http://iet.metastore.ingenta.com
1887

Optimal reactive power resources sizing for power system operations enhancement based on improved grey wolf optimiser

Optimal reactive power resources sizing for power system operations enhancement based on improved grey wolf optimiser

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In recent years, optimal sizing and location of reactive power resources are drawing much attention to help the operators of the utilities to enhance the power system operations. Therefore, this work presents a new version of grey wolf optimiser (GWO) to solve the problem of optimal reactive power resources sizing for power system operation enhancement. The proposed method which called improved grey wolf optimiser (IGWO) can be derived by modifying the exploration–exploitation balance in the conventional GWO to enhance its rate of convergence. Also, the weighted distance strategy is employed in the proposed IGWO to overcome the drawback of the conventional GWO. Optimal reactive power resources sizing problem is non-linear and non-convex optimisation problem. To solve this problem, different objective functions are used. These objective functions are minimisation of generating cost, minimisation of transmission power loss and voltage profile improvement. The validity and superiority of the proposed IGWO method are tested using three standard IEEE systems for normal and contingency conditions. Then the results are compared with those obtained from other recently published algorithms. The simulation results show that the proposed IGWO method is more accurate and efficient than other recently published algorithms.

References

    1. 1)
      • 1. Hazra, A., Das, S., Sarkar, P., et al: ‘Optimal allocation and sizing of multiple DG and capacitor banks considering load variations using water cycle algorithm’. 2017 4th Int. Conf. Power, Control Embedded Systems (ICPCES), Allahabad, India, March 2017, pp. 16.
    2. 2)
      • 2. El-Ela, A., El-Sehiemy, R., Kinawy, A., et al: ‘Optimal capacitor placement in distribution systems for power loss reduction and voltage profile improvement’, IET Gener. Transm. Distrib., 2016, 10, (5), pp. 12091221.
    3. 3)
      • 3. Othman, A.: ‘Optimal capacitor placement by enhanced bacterial foraging optimization (EBFO) with accurate thermal re-rating of critical cables’, Electr. Power Syst. Res., 2016, 140, pp. 671680.
    4. 4)
      • 4. Leite, J., Abril, I., Azevedo, M.: ‘Capacitor and passive filter placement in distribution systems by nondominated sorting genetic algorithm-II’, Electr. Power Syst. Res., 2017, 143, pp. 482489.
    5. 5)
      • 5. Araujo, L., Penido, D., Carneiro, S.Jr., et al: ‘Optimal unbalanced capacitor placement in distribution systems for voltage control and energy losses minimization’, Electr. Power Syst. Res., 2018, 154, pp. 110121.
    6. 6)
      • 6. El-fergany, A.A., Abdelaziz, A.Y.: ‘Efficient heuristic-based approach for multi-objective capacitor allocation in radial distribution networks’, IET Gener. Transm. Distrib., 2014, 8, (1), pp. 7080.
    7. 7)
      • 7. El-fergany, A.A., Abdelaziz, A.Y.: ‘Capacitor allocations in radial distribution networks using cuckoo search algorithm’, IET Gener. Transm. Distrib., 2014, 8, (2), pp. 223232.
    8. 8)
      • 8. Biswas, P., Mallipeddi, R., Suganthan, P., et al: ‘A multiobjective approach for optimal placement and sizing of distributed generators and capacitors in distribution network’, Appl. Soft Comput., 2017, 60, pp. 268280.
    9. 9)
      • 9. Gnanasekaran, N., Chandramohan, S., Kumar, P., et al: ‘Optimal placement of capacitors in radial distribution system using shark smell optimization algorithm’, Ain Shams Eng. J., 2016, 7, (2), pp. 907916.
    10. 10)
      • 10. Elrazaz, Z.S.: ‘Optimal allocation of reactors for light load operation. IEE Proc. Gener. Transm. Distrib., 2001, 148, (4), pp. 350354.
    11. 11)
      • 11. Sayadi, F., Esmaeili, S., Keynia, F.: ‘Feeder reconfiguration and capacitor allocation in the presence of non-linear loads using new P-PSO algorithm’, IET Gener. Transm. Distrib., 2016, 10, (10), pp. 23162326.
    12. 12)
      • 12. Muthukumar, K., Jayalalitha, S.: ‘Optimal placement and sizing of distributed generators and shunt capacitors for power loss minimization in radial distribution networks using hybrid heuristic search optimization technique’, Int. J. Electr. Power Energy Syst., 2016, 78, pp. 299319.
    13. 13)
      • 13. Pereira, J., Costa, G., Contreras, J., et al: ‘Optimal distributed generation and reactive power allocation in electrical distribution systems’, IEEE Trans. Sustain. Energy, 2016, 7, pp. 975984.
    14. 14)
      • 14. Mirjalili, S., Mirjalili, S.M., Lewis, A.: ‘Grey wolf optimizer’, Adv. Eng. Softw., 2014, 69, pp. 4661.
    15. 15)
      • 15. Zhang, Y., Zhou, J., Zheng, Y., et al: ‘Control optimisation for pumped storage unit in micro-grid with wind power penetration using improved grey wolf optimiser’, IET Gener. Transm. Distrib., 2017, 11, (13), pp. 32463256.
