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Article Number - 78941BB8400


Vol.5(3), pp. 44 - 49 , September 2013
https://doi.org/10.5897/JEEER12.107
ISSN: 1993-8225


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Full Length Research Paper

Effect of generation rate constraint on load frequency control of multi area interconnected thermal systems



Naresh Kumari
  • Naresh Kumari
  • Department of Electrical, Electronics and Communication Engineering, ITM University, Gurgaon (Haryana), India
  • Google Scholar
A. N. Jha
  • A. N. Jha
  • Department of Electrical, Electronics and Communication Engineering, ITM University, Gurgaon (Haryana), India
  • Google Scholar







 Accepted: 29 August 2013  Published: 15 September 2013

Copyright © 2013 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0


This paper deals with automatic generation control (AGC) of two unequal interconnected thermal areas considering the reheat turbines for thermal areas and appropriate Generation Rate Constraint (GRC). The response with GRC is compared with the analysis done without the Generation Rate Constraint. Although the frequency deviation is less with suitable controllers when the GRC is not considered, it is not the actual frequency deviation. When GRC is considered the actual frequency deviation can be found and then accordingly the controller is tuned. Particle swarm optimization (PSO) technique is used to simultaneously optimize the integral gains (KI), speed regulation parameter (Ri) and frequency bias (Bi) parameter. Most of the literature for AGC used classical approach based on integral squared error (ISE) technique, etc. for optimal selection of controller parameters. This is a trial and error method; extremely time consuming when several parameters have to be optimized simultaneously. The computational intelligence based technique like PSO is a more efficient and fast technique for optimization of different gains in load frequency control. MATLAB/SIMULINK is used as a simulation tool.

 

Key words: Area control error, automatic generation control, particle swarm optimization, generation rate constraint.

Elgerd OI (1983). Electric energy systems theory: An introduction. 2nd ed., New Delhi: Tata McGraw-Hill.
 
Haddin M, Soebagio, Soeprijanto A, Purnomo M (2011). Gain Coordination of AVR-PSS and AGC based on Particle Swarm Optimization to improve the dynamic stability of the power system. Int. J. Acad. Res. 3(3):462.
 
Hari L, Kothari ML, Nanda J (1991). Optimum selection of speed regulation parameters for automatic generation control in discrete mode considering generation rate constraints. IEE Proc. 138(5):401–406.
 
Kundur P (1994). Power system stability and control. Tata McGraw-Hill, New Delhi, India, pp. 410-478.
 
Liu X, Zhan X, Qian D (2010). Load Frequency Control Considering Generation Rate Constraints. Proceedings of the 8th World Congress on Intelligent Control and Automation.
 
Mallesham G, Mishra S, Jha AN (2012). Automatic Generation Control of Microgrid using Artificial Intelligence Techniques. IEEE Power and Energy Society General Meeting, 2012.
http://dx.doi.org/10.1109/PESGM.2012.6345404
 
Naik S, ChandraSekhar K, Vaisakh K (2005). Adaptive PSO based optimal fuzzy controller design for AGC equipped with SMES. J. Theor. Appl. Inf. Technol. 7(1):008-017.
 
Nanda J, Mangla A,Suri S (2006). Some new findings on automatic generation control of an interconnected hydrothermal system with conventional controllers. IEEE Trans. Energy Convers. 21(1):187–194. http://dx.doi.org/10.1109/TEC.2005.853757
 
Nanda J, Mishra S, Saikia LC (2009). Maiden Application of Bacterial Foraging-Based Optimization Technique in Multiarea Automatic Generation Control. IEEE Trans. Power Syst. 24(2).
http://dx.doi.org/10.1109/TPWRS.2009.2016588
 
Panda G, Panda S, Ardil C (2009). Automatic Generation Control of Interconnected Power System with Generation Rate Constraints by Hybrid Neuro Fuzzy Approach. World Academy of Science, Engineering and Technology, p. 52.
 
Patel RN, Sinha SK, Prasad R (2008). Design of a Robust Controller for AGC with Combined Intelligence Techniques. World Academy of Science, Engineering and Technology 21:687-693.
 
Saadat H (1999). Power System Analysis. McGraw-Hill.
 
Saikia LC, Mishra S, Sinha N, Nanda J (2008). Automatic generation control of a multi area hydrothermal system using reinforced learning neural network controller. Electr. Power Energy Syst. 33:1101-1108. http://dx.doi.org/10.1016/j.ijepes.2011.01.029
 
Saikia LC, Nanda J, Mishra S (2010). Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system. Electric. Power Energy Syst. AUPEC. 22:1-6.
 
Tyagi B, Srivastava SC (2006). A Decentralized Automatic Generation Control Scheme for Competitive Electricity Markets. IEEE Trans. Power Syst. 21(1):312-320.
http://dx.doi.org/10.1109/TPWRS.2005.860928
 
Venayagamoorthy GK, Harley RG (2002). Two Separate Continually Online-Trained Neuro-controllers for Excitation and Turbine Control of a Turbo generator. IEEE Trans. Ind. Appl. 38(3):887-893. http://dx.doi.org/10.1109/TIA.2002.1003445

 


APA (2013). Effect of generation rate constraint on load frequency control of multi area interconnected thermal systems. Journal of Electrical and Electronics Engineering Research, 5(3), 44 - 49.
Chicago Naresh Kumari and A. N. Jha. "Effect of generation rate constraint on load frequency control of multi area interconnected thermal systems." Journal of Electrical and Electronics Engineering Research 5, no. 3 (2013): 44 - 49.
MLA Naresh Kumari and A. N. Jha. "Effect of generation rate constraint on load frequency control of multi area interconnected thermal systems." Journal of Electrical and Electronics Engineering Research 5.3 (2013): 44 - 49.
   
DOI https://doi.org/10.5897/JEEER12.107
URL http://academicjournals.org/journal/JEEER/article-abstract/78941BB8400

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