CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Analysis of Distributed Generation Allocation and Sizing in Distribution Systems via a Multi-objective Particle Swarm Optimization and Improved Non dominated Sorting Genetic Algorithm-II

by Monica Deshmukh, Neeti Dugaya
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 133 - Number 4
Year of Publication: 2016
Authors: Monica Deshmukh, Neeti Dugaya
10.5120/ijca2016907632

Monica Deshmukh, Neeti Dugaya . Analysis of Distributed Generation Allocation and Sizing in Distribution Systems via a Multi-objective Particle Swarm Optimization and Improved Non dominated Sorting Genetic Algorithm-II. International Journal of Computer Applications. 133, 4 ( January 2016), 5-12. DOI=10.5120/ijca2016907632

@article{ 10.5120/ijca2016907632,
author = { Monica Deshmukh, Neeti Dugaya },
title = { Analysis of Distributed Generation Allocation and Sizing in Distribution Systems via a Multi-objective Particle Swarm Optimization and Improved Non dominated Sorting Genetic Algorithm-II },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 133 },
number = { 4 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 5-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume133/number4/23772-2016907632/ },
doi = { 10.5120/ijca2016907632 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:30:11.042125+05:30
%A Monica Deshmukh
%A Neeti Dugaya
%T Analysis of Distributed Generation Allocation and Sizing in Distribution Systems via a Multi-objective Particle Swarm Optimization and Improved Non dominated Sorting Genetic Algorithm-II
%J International Journal of Computer Applications
%@ 0975-8887
%V 133
%N 4
%P 5-12
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the recent era the distributed generation (DG) has a lot of power setups operation. The basic advantage of distribution generation includes reduce Power loss, eco; improve voltage, system upgrading postponement. Also it’s more reliable and environment friendly. We will compare optimization approach with the hybrid particle swarm optimization (HPSO) and the No dominated sorting Genetic Algorithm (NSGA-II).in this study for determining the optimal DG-unit’s size, power factor, and location in to reduce the real power loss in the whole system with HPSO algorithm we can find the solution considering maximization of system load and relative minimum power losses. The second algorithms is improved no dominated sorting genetic algorithm II (INSGA-II) with the help of which multi objective planning problem is resolved is also described here. Sample radial distribution feeder systems are compared here to find the validity of both above mentioned algorithm. In this way updating of the two parameters to find the most effective values has a higher chance of success as compared to any other metaheuristic methods.

References
  1. C. L. T. Borges and D. M. Falcao, “Impact of distributed generation allocation and sizing on reliability, losses and voltage profile,” in Proc. IEEE Power Tech Conf., Bologna, Italy, 2003, vol. 2, pp. 1–5.
  2. IEEE Standard for Interconnecting Distributed Resources with Electric Power systems, IEEE Std. 1547-2003, 2003, pp. 1–16.
  3. T. A. Short, Electric Power Distribution Handbook, 1st ed. Boca Raton, FL: CRC, 2003.
  4. B. A. de Souza and J. M. C. de Albuquerque, “Optimal placement of distributed generators networks using evolutionary programming,” in Proc. Transm. Distrib. Conf. Expo.: Latin Amer., 2006, pp.1–6.
  5. N. Acharya, P. Mahat, and N. Mithulananthan, “An analytical approach for DG allocation in primary distribution network,” Elect. Power Syst. Res., vol. 28, no. 10, pp. 669–678, Dec. 2006.
  6. Fahad S. Abu-Mouti, Student Member, IEEE, and M. E. El-Hawary, “Optimal Distributed Generation Allocation and Sizing in Distribution Systems via Artificial Bee Colony Algorithm” IEEE Transactions On Power Delivery, Vol. 26, No. 4, October 2011, 2090-2101
  7. M.M. Aman, G.B. Jasmon, A.H.A. Bakar, and H. Mokhlis, “A new approach for optimum simultaneous multi-DG distributed generation Units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm” Elsevier 18 January 2014, Energy (66) 202-215.
  8. Wanxing Sheng, Ke-Yan Liu,Yuan Liu, Xiaoli Meng, and Yunhua Li, “Optimal Placement and Sizing of Distributed Generation via an Improved Nondominated Sorting Genetic Algorithm II” IEEE Transactions On Power Delivery, Vol. 30, No. 2, April 2015
  9. P. Kessel and H. Glavitsch, “Estimating the voltage stability of a power system,” IEEE Trans. Power Del., vol. PWRD–1, no. 3, pp. 346–354, Jul. 1986.
  10. G. B. Jasmon and L. H. C. C. Lee, “Distribution network reduction for voltage instability analysis and loadflow calculations,” Int. J. Elect. Power Energy Syst., vol. 13, no. 1, pp. 1–3, 1991.
  11. Kennedy J, Eberhart R. Particle swarm optimization. Conference Particle Swarm Optim;4. p. 1942-8.
  12. Nguyen Cong H, Mithulananthan N, Bansal RC. Location and sizing of distributed generation units for loadability enhancement in primary feeder. IEEE Syst J 2013;7(4):797e806.
  13. Karaboga D. An idea based on honey bee swarm for numerical optimization. Techn Rep TR06. Erciyes: Erciyes Univ Press; 2005.
  14. Gözel T, Eminoglu U, Hocaoglu MH. A tool for voltage stability and optimization (VS&OP) in radial distribution systems using matlab graphical user interface (GUI). Simul Model Pract Theory 2008;16(5):505-18.
  15. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, Apr. 2002.
  16. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2nd ed. New York: Springer, 2009.
  17. M. Varadarajan and K. S. Swarup, “Network loss minimization with voltage security using differential evolution,” Elect. Power Syst. Res., vol. 78, no. 5, pp. 815–823, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Distributed generation (DG) HPSO INSGA-II Metaheuristic optimization algorithms Power losses reduction

Powered by PhDFocusTM