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Load Frequency Control of Nonlinear Interconnected Hydro-Thermal System Using Differential Evolution Technique

Authors: Banaja Mohanty, Prakash Kumar Hota


This paper presents a differential evolution algorithm to design a robust PI and PID controllers for Load Frequency Control (LFC) of nonlinear interconnected power systems considering the boiler dynamics, Governor Dead Band (GDB), Generation Rate Constraint (GRC). Differential evolution algorithm is employed to search for the optimal controller parameters. The proposed method easily copes of with nonlinear constraints. Further the proposed controller is simple, effective and can ensure the desirable overall system performance. The superiority of the proposed approach has been shown by comparing the results with published fuzzy logic controller for the same power systems. The comparison is done using various performance measures like overshoot, settling time and standard error criteria of frequency and tie-line power deviation following a 1% step load perturbation in hydro area. It is noticed that, the dynamic performance of proposed controller is better than fuzzy logic controller. Furthermore, it is also seen that the proposed system is robust and is not affected by change in the system parameters.

Keywords: differential evolution (DE), Automatic Generation Control (AGC), Generation Rate Constraint (GRC), Governor Dead Band (GDB)

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[1] Kothari. M.L, Kaul. B. L, and Nanda J., Automatic generation control of hydrothermal system, Journal of Inst. of Engg. (India), 61(2), Oct. 1980, pp. 85–91.
[2] Nanda. J, Mangla. A, Suri. S, Some new findings on automatic generation control of an Interconnected Hydrothermal system with conventional controllers, IEEE Trans. on Energy Conv., 21, March 2006, pp.187-194.
[3] Nanda. J, Mishra. S, Saikia. L. C, Maiden application of bacterial foraging based optimization technique in multi area automatic generation control, IEEE Trans. on Power Systems, 24(2), 2009, pp. 602-609.
[4] Gozde. H., Taplamacioglu. M.C, Automatic generation control application with craziness based particle swarm optimization in a thermal power system, Electric Power and Energy System, 33, 2011, pp. 8-16.
[5] Shoults. R.R , Jativa Ibarra. J.A., Multi area adaptive LFC developed for a comprehensive AGC simulation, IEEE Trans. Power Syst. 8 (2), 1993, pp 541–547.
[6] Chaturvedi. D.K, Satsangi. P.S., Kalra. P.K, Load frequency control: a generalized neural network approach, Elect. Power Energy Syst. 21 (6), 1999, pp 405–415.
[7] Ghosal. S.P, Optimization of PID gains by particle swarm optimization in fuzzy based automatic generation control. Electr Power Syst Res 72, 2004, pp. 203–212
[8] Ahamed. T.P.I, Rao P.S.N, Sastry P.S, A reinforcement learning approach to automatic generation control. Electr Power Syst Res 63, 2002, pp. 9–26
[9] Khuntia. S. R, Panda. S, Simulation study for automatic generation control of a multi-area power system by ANFIS approach. Applied Soft Computing 12, 2012, pp. 333-341
[10] Saikia L.C, Nanda. J and Mishra. S, Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system, Int. J. Elect. Power & Energy Systs., 33, 2011, pp 394-401.
[11] Ali. E. S., Abd-Elazim. S.M., Bacteria foraging optimization algorithm based load frequency controller for interconnected power system, Elect. Power and Energy Syst., 33, 2011, 633–638.
[12] Rout U.K., Sahu R.K and Panda. S., Design and analysis of differential evolution algorithm based automatic generation control for interconnected power system, Ain Shams Eng J 2012.
[13] Anand B. and Jayekumar. A. E, Fuzzy logic based load frequency control of hydro-thermal system with non-linearities, International journal of Electrical and Power Engg. , 3(2), 2009, pp 112-118.
[14] Stron R., Price K., Differential evolution – A simple and efficient adaptive scheme for global optimization over continuous spaces, Journal of Global Optimization, 11, 1995 341-359.
[15] Das S. , and Suganthan P.N., Differential Evolution: A Survey of the State-of-the-Art, IEEE trans. Evol. Compt., 15, 2011, pp.4-31.
[16] Panda S., Differential evolution algorithm for SSSC-based damping controller design considering time delay, J. Franklin Institute, 348 (8), 2011, pp.1903-1926.
[17] Panda S., Robust coordinated design of multiple and multi-type damping controller using differential evolution algorithm, Elect. Power and Energy Syst., 33, 2011, pp. 1018-1030.