    16. 16)
      • 16. Khairuzzaman, A., Chaudhury, S.: ‘Multilevel thresholding using grey wolf optimizer for image segmentation’, Expert Syst. Appl., 2017, 86, pp. 6476.
    17. 17)
      • 17. Simsir, S., Taspinar, N.: ‘Pilot tones design using grey wolf optimizer for OFDM˝U IDMA system’, Phys. Commun., 2017, 25, pp. 259267.
    18. 18)
      • 18. Sen, Z., Yongquan, Z., Zhiming, L., et al: ‘PGrey wolf optimizer for unmanned combat aerial vehicle path planning’, Adv. Eng. Soft., 2016, 99, pp. 121136.
    19. 19)
      • 19. Kohli, M., Arora, S.: ‘Chaotic grey wolf optimization algorithm for constrained optimization problems’, J. Comput. Design Eng., 2017, in press.
    20. 20)
      • 20. Chandra, M., Agrawal, A., Kishor, A., et al: ‘Web service selection with global constraints using modified gray wolf optimizer’. Int. Conf. Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, September 2016, pp. 19891994.
    21. 21)
      • 21. Malik, M., Mohideen, E., Ali, L.: ‘Weighted distance grey wolf optimizer for global optimization problems’. IEEE Int. Conf. Computational Intelligence and Computing Research (ICCIC), Madurai, India, December 2015, pp. 16.
    22. 22)
      • 22. Elattar, E.: ‘A hybrid genetic algorithm and bacterial foraging approach for dynamic economic dispatch problem’, Int. J. Electr. Power Energy Syst., 2015, 69, pp. 1826.
    23. 23)
      • 23. Khorsandi, A., Hosseinian, S., Ghazanfari, A.: ‘Modified artificial bee colony algorithm based on fuzzy multi-objective technique for optimal power flow problem’, Electr. Power Syst. Res., 2013, 95, pp. 206213.
    24. 24)
      • 24. Bouchekara, H., Abidob, M., Boucherma, M.: ‘Optimal power flow using teaching-learning-based optimization technique’, Electr. Power Syst. Res., 2014, 114, pp. 4959.
    25. 25)
      • 25. Adaryani, M., Karami, A.: ‘Artificial bee colony algorithm for solving multi-objective optimal power flow problem’, Int. J. Electr. Power Energy Syst, 2013, 53, pp. 219230.
    26. 26)
      • 26. Bhattacharya, A., Chattopadhyay, P.: ‘Application of biogeography-based optimisation to solve different optimal power flow problems’, IET Gener. Transm. Distrib., 2011, 5, (1), pp. 7080.
    27. 27)
      • 27. Mohamed, A., El-Gaafary, A., Mohamed, Y., et al: ‘Design static VAR compensator controller using artificial neural network optimized by modify grey wolf optimization’. Int. Joint Conf. Neural Networks (IJCNN), Killarney, Ireland, July 2015, pp. 17.
    28. 28)
      • 28. Alsac, O., Stott, B.: ‘Optimal load flow with steady-state security’, IEEE Trans. Power Appar. Syst., 1974, 93, (3), pp. 745751.
    29. 29)
      • 29. Ayana, K., Kiliç, U, Barakli, B.: ‘Chaotic artificial bee colony algorithm based solution of security and transient stability constrained optimal power flow’, Int. J. Electr. Power Energy Syst., 2015, 54, pp. 136147.
    30. 30)
      • 30. Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: ‘GSA: a gravitational search algorithm’, Inf. Sci., 2009, 179, (13), pp. 22322248.
    31. 31)
      • 31. Karaboga, D., Basturk, B.: ‘A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm’, J. Global Optim., 2007, 39, (3), pp. 459471.
    32. 32)
      • 32. Rao, R., Savsani, V., Vakharia, D.: ‘Teaching learning based optimization: a novel method for constrained mechanical design optimization problems’, Comput.-Aided Des., 2011, 43, (3), pp. 303315.
    33. 33)
      • 33. Yang, X.S.: ‘Nature-inspired Metaheuristic algorithms’ (Luniver Press, Frome, UK, 2010).
    34. 34)
      • 34. Bayraktar, Z., Komurcu, M., Werner, D.: ‘Wind driven optimization (WDO): a novel nature-inspired optimization algorithm and its application to electromagnetics’. IEEE Antennas and Propagation Society Int. Symp. (APSURSI), Toronto, ON, Canada, July 2010, pp. 14.
    35. 35)
      • 35. Abido, M.A.: ‘Optimal power flow using particle swarm optimization’, Int. J. Electr. Power Energy Syst., 2002, 24, pp. 563571.
    36. 36)
      • 36. Radosavljevic, J., Klimenta, D., Jevtic, M., et al: ‘Optimal power flow using a hybrid optimization algorithm of particle swarm optimization and gravitational search algorithm’, Electr. Power Compon. Syst., 2015, 43, pp. 19581970.
    37. 37)
      • 37. Pandiarajan, K., Babulal, C.: ‘Fuzzy harmony search algorithm based optimal power flow for power system security enhancement’, Int. J. Electr. Power Energy Syst., 2016, 78, pp. 7279.
    38. 38)
      • 38. Mohameda, A., Mohamed, Y., El-Gaafary, A., et al: ‘Optimal power flow using moth swarm algorithm’, Electr. Power Syst. Res., 2017, 142, pp. 190206.
    39. 39)
      • 39. Matpower Matlab toolbox. Available at http://www.pserc.cornell.edu/matpower, accessed 18 March 2018.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2018.0053
Loading

Related content

content/journals/10.1049/iet-gtd.2018.0053
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address