Commenced in January 2007
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Comparative Dynamic Performance of Load Frequency Control of Nonlinear Interconnected Hydro-Thermal System Using Intelligent Techniques

Authors: Banaja Mohanty, Prakash Kumar Hota

Abstract:

This paper demonstrates dynamic performance evaluation of load frequency control (LFC) with different intelligent techniques. All non-linearities and physical constraints have been considered in simulation studies such as governor dead band (GDB), generation rate constraint (GRC) and boiler dynamics. The conventional integral time absolute error has been considered as objective function. The design problem is formulated as an optimisation problem and particle swarm optimisation (PSO), bacterial foraging optimisation algorithm (BFOA) and differential evolution (DE) are employed to search optimal controller parameters. The superiority of the proposed approach has been shown by comparing the results with published fuzzy logic control (FLC) for the same interconnected power system. The comparison is done using various performance measures like overshoot, undershoot, settling time and standard error criteria of frequency and tie-line power deviation following a step load perturbation (SLP). It is noticed that, the dynamic performance of proposed controller is better than FLC. Further, robustness analysis is carried out by varying the time constants of speed governor, turbine, tie-line power in the range of +40% to -40% to demonstrate the robustness of the proposed DE optimized PID controller.

Keywords: Automatic generation control, governor dead band, generation rate constraint, differential evolution.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1126015

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References:


[1] M. L Kothari., B. L Kaul., J Nanda, “Automatic generation control of hydrothermal system”, Journal of Inst. of Engg. (India), vol. 61(2), Oct. 1980, pp. 85–91.
[2] J Nanda and A. Mangla, “Some new findings on automatic generation control of an Interconnected Hydrothermal system with conventional controllers”, IEEE transaction on energy conversion Vol. 21(1), March 2006, pp187-194.
[3] J. Nanda, S. Mishra and L. C. Saikia, “Maiden Application of Bacteria foraging-based optimization technique in Multi-area Automatic generation control”, IEEE Transactions on power systems, Vol.24, (2), May 2009, pp.602-609.
[4] H.Gozde and M. C. Taplamacioglu, “Automatic generation control application with craziness based particle swarm optimization in a thermal power system” Electrical power and energy systems, vol. 33,2011, pp. 8–16.
[5] R.R. Shoults and J.A. Jativa Ibarra, “Multi area adaptive LFC developed for a comprehensive AGC simulation”, IEEE Transaction Power System, vol. 8 (2), 1993, pp 541–547.
[6] D. K. Chaturvedi, P. S. Satsangi, and P. K. Kalra, “Load frequency control: A generalized neural network approach”, Electric power energy system, vol. 21, 6, Aug. 1999. pp. 405–415.
[7] S.P. Ghoshal, “Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control”, Electric power systems research, vol. 72, 2004, pp. 203–212
[8] T.P.I Ahamed, P.S.N Rao and P.S Sastry, “A reinforcement learning approach to automatic generation control”, Electric Power System Research, vol. 63, 2002, pp. 9–26.
[9] S. R. Khuntia, and S. Panda, “Simulation study for automatic generation control of a multi-area power system by ANFIS approach”, Applied Soft Computing, vol.12, 2012, pp. 333–341.
[10] J Nanda and A. Mangla, “Some new findings on automatic generation control of an Interconnected Hydrothermal system with conventional controllers”, IEEE transaction on energy conversion Vol. 21(1), March 2006, pp187-194.
[11] J. Nanda, S. Mishra and L. C. Saikia, “Maiden Application of Bacteria foraging-based optimization technique in Multi-area Automatic generation control”, IEEE Transactions on power systems, Vol.24, (2), May 2009, pp.602-609.
[12] U.K. Rout, R.K Sahu and S. Panda, “Design and analysis of differential evolution algorithm based automatic generation control for interconnected power system”, Ain Shams Engg Journal, Vol.4(3), 2013, pp. 409-421.
[13] B. Anand and A. E Jayekumar, “Fuzzy logic based load frequency control of hydro-thermal system with nonlinearities”, International journal of Electrical and Power Engineering, vol.3 (2), 2009, pp 112-118.
[14] “PSO Tutorial”, available at: http://www.swarmintelligence.org/tutorials.php
[15] K.M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control”, IEEE Control Systems Magazine. Vol.22, 2002, pp. 52–67.
[16] S. Mishra, “A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation”, IEEE Transaction evolution Comp. vol.9, 2005, pp. 61–73.
[17] R. Stron and K. Price, “Differential evolution – A simple and efficient adaptive scheme for global optimization over continuous spaces”, Journal of Global Optimization, vol.11, 1995, pp.341-359.
[18] S. Das and P.N. Suganthan, “Differential Evolution: A Survey of the State-of-the-Art”, IEEE transaction Evolution Compt., vol.15, 2011, pp.4-31.