Advertisement

Real-Coded Genetic Algorithm and Fuzzy Logic Approach for Real-Time Load-Tracking Performance of an Autonomous Power System

  • Abhik Banerjee
  • V. Mukherjee
  • S. P. Ghoshal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)

Abstract

This paper focuses on the application of real-coded genetic algorithm (RGA) to determine the optimal controller parameters of an autonomous power system model for its load tracking performance analysis. To determine the real-time parameters of the studied model, Sugeno fuzzy logic (SFL) is used. RGA is applied to obtain the controller parameters for transient response analysis under various operating conditions and fuzzy logic is applied to develop the rule base of the SFL model. The developed fuzzy system gives the on-line controller parameters for different operating conditions. Time-domain simulation of the investigated power system model reveals that the proposed RGA-SFL yields on-line, off-nominal controller parameters, resulting in on-line terminal voltage response. To show the efficiency and effectiveness of RGA, binary coded genetic algorithm is taken for the sake of comparison.

Keywords

Automatic voltage regulator load-tracking performance optimization real-coded genetic algorithm Sugeno fuzzy logic 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cheng, J.W.M., Galiana, F.D., McGillis, D.T.: Studies of bilateral contracts with respect to steady-state security in a deregulated environment. IEEE Transaction on Power Systems 13(3), 1020–1025 (1998)CrossRefGoogle Scholar
  2. 2.
    Wang, L., Lee, D.-J.: Load-tracking performance of an autonomous SOFC-based hybrid power generation/energy storage system. IEEE Transaction on Energy Conversions 25(1), 128–139 (2010)CrossRefGoogle Scholar
  3. 3.
    Astrom, K.J., Hang, C.C., Persson, P., et al.: Towards intelligent PID control. Automatica 28(1), 1–9 (1992)CrossRefGoogle Scholar
  4. 4.
    Krohling, R.A., Rey, J.P.: Design of optimal disturbance rejection PID controllers using genetic algorithm. IEEE Transaction on Evolutionary Computation 5(1), 78–82 (2001)CrossRefGoogle Scholar
  5. 5.
    Mukherjee, V., Ghoshal, S.P.: Intelligent particle swarm optimized fuzzy PID controller for AVR system. Electric Power Systems Research 72(12), 1689–1698 (2007)CrossRefGoogle Scholar
  6. 6.
    Devaraja, D., Selvabala, B.: Real coded genetic algorithm and fuzzy logic approach for real time tuning of proportional-integral-derivative controller in automatic voltage regulator system. Proc. IET Generation Transmission Distribution 3(1), 641–649 (2009)CrossRefGoogle Scholar
  7. 7.
    Sushil, K., Naresh, R.: Efficient real coded genetic algorithm to solve the non-convex hydrothermal scheduling problem. International Journal of Electrical Power and Energy Systems 29(10), 738–747 (2007)CrossRefGoogle Scholar
  8. 8.
    Ghoshal, S.P.: Multi-area frequency and tie-line power flow control with fuzzy logic based integral gain scheduling. Journal of The Institution of Engineers India Pt. EL 81, 135–141 (2003)Google Scholar
  9. 9.
    Sadat, H.: Power System Analysis. Tata-McGraw-Hill, India (2003)Google Scholar
  10. 10.
    Kundur, P.: Power System Stability and Control. Tata-McGraw-Hill, India (2006)Google Scholar
  11. 11.
    Chatterjee, A., Ghoshal, S.P., Mukherjee, V.: Chaotic ant swarm optimization for fuzzy-based tuning of power system stabilizer. International Journal of Electrical Power and Energy Systems 33(3), 657–672 (2011)CrossRefGoogle Scholar
  12. 12.
    Amjady, N., Nasiri-Rad, H.: Solution of nonconvex and non-smooth economic dispatch by a new adaptive real coded genetic algorithm. Expert Systems with Applications 37(7), 5239–5245 (2010)CrossRefGoogle Scholar
  13. 13.
    Chatterjee, A., Ghoshal, S.P., Mukherjee, V.: A comparative study of single input and dual input power system stabilizer by hybrid evolutionary programming. In: Proc. World Cong. Nature & Biologically Inspired Computing 2009, NaBIC 2009, pp. 1047–1052 (December 2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Abhik Banerjee
    • 1
  • V. Mukherjee
    • 2
  • S. P. Ghoshal
    • 3
  1. 1.Department of Electrical EngineeringAsansol Engineering CollegeAsansolIndia
  2. 2.Department of Electrical EngineeringIndian School of MinesDhanbadIndia
  3. 3.Department of Electrical EngineeringNational Institute of TechnologyDurgapurIndia

Personalised recommendations