Application of Firefly Algorithm for AGC Under Deregulated Power System

  • Tulasichandra Sekhar Gorripotu
  • Rabindra Kumar Sahu
  • Sidhartha Panda
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 31)


In this paper, Proportional–Integral–Derivative controller with derivative Filter (PIDF) is proposed for Automatic Generation Control (AGC) problem of four area reheat thermal power systems under deregulated environment by considering the physical constraints such as Generation Rate Constraint (GRC) and Governor Dead Band (GDB) nonlinearity. The system is investigated in all possible scenarios under deregulated environment. The gains of the controllers are optimized using an Integral of Time multiplied by Absolute value of Error (ITAE) criterion employing of Firefly Algorithm (FA).The performance of some diverse classical controllers such as Integral (I), Proportional–Integral (PI) and PIDF controllers are compared under poolco based scenario. Simulation results reveal that the performance of the system is better with PIDF controller compared to others.


Automatic generation control (AGC) Firefly algorithm (FA) Generation rate constraint (GRC) Governor dead band (GDB) Deregulated 


  1. 1.
    Elgerd, O.I.: Electric Energy Systems Theory—An Introduction. Tata McGraw Hill, New Delhi (2000)Google Scholar
  2. 2.
    Bevrani, H.: Robust Power System Frequency Control. Springer, Berlin (2009)CrossRefMATHGoogle Scholar
  3. 3.
    Bervani, H., Hiyama, T.: Intelligent Automatic Generation Control. CRC Press, Boca Raton (2011)Google Scholar
  4. 4.
    Donde, V., Pai, M.A., Hiskens, I.A.: Simulation and optimization in an AGC system after deregulation. IEEE Trans. Power Syst. 16, 481–489 (2011)CrossRefGoogle Scholar
  5. 5.
    Bhatt, P., Roy, R., Ghoshal, S.P.: Optimized multi area AGC simulation in restructured power systems. Int. J. Electr. Power Energy Syst. 32(4), 311–332 (2010)CrossRefGoogle Scholar
  6. 6.
    Sahu, R.K., Panda, S., Rout, U.K.: DE optimized parallel 2-DOF PID controller for load frequency control of power system with governor dead-band nonlinearity. Int. J. Electr. Power Energy Syst. 49(1), 19–33 (2013)CrossRefGoogle Scholar
  7. 7.
    Saikia, L.C., Mishra, S., Sinha, N., Nanda, J.: Automatic generation control of a multi area hydrothermal system using reinforced learning neural network controller. Int. J. Electr. Power Energy Syst. 33(4), 1101–1108 (2011)CrossRefGoogle Scholar
  8. 8.
    Shabani, H., Vahidi, B., Ebrahimpour, M.: A robust PID controller based on imperialist competitive algorithm for load-frequency control of power systems. ISA Trans. 52(1), 88–95 (2013)CrossRefGoogle Scholar
  9. 9.
    Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, UK (2008)Google Scholar
  10. 10.
    Chandrasekaran, K., Simon, S.P., Padhy, N.P.: Binary real coded firefly algorithm for solving unit commitment problem. Inf. Sci. 249, 67–84 (2013)CrossRefGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Tulasichandra Sekhar Gorripotu
    • 1
  • Rabindra Kumar Sahu
    • 1
  • Sidhartha Panda
    • 1
  1. 1.Department of Electrical EngineeringVeer Surendra Sai University of Technology (VSSUT)BurlaIndia

Personalised recommendations