Grand Salmon Run Algorithm for Solving Optimal Reactive Power Dispatch Problem

K. Lenin; B. Ravindranath Reddy; M. Surya Kalavathi

doi:doi:10.11648/j.ijepe.20140302.16

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Grand Salmon Run Algorithm for Solving Optimal Reactive Power Dispatch Problem

K. Lenin, B. Ravindranath Reddy, M. Surya Kalavathi

Published in International Journal of Energy and Power Engineering (Volume 3, Issue 2)

Received: 4 April 2014 Accepted: 14 April 2014 Published: 30 April 2014

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Abstract

The chief aspect of solving Optimal Reactive Power Dispatch Problem (ORPD) is to minimize the real power loss and also to keep the voltage profile within the limits. In this paper, a new metaheuristic optimizing algorithm that is the simulation of “Grand Salmon Run” (GSR) is developed. The salmon run phenomena is one of the grand annual natural actions occurrence in the North America, where millions of salmons travel through mountain streams for spawn. The proposed GSR has been validated, by applying it on standard IEEE 30 bus test system. The results have been compared to other heuristics methods and the simulation results reveals about the good performance of the proposed algorithm

Published in	International Journal of Energy and Power Engineering (Volume 3, Issue 2)
DOI	10.11648/j.ijepe.20140302.16
Page(s)	77-82
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), 2014. Published by Science Publishing Group

Keywords

Nature-Inspired Algorithm, Salmon Run Metaheuristic, Optimal Reactive Power, Transmission Loss

References

[1]	H.W. Dommel, W.F. Tinney. Optimal power flow solutions. IEEE, Trans. On power Apparatus and Systems, VOL. PAS-87, octobre 1968,pp.1866-1876.
[2]	Lee K, Park Y, Ortiz J. A. United approach to optimal real and reactive power dispatch. IEEE Trans Power Appar. Syst. 1985; 104(5):1147-53.
[3]	Y. Y. Hong, D.I. Sun, S. Y. Lin and C. J. Lin. Multi-year multi-case optimal AVR planning. IEEE Trans. Power Syst., vol.5 , no.4, pp.1294-1301,Nov.1990.
[4]	J. A. Momoh, S. X. GUO, E .C. Ogbuobiri, and R. Adapa. The quadratic interior point method solving power system optimization problems. IEEE Trans. Power Syst. vol. 9, no. 3, pp. 1327-1336,Aug.1994.
[5]	S. Granville. Optimal Reactive Dispatch Through Interior Point Methods. IEEE Trans. Power Syst. vol. 9, no. 1, pp. 136-146, Feb. 1994.
[6]	J.A. Momoh, J. Z. Zhu. Improved interior point method for OPF problems. IEEE Trans. On power systems; Vol. 14, No. 3, pp. 1114-1120, August 1999.
[7]	Y.C. Wu, A. S. Debs, and R.E. Marsten. A Direct nonlinear predictor-corrector primal-dual interior point algorithm for optimal power flows. IEEE Transactions on power systems Vol. 9, no. 2, pp 876-883, may 1994.
[8]	L.L .Lai, J.T. Ma, R. Yokoma, M. Zhao. Improved genetic algorithms for optimal power flow under both normal and contingent operation states. Electrical Power & Energy System, Vol. 19, No. 5, p. 287-292, 1997.
[9]	Q.H. Wu, Y.J.Cao, and J.Y. Wen. Optimal reactive power dispatch using an adaptive genetic algorithm. Int. J. Elect. Power Energy Syst. Vol 20. Pp. 563-569; Aug 1998.
[10]	B. Zhao, C. X. Guo, and Y.J. CAO. Multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Trans. Power Syst. Vol. 20, no. 2, pp. 1070-1078, May 2005.
[11]	J. G. Vlachogiannis, K.Y. Lee. A Comparative study on particle swarm optimization for optimal steady-state performance of power systems. IEEE trans. on Power Syst., vol. 21, no. 4, pp. 1718-1728, Nov. 2006.
[12]	A.E. Eiben and J.E. Smith. Introduction to Evolutionary Computing. Springer-Verlag, Berlin, 2003.
[13]	Charles Darwin. The origin of species. John Murray, London, UK, 1859.
[14]	D. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, MA, 1996.
[15]	John H. Holland. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge, MA, USA, 1992.
[16]	Thomas B¨ack. Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, 1996.
[17]	Fister, M. Mernik, and B. Filipiˇc. Graph 3-coloring with a hybrid self-adaptive evolutionary algorithm. Computational optimization and applications, pages 1–32, 2012.
[18]	F. Neri and V. Tirronen. Recent advances in differential evolution: a survey and experimental analysis. Artificial Intelligence Review, 33(1–2):61–106, 2010.
[19]	J. Brest, S. Greiner, B. Boskovic, M. Mernik, and V. Zumer. Selfadapting control parameters in differential evolution: A comparative study on numerical benchmark problems. Evolutionary Computation, IEEE Transactions on, 10(6):646–657, 2006.
[20]	S. Das and P.N. Suganthan. Differential evolution: A survey of the state-of-the-art. Evolutionary Computation, IEEE Transactions on, 15(1):4–31, 2011.
[21]	John R. Koza. Genetic programming 2 - automatic discovery of reusable programs. Complex adaptive systems. MIT Press, 1994.
[22]	L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial Intelligence through Simulated Evolution. John Wiley, New York, USA, 1966.
[23]	B´enichou, O., Loverdo, C., Moreau, M., and Voituriez, R., Twodimensional intermittent search processes: An alternative to L´evy flight strategies, Phys. Rev., E74, 020102(R), (2006).
[24]	Mozaffari, A. Fathi, S. Behzadipour, The Great Salmon Run: A novel bio-inspired algorithm for artificial system design and optimization, International Journal of Bio-Inspired Computation 4 (2012) 286-301.
[25]	Fathi, A. Mozaffari, Modeling a shape memory alloy actuator using an evolvable recursive black-box and hybrid heuristic algorithms inspired based on the annual migration of salmons in nature, Applied Soft Computing 14 (2014) 229-251.
[26]	L.F. Sundstom, E. Petersson, J.I. Johnsson, Heart rate responses to predation risk in Salmo trutta are affected by the rearing environment, International Journal of Fish Biology 67(2005) 1280–1286.
[27]	E.B. Taylor, A review of local adaptation in Salmonidae, with particular reference to Atlantic and Pacific salmon, International Journal of Aquaculture 98(1991) 185–207.

