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Year 2019, Volume: 19 Issue: 1, 37 - 47, 01.01.2019

Abstract

References

  • 1. K.Y. Lee, Y.M. Park, J.L. Ortiz, “A united approach to optimal real and reactive power dispatch”, IEEE Trans Power Appar Syst, vol. 5, no. 5 , pp. 1147-1153, May, 1985. [CrossRef] 2. J. Carpentier, “Optimal power flows”, Electr Power Energy Syst, vol. 1, no. 1, pp. 3-15, Apr, 1979. [CrossRef] 3. K. Mahadevan, P.S. Kannan, “Comprehensive learning particle swarm optimization for reactive power dispatch”, Appl Soft Comput, vol. 10, no. 2, pp. 641-652, Mar, 2010. [CrossRef] 4. A.A. Abou El Ela, M.A. Abido, S.R. Spea, “Differential evolution algorithm for optimal reactive power dispatch”, Electr Pow Syst Res, vol. 81, no. 2, pp. 458-464, Feb, 2011. [CrossRef] 5. A. Bhattacharya, P.K. Chattopadhyay, “Solution of optimal reactive power flow using biogeography-based optimization”, International Journal of Electrical and Computer Engineering, vol. 4, no. 3, pp. 621-629, 2010. 6. J. Radosavljevic, M. Jevtic, M. Milovanovic, “A solution to the ORPD problem and critical analysis of the results”, Electr Eng, vol. 100, no. 1, pp 253-265, Mar, 2018. [CrossRef] 7. A. Rajan, T. Malakar, “Optimal reactive power dispatch using hybrid Nelder-Mead simplex based firefly algorithm”, Electr Power Energy Syst, vol. 66, pp 9-24, Mar, 2015. [CrossRef] 8. A. Rajan, K. Jeevan, T. Malakar, “Weighted elitism based Ant Lion Optimizer to solve optimum VAr planning problem”, Appl Soft Comput, vol. 55, pp. 352-370, Jun, 2017. [CrossRef] 9. S. Mouassa, T. Bouktir, A. Salhi, “Ant lion optimizer for solving optimal reactive power dispatch problem in power systems”, Eng Sci Technol Int J, vol. 20, no. 3, pp. 885-895, Jun, 2017. [CrossRef] 10. B. Mandal, P. K.. Roy, “Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization”, Electr Power Energy Syst, vol. 53, pp. 123-134, Dec, 2013. [CrossRef] 11. K.b.o. Medani, S. Sayah, A. Bekrar, “Whale optimization algorithm based optimal reactive power dispatch: A case study of the Algerian power system”, Electr Power Syst Res, Oct, 2017, unpublished. 12. S. Dutta, S. Paul, P.K. Roy, “Optimal allocation of SVC and TCSC using quasi-oppositional chemical reaction optimization for solving multi-objective ORPD problem”, Journal of Electrical Systems and Information Technology, vol. 5, no. 1, pp. 83-98, May, 2018. [CrossRef] 13. B. Shaw, V. Mukherjee, S.P. Ghoshal, Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm, Electr Power Energy Syst, vol. 55, pp. 29-40, Feb, 2014. [CrossRef] 14. H. Singh, L. Srivastava, “Modified differential evolution algorithm for multi-objective VAR management”, Electr Power Energy Syst, vol. 55, pp. 731-740, Feb, 2014. [CrossRef] 15. G. Chen, L. Liu, Z. Zhang, S. Huang, “Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints”, Appl Soft Comput, vol. 50, pp. 58-70, Jan, 2017. [CrossRef] 16. G. Chen, L. Liu, P. Song, Y. Du, “Chaotic improved PSO-based multi-objective optimization for minimization of power losses and Lindex in power systems”, Energy Convers Manage, vol. 86, pp. 548-560, Oct, 2014. [CrossRef] 17. A. Ghasemi, K. Valipour, A. Tohidi, “Multi objective optimal reactive power dispatch using a new multi objective strategy”, Electr Power Energy Syst, vol. 57, pp. 318-334, May, 2014. [CrossRef] 18. M. Mehdinejad, B. Mohammadi-Ivatloo, R. Dadashzadeh-Bonab, K. Zare, “Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms”, Electr