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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 644266260
doi: 10.4316/AECE


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  2/2015 - 4

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: 356 | Views: 1,107

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

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[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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[CrossRef] [Web of Science Times Cited 50] [SCOPUS Times Cited 88]


[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 86] [SCOPUS Times Cited 113]


[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.

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[CrossRef] [Web of Science Times Cited 69] [SCOPUS Times Cited 117]


[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 38]


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[CrossRef]


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[CrossRef]




References Weight

Web of Science® Citations for all references: 1,607 TCR
SCOPUS® Citations for all references: 3,182 TCR

Web of Science® Average Citations per reference: 57 ACR
SCOPUS® Average Citations per reference: 114 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 2016-12-09 19:07 in 136 seconds.




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