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APA Style

K. Lenin, B. Ravindranath Reddy, M. Surya Kalavathi. (2014). Grand Salmon Run Algorithm for Solving Optimal Reactive Power Dispatch Problem. International Journal of Energy and Power Engineering, 3(2), 77-82. https://doi.org/10.11648/j.ijepe.20140302.16

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ACS Style

K. Lenin; B. Ravindranath Reddy; M. Surya Kalavathi. Grand Salmon Run Algorithm for Solving Optimal Reactive Power Dispatch Problem. Int. J. Energy Power Eng. 2014, 3(2), 77-82. doi: 10.11648/j.ijepe.20140302.16

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AMA Style

K. Lenin, B. Ravindranath Reddy, M. Surya Kalavathi. Grand Salmon Run Algorithm for Solving Optimal Reactive Power Dispatch Problem. Int J Energy Power Eng. 2014;3(2):77-82. doi: 10.11648/j.ijepe.20140302.16

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@article{10.11648/j.ijepe.20140302.16,
  author = {K. Lenin and B. Ravindranath Reddy and M. Surya Kalavathi},
  title = {Grand Salmon Run Algorithm for Solving Optimal Reactive Power Dispatch Problem},
  journal = {International Journal of Energy and Power Engineering},
  volume = {3},
  number = {2},
  pages = {77-82},
  doi = {10.11648/j.ijepe.20140302.16},
  url = {https://doi.org/10.11648/j.ijepe.20140302.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20140302.16},
  abstract = {The chief aspect of solving Optimal Reactive Power Dispatch Problem (ORPD) is to minimize the real power loss and also to keep the voltage profile within the limits. In this paper, a new metaheuristic optimizing algorithm that is the simulation of “Grand Salmon Run” (GSR) is developed. The salmon run phenomena is one of the grand annual natural actions occurrence in the North America, where millions of salmons travel through mountain streams for spawn. The proposed GSR has been validated, by applying it on standard IEEE 30 bus test system. The results have been compared to other heuristics methods and the simulation results reveals about the good performance of the proposed algorithm},
 year = {2014}
}

