Jaiswal

Electrical Engineering Department, Delhi Technological University, Delhi, India
Uma Nangia

Electrical Engineering Department, Delhi Technological University, Delhi, India
Narender Kumar Jain

Electrical Engineering Department, Delhi Technological University, Delhi, India

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APA Style

Ganesh Kumar Jaiswal, Uma Nangia, Narender Kumar Jain. (2021). Load Flow Studies Using Intelligent Techniques: Review. American Journal of Networks and Communications, 10(2), 13-19. https://doi.org/10.11648/j.ajnc.20211002.11

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ACS Style

Ganesh Kumar Jaiswal; Uma Nangia; Narender Kumar Jain. Load Flow Studies Using Intelligent Techniques: Review. Am. J. Netw. Commun. 2021, 10(2), 13-19. doi: 10.11648/j.ajnc.20211002.11

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AMA Style

Ganesh Kumar Jaiswal, Uma Nangia, Narender Kumar Jain. Load Flow Studies Using Intelligent Techniques: Review. Am J Netw Commun. 2021;10(2):13-19. doi: 10.11648/j.ajnc.20211002.11

Copy | Download

@article{10.11648/j.ajnc.20211002.11,
  author = {Ganesh Kumar Jaiswal and Uma Nangia and Narender Kumar Jain},
  title = {Load Flow Studies Using Intelligent Techniques: Review},
  journal = {American Journal of Networks and Communications},
  volume = {10},
  number = {2},
  pages = {13-19},
  doi = {10.11648/j.ajnc.20211002.11},
  url = {https://doi.org/10.11648/j.ajnc.20211002.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20211002.11},
  abstract = {Load flow studies are a vital tool for investigating best-operating conditions and proper future planning of power system network. All power system analysis calculations include load flow studies, which are probably the most significant and common. The load flow analysis is used to determine the magnitude and phase angle of the voltage at each bus, as well as the amount of real and reactive power flowing through each transmission system line. In order to solve non-linear load flow problems considering different constraints, several conventional and intelligent techniques have been developed. While conventional methods usually find a solution in a decent amount of time, they frequently involve numerical robustness issues, such as a narrow convergence region and an ill-conditioned system. The load flow analysis methods based on intelligent techniques do not rely on the starting values of the variables and perform better than conventional methods when the power system becomes highly stressed. This paper provides a comprehensive review of various intelligent techniques, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Fuzzy Logic (FL), Ant Colony Optimization (ACO), Artificial Neural Network (ANN), Artificial Bee Colony (ABC), and others, that are used under various defined conditions for load flow studies of various power system networks.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Load Flow Studies Using Intelligent Techniques: Review
AU  - Ganesh Kumar Jaiswal
AU  - Uma Nangia
AU  - Narender Kumar Jain
Y1  - 2021/11/25
PY  - 2021
N1  - https://doi.org/10.11648/j.ajnc.20211002.11
DO  - 10.11648/j.ajnc.20211002.11
T2  - American Journal of Networks and Communications
JF  - American Journal of Networks and Communications
JO  - American Journal of Networks and Communications
SP  - 13
EP  - 19
PB  - Science Publishing Group
SN  - 2326-8964
UR  - https://doi.org/10.11648/j.ajnc.20211002.11
AB  - Load flow studies are a vital tool for investigating best-operating conditions and proper future planning of power system network. All power system analysis calculations include load flow studies, which are probably the most significant and common. The load flow analysis is used to determine the magnitude and phase angle of the voltage at each bus, as well as the amount of real and reactive power flowing through each transmission system line. In order to solve non-linear load flow problems considering different constraints, several conventional and intelligent techniques have been developed. While conventional methods usually find a solution in a decent amount of time, they frequently involve numerical robustness issues, such as a narrow convergence region and an ill-conditioned system. The load flow analysis methods based on intelligent techniques do not rely on the starting values of the variables and perform better than conventional methods when the power system becomes highly stressed. This paper provides a comprehensive review of various intelligent techniques, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Fuzzy Logic (FL), Ant Colony Optimization (ACO), Artificial Neural Network (ANN), Artificial Bee Colony (ABC), and others, that are used under various defined conditions for load flow studies of various power system networks.
VL  - 10
IS  - 2
ER  -

Copy | Download