Power Energy Syst, vol. 83, pp. 104-116, Dec, 2016. [CrossRef] 19. M.Y. Cheng, D. Prayogo, “Symbiotic Organisms Search: A new metaheuristic optimization algorithm”, Computers & Structures, vol. 139, pp. 98-112, Jul, 2014. [CrossRef] 20. U. Guvenc, S. Duman, M.K. Dosoglu, et al., “Application of Symbiotic Organisms Search Algorithm to solve various economic load dispatch problems”, 2016 International Symposium on INnovations in Intelligent SysTems and Applications, Sinaia, Romania, 2016. [CrossRef] 21. B. Das, V. Mukherjee, D. Das, “DG placement in radial distribution network by symbiotic organisms search algorithm for real power loss minimization”, Applied Soft Computing, vol. 49, pp. 920-936, Dec, 2016. [CrossRef] 22. D.C. Secui, “A modified Symbiotic Organisms Search algorithm for large scale economic dispatch problem with valve-point effects”, Energy, vol. 113, pp. 366-384, Oct, 2016. [CrossRef] 23. A. Saha, A.K. Chakraborty, P. Das, “Quasi-reflection based symbiotic organisms search algorithm for solving static optimal power flow problem”, Scientia Iranica, Feb, 2018. 24. T.P. Runarsson, X. Yao, “Constrained evolutionary optimization,” in Evolutionary Optimization: International Series in Operations Research&Management Science, Boston, MA: Springer, 2003, vol. 48, pp. 87-113. [CrossRef] 25. R.D. Zimmerman, C.E. Murillo-Sanchez, R.J. Thomas, “MATPOWER: Steady-State Operations, Planning and Analysis Tools for Power Systems Research and Education”, IEEE Trans Power Syst, vol. 26, no. 1, pp. 12-19, Feb, 2011. [CrossRef] 26. The IEEE 30-bus test system. (2017, Mar 15). Retrieved from http://www.ee.washington.edu/research/pstca/pf30/pgtca30bus.htm
  • Enes Yalçın received the B.Sc degree in Electrical and Electronics Engineering from Kırıkkale University in 2006 and M.Sc degree in Electrical and Electronics Engineering from Kırıkkale University in 2010. He is currently a Technical Inspector in TEİAŞ and Ph.D student at Gazi University. His research interests include power system optimization, optimal power flow, grid integration of renewable energy sources and transmission system planning.
  • Müslüm Cengiz Taplamacıoğlu graduated from Department of Electrical and Electronics Engineering, Gazi University. He received the degrees of M.Sc. in Industrial Engineering from Gazi University and in Electrical and Electronics Engineering from Middle East Technical University and received the degree of Ph.D. in Electrical, Electronics and System Engineering from University of Wales (Cardiff, UK). He has been a full time Professor of the Electrical and Electronics Engineering since 2000. He is currently working as a Professor at Gazi University. His research areas consist of high voltage engineering, optimization of power system operation and control problems, renewable energy source integration problems, electrical field computation, power systems and protection devices.
  • Ertuğrul Çam received the B.Sc degree in Electrical and Electronics Engineering from Dokuz Eylul University in 1996, the M.Sc degree in Electrical and Electronics Engineering from Ege University in 1999 and the Ph.D degree in Mechanical Engineering from Kırıkkale University in 2004. He is currently working as a Professor at Kırıkkale University. His research areas consist of power system control, renewable energy sources, fuzzy logic.