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TY  - JOUR
T1  - Grand Salmon Run Algorithm for Solving Optimal Reactive Power Dispatch Problem
AU  - K. Lenin
AU  - B. Ravindranath Reddy
AU  - M. Surya Kalavathi
Y1  - 2014/04/30
PY  - 2014
N1  - https://doi.org/10.11648/j.ijepe.20140302.16
DO  - 10.11648/j.ijepe.20140302.16
T2  - International Journal of Energy and Power Engineering
JF  - International Journal of Energy and Power Engineering
JO  - International Journal of Energy and Power Engineering
SP  - 77
EP  - 82
PB  - Science Publishing Group
SN  - 2326-960X
UR  - https://doi.org/10.11648/j.ijepe.20140302.16
AB  - The chief aspect of solving Optimal Reactive Power Dispatch Problem (ORPD) is to minimize the real power loss and also to keep the voltage profile within the limits. In this paper, a new metaheuristic optimizing algorithm that is the simulation of “Grand Salmon Run” (GSR) is developed. The salmon run phenomena is one of the grand annual natural actions occurrence in the North America, where millions of salmons travel through mountain streams for spawn. The proposed GSR has been validated, by applying it on standard IEEE 30 bus test system. The results have been compared to other heuristics methods and the simulation results reveals about the good performance of the proposed algorithm
VL  - 3
IS  - 2
ER  -

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Author Information

K. Lenin

Jawaharlal Nehru Technological University Kukatpally, Hyderabad, India
B. Ravindranath Reddy

Jawaharlal Nehru Technological University Kukatpally, Hyderabad, India
M. Surya Kalavathi

Jawaharlal Nehru Technological University Kukatpally, Hyderabad, India

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Plain Text BibTeX RIS

APA Style

K. Lenin, B. Ravindranath Reddy, M. Surya Kalavathi. (2014). Grand Salmon Run Algorithm for Solving Optimal Reactive Power Dispatch Problem. International Journal of Energy and Power Engineering, 3(2), 77-82. https://doi.org/10.11648/j.ijepe.20140302.16

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ACS Style

K. Lenin; B. Ravindranath Reddy; M. Surya Kalavathi. Grand Salmon Run Algorithm for Solving Optimal Reactive Power Dispatch Problem. Int. J. Energy Power Eng. 2014, 3(2), 77-82. doi: 10.11648/j.ijepe.20140302.16

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AMA Style

K. Lenin, B. Ravindranath Reddy, M. Surya Kalavathi. Grand Salmon Run Algorithm for Solving Optimal Reactive Power Dispatch Problem. Int J Energy Power Eng. 2014;3(2):77-82. doi: 10.11648/j.ijepe.20140302.16

Copy | Download

@article{10.11648/j.ijepe.20140302.16,
  author = {K. Lenin and B. Ravindranath Reddy and M. Surya Kalavathi},
  title = {Grand Salmon Run Algorithm for Solving Optimal Reactive Power Dispatch Problem},
  journal = {International Journal of Energy and Power Engineering},
  volume = {3},
  number = {2},
  pages = {77-82},
  doi = {10.11648/j.ijepe.20140302.16},
  url = {https://doi.org/10.11648/j.ijepe.20140302.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20140302.16},
  abstract = {The chief aspect of solving Optimal Reactive Power Dispatch Problem (ORPD) is to minimize the real power loss and also to keep the voltage profile within the limits. In this paper, a new metaheuristic optimizing algorithm that is the simulation of “Grand Salmon Run” (GSR) is developed. The salmon run phenomena is one of the grand annual natural actions occurrence in the North America, where millions of salmons travel through mountain streams for spawn. The proposed GSR has been validated, by applying it on standard IEEE 30 bus test system. The results have been compared to other heuristics methods and the simulation results reveals about the good performance of the proposed algorithm},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Grand Salmon Run Algorithm for Solving Optimal Reactive Power Dispatch Problem
AU  - K. Lenin
AU  - B. Ravindranath Reddy
AU  - M. Surya Kalavathi
Y1  - 2014/04/30
PY  - 2014
N1  - https://doi.org/10.11648/j.ijepe.20140302.16
DO  - 10.11648/j.ijepe.20140302.16
T2  - International Journal of Energy and Power Engineering
JF  - International Journal of Energy and Power Engineering
JO  - International Journal of Energy and Power Engineering
SP  - 77
EP  - 82
PB  - Science Publishing Group
SN  - 2326-960X
UR  - https://doi.org/10.11648/j.ijepe.20140302.16
AB  - The chief aspect of solving Optimal Reactive Power Dispatch Problem (ORPD) is to minimize the real power loss and also to keep the voltage profile within the limits. In this paper, a new metaheuristic optimizing algorithm that is the simulation of “Grand Salmon Run” (GSR) is developed. The salmon run phenomena is one of the grand annual natural actions occurrence in the North America, where millions of salmons travel through mountain streams for spawn. The proposed GSR has been validated, by applying it on standard IEEE 30 bus test system. The results have been compared to other heuristics methods and the simulation results reveals about the good performance of the proposed algorithm
VL  - 3
IS  - 2
ER  -

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