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Stefan cel Mare
University of Suceava
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ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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 HIGH-IMPACT PAPER 

Solution Approach to Automatic Generation Control Problem Using Hybridized Gravitational Search Algorithm Optimized PID and FOPID Controllers

DAHIYA, P. See more information about DAHIYA, P. on SCOPUS See more information about DAHIYA, P. on IEEExplore See more information about DAHIYA, P. on Web of Science, SHARMA, V. See more information about  SHARMA, V. on SCOPUS See more information about  SHARMA, V. on SCOPUS See more information about SHARMA, V. on Web of Science, NARESH, R. See more information about NARESH, R. on SCOPUS See more information about NARESH, R. on SCOPUS See more information about NARESH, R. on Web of Science
 
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Download PDF pdficon (1,083 KB) | Citation | Downloads: 903 | Views: 3,699

Author keywords
automatic generation control, disruption operator, fractional calculus, gravitational search algorithm, opposition based learning

References keywords
power(21), control(17), load(9), frequency(9), algorithm(9), generation(8), systems(7), automatic(7), system(6), search(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-05-31
Volume 15, Issue 2, Year 2015, On page(s): 23 - 34
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.02004
Web of Science Accession Number: 000356808900004
SCOPUS ID: 84979725893

Abstract
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This paper presents the application of hybrid opposition based disruption operator in gravitational search algorithm (DOGSA) to solve automatic generation control (AGC) problem of four area hydro-thermal-gas interconnected power system. The proposed DOGSA approach combines the advantages of opposition based learning which enhances the speed of convergence and disruption operator which has the ability to further explore and exploit the search space of standard gravitational search algorithm (GSA). The addition of these two concepts to GSA increases its flexibility for solving the complex optimization problems. This paper addresses the design and performance analysis of DOGSA based proportional integral derivative (PID) and fractional order proportional integral derivative (FOPID) controllers for automatic generation control problem. The proposed approaches are demonstrated by comparing the results with the standard GSA, opposition learning based GSA (OGSA) and disruption based GSA (DGSA). The sensitivity analysis is also carried out to study the robustness of DOGSA tuned controllers in order to accommodate variations in operating load conditions, tie-line synchronizing coefficient, time constants of governor and turbine. Further, the approaches are extended to a more realistic power system model by considering the physical constraints such as thermal turbine generation rate constraint, speed governor dead band and time delay.


References | Cited By  «-- Click to see who has cited this paper

[1] H. Saadat, Power system analysis, 2nd ed. New Delhi, India: TMH, pp. 551, 2002.

[2] Ibraheem, P. Kumar, D.P. Kothari, "Recent philosophies of automatic generation control strategies in power systems," IEEE T Power Syst, Vol. 20, No. 1, pp. 346-57, Feb. 2005.
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[3] H. Shayeghi, H.A. Shayanfar, A. Jalili, "Load frequency control strategies: A state-of-the-art survey for the researcher," Energ Convers Manage, Vol. 50, No. 2, pp. 344-353, Feb. 2009.
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[4] S.K. Pandey, R.M. Soumya, N. Kishor, "A literature survey on load-frequency control for conventional and distribution generation power systems," Renew Sust Energ Rev, Vol. 25, pp. 318-334, Sep. 2013.
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[5] S.J.P.S. Mariano, J.A.N. Pombo, M.R.A. Calado, L.A.F.M. Ferreira, "A procedure to specify the weighting matrices for an optimal load-frequency controller," Turk J Electr Eng Co, Vol. 20, No. 3, pp. 367-379, 2012.
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[6] N. Hasan, Ibraheem, P. Kumar, N. Nizamuddin, "Sub-optimal automatic generation control of interconnected power system using constrained feedback control strategy," Int J Elec Power, Vol. 43, No. 1, pp. 295-303, Dec. 2012.
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[9] E. Cam and I. Kocaarslan, "Load frequency control in two area power systems using fuzzy logic controller," Energ Convers Manage, Vol. 46, No. 2, pp. 233-243, Jan. 2005.
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[10] M.I. Alomoush, "Load frequency control and automatic generation control using fractional order controllers," Electr Eng, Vol. 91, No. 7, pp. 357-368, Jan. 2010.
[CrossRef] [Web of Science Times Cited 107] [SCOPUS Times Cited 99]


[11] H. Shayeghi, A. Jalili, H.A. Shayanfar, "Multi-stage fuzzy load frequency control using PSO," Energ Convers Manage, Vol. 49, No. 10, pp. 2570-2580, Oct. 2008.
[CrossRef] [Web of Science Times Cited 102] [SCOPUS Times Cited 149]


[12] F. Valdez, P. Melin, O. Castillo, "An improved evolutionary method with fuzzy logic for combining particle swarm optimization and genetic algorithms," Appl Soft Comput, Vol. 11, No. 2, pp. 2625-2632, Mar. 2011.
[CrossRef] [Web of Science Times Cited 152] [SCOPUS Times Cited 194]


[13] N. A. El-Hefnawy, "Solving bi-level problems using modified particle swarm optimization algorithm," Int J Artificial Intelligence, Vol. 12, No. 2, pp. 88-101, Oct. 2014.