The Adaptive Chaotic Symbiotic Organisms Search Algorithm Proposal for Optimal Reactive Power Dispatch Problem in Power Systems

Year 2019, Volume: 19 Issue: 1, 37 - 47, 01.01.2019

Abstract

DOI: 10.26650/electrica.2019.18008


This paper presents an adaptive chaotic
symbiotic organisms search algorithm (A-CSOS) for finding the solution of
optimal reactive power dispatch (ORPD) problem which is one of the main issues
of power system planning and operations. The most important advantage of
symbiotic organisms search algorithm (SOS) is that is not need any particular
algorithm parameters. However, the SOS algorithm has some features to be
enhanced, like falling into local minima and sluggish convergence. A-CSOS
algorithm with adding new and improved features like adaptivity and chaos to
conventional SOS algorithm is proposed to solve ORPD problem. The ORPD problem
is mainly focused on minimization of transmission loss (Ploss) and total
voltage deviation (TVD). To determine the optimal set points of control
variables including generator bus voltages, tap positions of transformers, and
reactive power outputs of shunt VAR compensators is very crucial for
minimization to Ploss and TVD. The proposed algorithm is implemented on IEEE
30-bus test power systems for ascertaining the performance of A-CSOS algorithm
on ORPD problem. The results showed that the proposed approach is up to 10.39%
better than many of which the latest algorithms in literature and encourage the
researchers to implement A-CSOS algorithm to ORPD problem. 

Cite this article as: Yalçın E,
Taplamacıoğlu MC, Çam E. “The Adaptive Chaotic Symbiotic Organisms Search
Algorithm Proposal for Optimal Reactive Power Dispatch Problem in Power
Systems”, Electrica, 2019; 19(1): 37-47.

References

  • 1. K.Y. Lee, Y.M. Park, J.L. Ortiz, “A united approach to optimal real and reactive power dispatch”, IEEE Trans Power Appar Syst, vol. 5, no. 5 , pp. 1147-1153, May, 1985. [CrossRef] 2. J. Carpentier, “Optimal power flows”, Electr Power Energy Syst, vol. 1, no. 1, pp. 3-15, Apr, 1979. [CrossRef] 3. K. Mahadevan, P.S. Kannan, “Comprehensive learning particle swarm optimization for reactive power dispatch”, Appl Soft Comput, vol. 10, no. 2, pp. 641-652, Mar, 2010. [CrossRef] 4. A.A. Abou El Ela, M.A. Abido, S.R. Spea, “Differential evolution algorithm for optimal reactive power dispatch”, Electr Pow Syst Res, vol. 81, no. 2, pp. 458-464, Feb, 2011. [CrossRef] 5. A. Bhattacharya, P.K. Chattopadhyay, “Solution of optimal reactive power flow using biogeography-based optimization”, International Journal of Electrical and Computer Engineering, vol. 4, no. 3, pp. 621-629, 2010. 6. J. Radosavljevic, M. Jevtic, M. Milovanovic, “A solution to the ORPD problem and critical analysis of the results”, Electr Eng, vol. 100, no. 1, pp 253-265, Mar, 2018. [CrossRef] 7. A. Rajan, T. Malakar, “Optimal reactive power dispatch using hybrid Nelder-Mead simplex based firefly algorithm”, Electr Power Energy Syst, vol. 66, pp 9-24, Mar, 2015. [CrossRef] 8. A. Rajan, K. Jeevan, T. Malakar, “Weighted elitism based Ant Lion Optimizer to solve optimum VAr planning problem”, Appl Soft Comput, vol. 55, pp. 352-370, Jun, 2017. [CrossRef] 9. S. Mouassa, T. Bouktir, A. Salhi, “Ant lion optimizer for solving optimal reactive power dispatch problem in power systems”, Eng Sci Technol Int J, vol. 20, no. 3, pp. 885-895, Jun, 2017. [CrossRef] 10. B. Mandal, P. K.. Roy, “Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization”, Electr Power Energy Syst, vol. 53, pp. 123-134, Dec, 2013. [CrossRef] 11. K.b.o. Medani, S. Sayah, A. Bekrar, “Whale optimization algorithm based optimal reactive power dispatch: A case study of the Algerian power system”, Electr Power Syst Res, Oct, 2017, unpublished. 12. S. Dutta, S. Paul, P.K. Roy, “Optimal allocation of SVC and TCSC using quasi-oppositional chemical reaction optimization for solving multi-objective ORPD problem”, Journal of Electrical Systems and Information Technology, vol. 5, no. 1, pp. 83-98, May, 2018. [CrossRef] 13. B. Shaw, V. Mukherjee, S.P. Ghoshal, Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm, Electr Power Energy Syst, vol. 55, pp. 29-40, Feb, 2014. [CrossRef] 14. H. Singh, L. Srivastava, “Modified differential evolution algorithm for multi-objective VAR management”, Electr Power Energy Syst, vol. 55, pp. 731-740, Feb, 2014. [CrossRef] 15. G. Chen, L. Liu, Z. Zhang, S. Huang, “Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints”, Appl Soft Comput, vol. 50, pp. 58-70, Jan, 2017. [CrossRef] 16. G. Chen, L. Liu, P. Song, Y. Du, “Chaotic improved PSO-based multi-objective optimization for minimization of power losses and Lindex in power systems”, Energy Convers Manage, vol. 86, pp. 548-560, Oct, 2014. [CrossRef] 17. A. Ghasemi, K. Valipour, A. Tohidi, “Multi objective optimal reactive power dispatch using a new multi objective strategy”, Electr Power Energy Syst, vol. 57, pp. 318-334, May, 2014. [CrossRef] 18. M. Mehdinejad, B. Mohammadi-Ivatloo, R. Dadashzadeh-Bonab, K. Zare, “Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms”, Electr Power Energy Syst, vol. 83, pp. 104-116, Dec, 2016. [CrossRef] 19. M.Y. Cheng, D. Prayogo, “Symbiotic Organisms Search: A new metaheuristic optimization algorithm”, Computers & Structures, vol. 139, pp. 98-112, Jul, 2014. [CrossRef] 20. U. Guvenc, S. Duman, M.K. Dosoglu, et al., “Application of Symbiotic Organisms Search Algorithm to solve various economic load dispatch problems”, 2016 International Symposium on INnovations in Intelligent SysTems and Applications, Sinaia, Romania, 2016. [CrossRef] 21. B. Das, V. Mukherjee, D. Das, “DG placement in radial distribution network by symbiotic organisms search algorithm for real power loss minimization”, Applied Soft Computing, vol. 49, pp. 920-936, Dec, 2016. [CrossRef] 22. D.C. Secui, “A modified Symbiotic Organisms Search algorithm for large scale economic dispatch problem with valve-point effects”, Energy, vol. 113, pp. 366-384, Oct, 2016. [CrossRef] 23. A. Saha, A.K. Chakraborty, P. Das, “Quasi-reflection based symbiotic organisms search algorithm for solving static optimal power flow problem”, Scientia Iranica, Feb, 2018. 24. T.P. Runarsson, X. Yao, “Constrained evolutionary optimization,” in Evolutionary Optimization: International Series in Operations Research&Management Science, Boston, MA: Springer, 2003, vol. 48, pp. 87-113. [CrossRef] 25. R.D. Zimmerman, C.E. Murillo-Sanchez, R.J. Thomas, “MATPOWER: Steady-State Operations, Planning and Analysis Tools for Power Systems Research and Education”, IEEE Trans Power Syst, vol. 26, no. 1, pp. 12-19, Feb, 2011. [CrossRef] 26. The IEEE 30-bus test system. (2017, Mar 15). Retrieved from http://www.ee.washington.edu/research/pstca/pf30/pgtca30bus.htm
  • Enes Yalçın received the B.Sc degree in Electrical and Electronics Engineering from Kırıkkale University in 2006 and M.Sc degree in Electrical and Electronics Engineering from Kırıkkale University in 2010. He is currently a Technical Inspector in TEİAŞ and Ph.D student at Gazi University. His research interests include power system optimization, optimal power flow, grid integration of renewable energy sources and transmission system planning.
  • Müslüm Cengiz Taplamacıoğlu graduated from Department of Electrical and Electronics Engineering, Gazi University. He received the degrees of M.Sc. in Industrial Engineering from Gazi University and in Electrical and Electronics Engineering from Middle East Technical University and received the degree of Ph.D. in Electrical, Electronics and System Engineering from University of Wales (Cardiff, UK). He has been a full time Professor of the Electrical and Electronics Engineering since 2000. He is currently working as a Professor at Gazi University. His research areas consist of high voltage engineering, optimization of power system operation and control problems, renewable energy source integration problems, electrical field computation, power systems and protection devices.
  • Ertuğrul Çam received the B.Sc degree in Electrical and Electronics Engineering from Dokuz Eylul University in 1996, the M.Sc degree in Electrical and Electronics Engineering from Ege University in 1999 and the Ph.D degree in Mechanical Engineering from Kırıkkale University in 2004. He is currently working as a Professor at Kırıkkale University. His research areas consist of power system control, renewable energy sources, fuzzy logic.
There are 4 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Enes Yalçın

