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Optimal Power Flow Solution Using Self–Evolving Brain–Storming Inclusive Teaching–Learning–Based Algorithm

  • K. R. Krishnanand
  • Syed Muhammad Farzan Hasani
  • Bijaya Ketan Panigrahi
  • Sanjib Kumar Panda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7928)

Abstract

In this paper, a new hybrid self-evolving algorithm is presented with its application to a highly nonlinear problem in electrical engineering. The optimal power flow problem described here focuses on the minimization of the fuel costs of the thermal units while maintaining the voltage stability at each of the load buses. There are various restrictions on acceptable voltage levels, capacitance levels of shunt compensation devices and transformer taps making it highly complex and nonlinear. The hybrid algorithm discussed here is a combination of the learning principles from Brain Storming Optimization algorithm and Teaching-Learning-Based Optimization algorithm, along with a self-evolving principle applied to the control parameter. The strategies used in the proposed algorithm makes it self-adaptive in performing the search over the multi-dimensional problem domain. The results on an IEEE 30 Bus system indicate that the proposed algorithm is an excellent candidate in dealing with the optimal power flow problems.

Keywords

Brain-Storming Optimization Non-dominated sorting Optimal power flow Teaching-learning-based optimization 

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References

  1. 1.
    Back, T., Fogel, D., Michalewicz, Z.: Handbook of evolutionary computation. Oxford University Press, New York (1997)CrossRefGoogle Scholar
  2. 2.
    Bakirtzis, A., Petridis, V., Kazarlis, S.: Genetic algorithm solution to the economic dispatch problem. IEE Gen. Trans. Dist. 141(4), 377–382 (1994)CrossRefGoogle Scholar
  3. 3.
    Gaing, Z.L.: Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans. on Power Syst. 18(3), 1187–1195 (2003)CrossRefGoogle Scholar
  4. 4.
    Abou El Ela, A.A., Abido, M.A., Spea, S.R.: Optimal power flow using differential evolution algorithm. Electric Power Systems Research 80(7), 878–885 (2010)CrossRefGoogle Scholar
  5. 5.
    Nayak, S.K., Krishnanand, K.R.: Panigrahi, B.K., Rout, P.K.: Application of Artificial Bee Colony to Economic Load Dispatch Problem with Ramp Rate Limits and Prohibited Operating Zones. In: IEEE Proc. on Nature and Biologically Inspired Computing, pp. 1237–1242 (2009)Google Scholar
  6. 6.
    Attia, A.F., Al-Turki, Y.A., Abusorrah, A.M.: Optimal Power Flow Using Adapted Genetic Algorithm with Adjusting Population Size. Electric Power Components and Systems 40(11), 1285–1299 (2012)CrossRefGoogle Scholar
  7. 7.
    Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design 43(3), 303–315 (2011)CrossRefGoogle Scholar
  8. 8.
    Krishnanand, K.R., Panigrahi, B.K., Rout, P.K., Mohapatra, A.: Application of Multi-Objective Teaching-Learning-Based Algorithm to an Economic Load Dispatch Problem with Incommensurable Objectives. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds.) SEMCCO 2011, Part I. LNCS, vol. 7076, pp. 697–705. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Shi, Y.: An Optimization Algorithm Based on Brainstorming Process. Int. J. of Swarm Intel. Res. 2(4), 35–62 (2011)CrossRefGoogle Scholar
  10. 10.
    Osborn, A.F.: Applied imagination: Principles and procedures of creative problem solving. Charles Scribner’s Son, New York (1963)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • K. R. Krishnanand
    • 1
  • Syed Muhammad Farzan Hasani
    • 1
  • Bijaya Ketan Panigrahi
    • 2
  • Sanjib Kumar Panda
    • 1
  1. 1.Electrical and Computer EngineeringNational University of SingaporeSingapore
  2. 2.Department of Electrical EngineeringIndian Institute of TechnologyDelhiIndia

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