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), 2024. 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)

References

[1]	Grainger, J., Stevenson, W., “Power System Analysis”. New York: McGraw–Hill, (1994).
[2]	R. J. Brown and W. F. Tinney, “Digital solutions for large power networks,” AIEE Trans. (Power App. Syst.), vol. 76, pp. 347-355, June 1957.
[3]	J. E. Van Ness, ‘Iteration methods for digital load flow studies,” AIEE Trans. (Pown App. Syst.), Aug. 1959, vol 78. pp. 583-588.
[4]	B. Stott, O. Alsac, “Fast Decoupled Load Flow”, IEEE Trans., 1974, PAS-93, pp. 859-869.
[5]	Holland JH, “Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence”, 1975, University of Michigan Press, Ann Arbor.
[6]	X. Yin and N. Germay “Investigations on solving the load flow problems by Genetic Algorithms” Electr. Power Syst. Res., 1991, 22, pp. 151-163.
[7]	H. A. Kubba and S. S. Mahmood “Genetic Algorithm based load flow solution problem in electrical power systems” Journal of Engineering, Vol. 15, December 2009.
[8]	K. P. Wong, A. Li and T. M. Y. Law, “Advanced constrained genetic algorithm load flow method”, IEE Proc.-Gener. Transm. Distrib., Vol. 146, No. 6, November 1999, pp. 609-616.
[9]	S. Shrawane and M. Diagavane, “Applications of Genetic Algorithm for power flow analysis” International Journal of Engineering Research & Technology (IJERT)” Vol. 2 Issue 9, September – 2013.
[10]	H. Udatha and M. D. Reddy, “Load Flow Analysis Using Real Coded Genetic Algorithm” Int. Journal of Engineering Research and Applications, Vol. 4, Issue 2 (Version 1), February 2014, pp. 522-527.
[11]	J. Kennedy, R. C. Eberhart, et al., “Particle swarm optimization”, In Proceedings of IEEE International conference on neural networks, 1995, volume 4, pages 1942–1948.
[12]	P. Acharjee and S. K. Goswami, “Chaotic particle swarm optimization based robust load flow”, Electrical Power and Energy Systems 32 (2010), pp. 141–146.
[13]	S. Mehfuz and S. Kumar “Two dimensional particle swarm optimization algorithm for load flow analysis” International Journal of Computational Intelligence Systems, Vol. 7, No. 6 (December 2014), 1074-1082.
[14]	D. Saini and N. Saini, “A Study of Load Flow Analysis Using Particle Swarm Optimization”, Int. Journal of Engineering Research and Applications, Vol. 5, Issue 1 (Part 1), January 2015, pp. 125-131.
[15]	P. Ghosh etal. “Applicationof Particle of Particle Swarm Optimization For Load-Flow Computation”, International Journal of Advanced Computational Engineering and Networking, Volume-4, Issue-7, Jul.-2016.
[16]	M. Dorigo, luca M. G., " Ant Colony system: A Cooperative learning approach to the Travelling Salesman Problem”, IEEE transaction on evolutionary computation, Vol. 1, No. 1, 1997.
[17]	J. G. Vlachogiannis, N. D. Hatziargyriou, “Ant Colony System-Based Algorithm for Constrained Load Flow Problem”, Ieee Transactions On Power Systems, VOL. 20, NO. 3, August 2005, pp. 1241-1248.
[18]	Y. Kumar etal. “Development of ANT Algorithm For Load Flow Analysis”, IEEE/PES Power System Conference and Exposition, 2009.
[19]	K. Upamanyu, Keshav Bansal and Miteshwar Singh “Ant Colony Based Load Flow Optimisation Using Matlab”, International Journal Of Engineering Development And Research, 2010, pp. 47-51.
[20]	Zadeh, L. A. "Fuzzy sets". Information and Control, 1965, 8 (3): pp. 338–353.
[21]	K. L. Lo, Y. J. Lin, W. H. Siew, “Fuzzy-Logic method for adjustment of various parameters in load flow calculation”, IEE proc., 1999, 146, pp. 276-282.
[22]	J. G. Viachogiannis, “Fuzzy logic applications in load flow studies”, IEE Proc. Gener. Transm. Distrib., 2001, 148, pp. 34-40.
[23]	P. R. Bijwe, M. Hanmandlu, V. N. Pande, “Fuzzy power flow solutions with reactive limits and multiple uncertainties”, Electr. Power Syst. Res., 2005, 76, pp. 145-152.
[24]	P. Acharjee and K. Ali, “Load flow analysis using decoupled fuzzy load flow under critical conditions”, International Journal of Engineering, Science and Technology, Vol. 3, No. 3, 2011, pp. 111-118
[25]	H. A. Kubba and Y. F. Hassan, “A Real-Time Fuzzy Load Flow and Contingency Analysis Based on Gaussian Distribution System”, Journal of Engineering, Vol. 21, August 2015.
[26]	Hagan MT, Demuth HB and Beale MH “Neural network design”,. PWS Publishing Company, Boston, vol 1, 1996.
[27]	Leonardo Paucar, and Marcos J. Rider, “Artificial Neural Networks for solving power flow problem in electric power systems,” Electric power system Research, Vol. 62, 2002, pp. 139-144.
[28]	A. Jain etal.“Stochastic Load Flow Analysis Using Artificial Neural Networks” Power Engineering Society General meeting, 2006, IEEE Montreal, Que, pp. 6.
[29]	J. Krishna, L. Srivastava, “Counterpropagation Neural Network for Solving Power Flow Problem”, International Journal of Electrical and Computer Engineering, 2006, pp, 350-355.
[30]	A. Karami, M. S. Mohammadi, “Radial basis function neural network for power system load flow”, Electrical Power and Energy Systems 30 (2008), pp. 60–66.
[31]	H. H. Müllera, M. J. Riderb, Carlos A. Castroa, “Artificial neural networks for load flow and external equivalents studies”, Electric Power Systems Research 80 (2010), pp. 1033–1041.
[32]	S. Berat and Mehmet, “Power flow analysis by Artificial Neural Network”, International Journal of Energy and Power Engineering, 2013; 2 (6), pp. 204-208.
[33]	M. Suresh, T. S. Sirish “Neural Networks Based Load Flow Analysis Of Radial Distribution Networks”, International Journal Of Soft Computing And Artificial Intelligence, Volume-2, Issue-2, Nov.-2014, pp. 41-44.
[34]	V V S Bhaskara Reddy, Sonia Bankuru, “Distribution Load flow using Artificial Neural Networks”, International Journal of Application or Innovation in Engineering & Management, Volume 3, Issue 12, December 2014, pp. 227-232.
[35]	Wael Abdullah Alsulami “Fast and Accurate Load Flow Solution for On-line Applications Using ANN”, Journal of Engineering Sciences & Information Technology Issue (II), Vol. I - June 2017, pp. 89-102.
[36]	Rick Rarick, Dan Simon, F. Eugenio Villaseca and Bharat Vyakaranam, “Biogeography-Based Optimization and the solution of the Power Flow Problem”, IEEE International Conference on Systems, Man, and Cybernetics” (2009), pp. 1003-1008.
[37]	M. Bezza and M. Nahid “14-Bus Load Flow Problem Solving By Firefly Algorithm”, American Journal of Innovative Research and Applied Sciences, (2019), pp. 395-402.
[38]	S. Mukherjee and P. K. Roy, “Moth-Flame Optimization Algorithm Based Load Flow Analysis of Ill-Conditioned Power Systems”, International Journal of Applied Evolutionary Computation, Volume 11 Issue 1 January-March 2020, pp. 1-27.
[39]	V. Gopala Krishna Rao, V. Bapiraju and G. Ravindranath,“Fuzzy Load Modeling And Load Flow Study Using Radial Basis Function (Rbf)”, Journal Of Theoretical And Applied Information Technology, 2005– 2009, Pp. 471-475.
[40]	T. O. Ting, K. P. wong and C. Y. Chung,“Hybrid constrained genetic algorithm/particle swarm optimisation load flow algorithm”, IET Gener. Transm. Distrib., 2008, Vol. 2, No. 6, pp. 800–812.
[41]	A. Singh et al. “Particle Swarm Optimization- Artificial Neural Network for Power System Load Flow”, International Journal of Power System Operation and Energy Management, Volume-1, Issue-2, 2011, pp. 73-82.
[42]	K. Gnanambal et al., “Three-phase power flow analysis in sequence component frame using Hybrid Particle Swarm Optimization”, Applied Soft Computing 11 (2011), pp. 1727–1734.
[43]	C. P. Salomon et al., “A Hybrid Particle Swarm Optimization Approach For Load-Flow Computation”, International Journal Of Innovative Computing, Information & Control, November 2013, Vol. 9, No. 11, pp. 4359-4372.
[44]	Elnaz Davoodi et al., “A hybrid Improved Quantum-behaved Particle Swarm Optimization–Simplex method (IQPSOS) to solve power system load flow problems”, Applied Soft Computing 21 (2014), pp. 171–179.
[45]	Christopher O. Ahiakwo et al., “Application of Neuro-Swarm Intelligence Technique ToLoad Flow Analysis”, American Journal of Engineering Research (AJER), Volume-7, Issue-8, pp. 94-103.

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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