[14] E.S. Ali, S.M. Abd-Elazim, "Bacteria foraging optimization algorithm based load frequency control for interconnected power system," Int J Elec Power, Vol. 33, No. 3, pp. 633-638, Mar. 2011.
[CrossRef] [Web of Science Times Cited 275] [SCOPUS Times Cited 374]


[15] U.K. Rout, R.K. Sahu, S. Panda, "Design and analysis of differential evolution algorithm based automatic generation control for interconnected power system," Ain Shams Eng J, Vol. 4, No. 3, pp. 409-421, Sep. 2013.
[CrossRef] [SCOPUS Times Cited 216]


[16] H. Shabani, B. Vahidi, M. Ebrahimpour, "A robust PID controller based on imperialist competitive algorithm for load-frequency control of power systems," ISA T, Vol. 52, No. 1, pp. 88-95, Jan. 2013.
[CrossRef] [Web of Science Times Cited 204] [SCOPUS Times Cited 256]


[17] S. Debbarma, L.C. Saikia, N. Sinha, "Automatic generation control using two degree of freedom fractional order PID controller," Int J Elec Power, Vol. 58, pp. 120-129, Jun. 2014.
[CrossRef] [Web of Science Times Cited 137] [SCOPUS Times Cited 175]


[18] E. Rashedi, H. Nezamabadi-pour, J.S. Saryazdi, "GSA: a gravitational search algorithm," Inform Sciences, Vol. 179, No. 13, pp. 2232-2248, Jun. 2009.
[CrossRef] [Web of Science Times Cited 3879] [SCOPUS Times Cited 4726]


[19] R.K. Sahu, S. Panda, S. Padhan, "Optimal gravitational search algorithm for automatic generation control of interconnected power systems," Ain Shams Eng J, Vol. 5, No. 3, pp. 721-733, Sep. 2014.
[CrossRef] [SCOPUS Times Cited 143]


[20] R. E. Precup, R. C. David, E. M. Petriu, S. Preitl, A. S. Paul, "Gravitational search algorithm-based tuning of fuzzy control systems with a reduced parametric sensitivity," Series: Soft Computing in Industrial Applications, Advances in Intelligent and Soft Computing, Vol. 96, pp. 141-150, 2011.
[CrossRef] [SCOPUS Times Cited 75]


[21] S. Rahnamayan, H.R. Tizhoosh, M.M.A. Salama, "A novel population initialization method for accelerating evolutionary algorithms," Comput Math Appl, Vol. 53, No. 10, pp. 1605-1614, May 2007.
[CrossRef] [Web of Science Times Cited 189] [SCOPUS Times Cited 247]


[22] B. Shaw, V. Mukherjee, S.P. Ghoshal, "A novel opposition-based gravitational search algorithm for combined economic and emission dispatch problems of power systems," Int J Elec Power, Vol. 35, No. 1, pp. 21-33, Feb. 2012.
[CrossRef] [Web of Science Times Cited 180] [SCOPUS Times Cited 208]


[23] S. Sarafrazi, H. Nezamabadipour, S. Saryazdi, "Disruption: a new operator in gravitational search algorithm," Sci Iran, Vol. 18, No. 3, pp. 539-548, Jun. 2011.
[CrossRef] [Web of Science Times Cited 126] [SCOPUS Times Cited 155]


[24] A.W. Berger, F.C. Schweppe, "Real time pricing to assist in load frequency control," IEEE T Power Syst, Vol. 4, No. 3, pp. 920-926, Aug. 1989.
[CrossRef] [Web of Science Times Cited 80] [SCOPUS Times Cited 99]


[25] I.C. Report, "Dynamic model for steam and hydro turbines in power system studies," IEEE T Power Ap Syst, Vol. PAS-92, No. 6, pp. 1904-1915, Nov. 1973.
[CrossRef] [SCOPUS Times Cited 737]


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[27] W.I. Rowen, "Simplified mathematical representation of heavy duty gas turbine," J Eng Gas Turb Power, Vol. 105, No. 4, pp. 865-870, Oct. 1983.
[CrossRef] [Web of Science Times Cited 371] [SCOPUS Times Cited 532]




References Weight

Web of Science® Citations for all references: 7,418 TCR
SCOPUS® Citations for all references: 10,547 TCR

Web of Science® Average Citations per reference: 265 ACR
SCOPUS® Average Citations per reference: 377 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-11-21 18:19 in 207 seconds.




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