World Academy of Science, Engineering and Technology
Published December 23, 2011 | Version 10528
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Intelligent Automatic Generation Control of Two Area Interconnected Power System using Hybrid Neuro Fuzzy Controller

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This paper presents the development and application of an adaptive neuro fuzzy inference system (ANFIS) based intelligent hybrid neuro fuzzy controller for automatic generation control (AGC) of two-area interconnected thermal power system with reheat non linearity. The dynamic response of the system has been studied for 1% step load perturbation in area-1. The performance of the proposed neuro fuzzy controller is compared against conventional proportional-integral (PI) controller, state feedback linear quadratic regulator (LQR) controller and fuzzy gain scheduled proportionalintegral (FGSPI) controller. Comparative analysis demonstrates that the proposed intelligent neuro fuzzy controller is the most effective of all in improving the transients of frequency and tie-line power deviations against small step load disturbances. Simulations have been performed using Matlab®.

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References

  • O. I. Elgerd, Electric Energy Systems Theory: An Introduction. New York: McGraw-Hill, 1982.
  • Kundur, P. Power system stability and control, McGraw-Hill, Inc.,1994.
  • Ibraheem, Prabhat Kumar, and Dwarka P. Kothari, "Recent philosophies of automatic generation control strategies in power systems," IEEE Transactions On Power Systems, vol. 20, no. 1, pp. 346-57, February 2005.
  • C. S. Indulkar and B. Raj, "Application of fuzzy controller to automatic generation control," Elect. Machines Power Syst., vol. 23, no. 2, pp. 209-220, Mar.-Apr. 1995.
  • Chang C.S., Fu W., "Area load-frequency control using fuzzy gain scheduling of PI controllers," Electric Power system Research, vol. 42, no. 2, pp. 145-52, 1997.
  • J. Talaq and F. Al-Basri, "Adaptive fuzzy gain scheduling for loadfrequency control," IEEE Trans. Power Syst., vol. 14, no. 1, pp. 145- 150, Feb. 1999.
  • D. K. Chaturvedi, P. S. Satsangi, and P. K. Kalra, "Load frequency control: A generalized neural network approach," Elect. Power Energy Syst., vol. 21, no. 6, pp. 405-415, Aug. 1999.
  • Y. L. Karnavas and D. P. Papadopoulos, "AGC for autonomous power system using combined intelligent techniques," Elect. Power Syst. Res., vol. 62, no. 3, pp. 225-239, Jul. 2002.
  • S. K. Aditya and D. Das, "Design of load frequency controllers using genetic algorithm for two area interconnected hydro power system," Elect. Power Compon. Syst., vol. 31, no. 1, pp. 81-94, Jan. 2003. [10] Ibhan Kocaarslan, Ertugrul Cam, "Fuzzy logic controller in interconnected electric power systems for load-frequency control," Electrical Power and Energy Systems, vol. 27,no. 8, pp. 542-549, 2005. [11] L. H. Hassan, H. A. F. Mohamed, M. Moghavvemi, S. S. Yang, "Automatic generation control of power system with fuzzy gain scheduling integral and derivative controllers", International Journal of Power, Energy and Artificial Intelligence, vol. 1, no. 1, pp. 29-33, August 2008. [12] Gayadhar Panda, Sidhartha Panda and Cemal Ardil, "Automatic Generation Control of Interconnected Power System with Generation Rate Constraints by Hybrid Neuro Fuzzy Approach," Int. J. of Electrical and Electronics Engineering, vol. 3, no. 9, pp. 532-537, 2009. [13] J.S.R. Jang, "ANFIS: Adaptive-Network-Based Fuzzy Inference System," IEEE Trans. Syst., Man, Cybern., vol. 23, no. 3, pp. 665–685, 1993. [14] Porter, B., "Optimal control of multivariable systems incorporahng integral feedback," Electronics Letters, vol 7, pp 170-172, 1971. [15] Reddoch, P., Julich, T. Tan, and Tacker, E., "Models and performance functional for load frequency control in interconnected power systems," IEEE Conf. on Decision and Control, Florida, Dec. 1971.