Application of Modified Dynamic Neural Network for the Load Frequency Control of a Two Area Thermal Reheat Power System

(*) Corresponding author

Authors' affiliations

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


This paper presents the use of artificial intelligent technique to study the load frequency control of a two area reheat thermal interconnected power system. In this proposed scheme, a control methodology is developed using Modified Dynamic Neural Network (MDNN) for the design of integral controller. The control strategies found to be guarantee such that the steady state error of frequencies and inadvertent interchange of tie-line power are maintained in a given tolerance limitations.
A comparison of the output response of the system with the Proportional-Integral (PI) controller and MDNN controller based approaches shows the superiority of proposed MDNN based approach over PI controller for different loading conditions (1%and 5% step load variations). From the simulation results a comparative performance study in terms of the settling time and peak over shoot of the output response of the system has been tabulated.

Copyright © 2013 Praise Worthy Prize - All rights reserved.


Load Frequency Control (LFC); Integral Controller; Modified Dynamic Neural Network (MDNN) Controller; Area Control Error (ACE)

Full Text:



Shayeghi H., Shayanfar H. A, Jalili A., Load frequency control strategies: A state of the art survey for the researcher, Energy Conservation and Management, 2009; 50(2); 344-353.

Ibraheem.I, Kumar.P, Kothari.D.P,”Resent philosophies of automatic generation control strategies in power systems”, IEEE Transaction on Power Systems, 2005; 20(1):346–357.

Malik OP, Kumar A, Hope GS. “A load frequency control algorithm based on a generalized approach”, IEEE Transaction on Power System, 1988; 3(2):375–82.

Velusami.S, Chidambaram.I.A,”Decentralized biased dual mode controller for LFC of interconnected power systems considering GDB and GRC nonlinearities”, Energy conversion & Management, 2007; 48(1):1691-1702.

Mehdi Peyvandi, Application of evolutionari algorithms for SSSC-based controller optimization to improve power system stability, (2011) International Review on Modelling and Simulations (IREMOS), 4 (1), pp. 146–156.

B. Parmasivam, I. A. Chidambaram, Design of a Load- Frequency Controller using Craziness based PSO for an Interconnected Power System with SSSC and RFB, (2012) International Review of Automatic Control (IREACO), 5 (2), pp. 102-112.

Pan CT, Liaw CM.” An adaptive controller for power system load frequency control”, IEEE Transaction on Power Systems, 2001; 4(1):122–128.

Wang, Zhou, Wen. “Robust-load frequency controller design for power systems”. IEE Proceedings of Control, 1993; 140(1):11–16.

Draeger.A, Engel. S.H, Ranke.H, “Model predictive control using neural networks”. IEEE Control System Management, 1995; 15:61–6.

P.D. Wasserman, Neural Computing: Theory and Practice, Van Nostrand, New York, 1989.

M. M. Gupta and D.H. Rae,” Dynamic neural units with applications to the control of unknown nonlinear systems”, 7th Journal of intelligent and Fuzzy Systems, Vol.1, No.1, pp. 73-92, 1993.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2023 Praise Worthy Prize