M. Cengiz Taplamacıoğlu This is me

Ertuğrul Çam This is me

Publication Date January 1, 2019
Published in Issue Year 2019 Volume: 19 Issue: 1

Cite

APA Yalçın, E., Taplamacıoğlu, M. C., & Çam, E. (2019). The Adaptive Chaotic Symbiotic Organisms Search Algorithm Proposal for Optimal Reactive Power Dispatch Problem in Power Systems. Electrica, 19(1), 37-47.
AMA Yalçın E, Taplamacıoğlu MC, Çam E. The Adaptive Chaotic Symbiotic Organisms Search Algorithm Proposal for Optimal Reactive Power Dispatch Problem in Power Systems. Electrica. January 2019;19(1):37-47.
Chicago Yalçın, Enes, M. Cengiz Taplamacıoğlu, and Ertuğrul Çam. “The Adaptive Chaotic Symbiotic Organisms Search Algorithm Proposal for Optimal Reactive Power Dispatch Problem in Power Systems”. Electrica 19, no. 1 (January 2019): 37-47.
EndNote Yalçın E, Taplamacıoğlu MC, Çam E (January 1, 2019) The Adaptive Chaotic Symbiotic Organisms Search Algorithm Proposal for Optimal Reactive Power Dispatch Problem in Power Systems. Electrica 19 1 37–47.
IEEE E. Yalçın, M. C. Taplamacıoğlu, and E. Çam, “The Adaptive Chaotic Symbiotic Organisms Search Algorithm Proposal for Optimal Reactive Power Dispatch Problem in Power Systems”, Electrica, vol. 19, no. 1, pp. 37–47, 2019.
ISNAD Yalçın, Enes et al. “The Adaptive Chaotic Symbiotic Organisms Search Algorithm Proposal for Optimal Reactive Power Dispatch Problem in Power Systems”. Electrica 19/1 (January 2019), 37-47.
JAMA Yalçın E, Taplamacıoğlu MC, Çam E. The Adaptive Chaotic Symbiotic Organisms Search Algorithm Proposal for Optimal Reactive Power Dispatch Problem in Power Systems. Electrica. 2019;19:37–47.
MLA Yalçın, Enes et al. “The Adaptive Chaotic Symbiotic Organisms Search Algorithm Proposal for Optimal Reactive Power Dispatch Problem in Power Systems”. Electrica, vol. 19, no. 1, 2019, pp. 37-47.
Vancouver Yalçın E, Taplamacıoğlu MC, Çam E. The Adaptive Chaotic Symbiotic Organisms Search Algorithm Proposal for Optimal Reactive Power Dispatch Problem in Power Systems. Electrica. 2019;19(1):37-4.