access icon free Recurrent multi-objective differential evolution approach for reactive power management

© The Institution of Engineering and Technology
Received 26/05/2015, Accepted 07/09/2015, Revised 13/08/2015, Published 07/01/2016

This study proposes a novel recurrent multi-objective differential evolution (RMODE) algorithm to solve the constrained reactive power management (RPM) problem, which is a non-linear, multi-objective optimisation problem. Minimisation of total active power loss and improvement of voltage profile are considered as the objectives of the RPM problem. For RPM, generator bus voltage magnitudes, transformer tap settings and reactive power of capacitor/reactor are taken as the decision variables. In the proposed RMODE algorithm, the multi-objective differential evolution (MODE) algorithm has been applied repeatedly using the available Pareto-optimal solutions and re-initialising the remaining population. Thus, for each next cycle of the RMODE, the better values of best compromise solution have been obtained. Effectiveness of the proposed RMODE algorithm has been demonstrated for RPM in the standard IEEE-30 bus system and a practical 75-bus Indian system. Compared with multi-objective particle swarm optimisation (PSO), genetic algorithm toolbox for multi-objective optimisation, MODE and reported results using modified differential evolution and classical PSO, the proposed approach seems to be a promising alternative approach for solving RPM problem in practical power system.

Inspec keywords: power transformers; nonlinear programming; reactive power; power capacitors; evolutionary computation

Other keywords: RMODE algorithm; capacitor reactive power; constrained reactive power management problem; 75-bus Indian system; voltage profile improvement; generator bus voltage magnitudes; nonlinear optimisation problem; recurrent multiobjective differential evolution approach; IEEE-30 bus system; reactive power dispatch; transformer tap settings; reactor reactive power; total active power loss minimisation; RPM problem; multiobjective optimisation problem; Pareto-optimal solutions

Subjects: Power systems; Other power apparatus and electric machines; Transformers and reactors; Optimisation techniques

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
      • 17. Deb, K.: ‘Multi-objective optimization using evolutionary algorithm’ (John Wiley & Sons Ltd., 2010).
    5. 5)
      • 27. Srivastava, L.: ‘Artificial neural network approach to power system voltage security assessment’. PhD thesis, Department of Electrical Engineering, University of Roorkee, Roorkee, India, 1998.
    6. 6)
    7. 7)
      • 30. The MathWorks Inc.: ‘Global optimization toolbox’. Available at http://www.mathworks.com.
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
      • 28. Goldberg, D.E.: ‘Genetic algorithms in search optimization and machine learning’ (Addison-Wesley Publishing Company Inc., 1989).
    23. 23)
    24. 24)
    25. 25)
    26. 26)
    27. 27)
    28. 28)
    29. 29)
      • 34. Babu, B.V., Gujarathi, A.M., Katla, P., et al: ‘Strategies of multi-objective differential evolution (MODE) for optimization of adiabatic styrene reactor’. Proc. of Int. Conf. on Emerging Mechanical Technology: Macro to Nano, Pilani, India, 2007, (EMTMN-2007), pp. 243–250.
    30. 30)
      • 33. Daniela, Z.: ‘A comparative analysis of crossover variants in differential evolution’. Proc. of Int. Multiconference on Computer Science and Information Technology, Wisła, Poland, 2007, pp. 171–181.
    31. 31)
    32. 32)
    33. 33)
    34. 34)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2015.0648
Loading

Related content

content/journals/10.1049/iet-gtd.2015.0648
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading