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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: 643243560
doi: 10.4316/AECE


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Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance, ZHANG, Y., WANG, P., CHENG, P., LEI, S.
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2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
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2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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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
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,083 KB) | Citation | Downloads: 431 | Views: 1,494

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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[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 104] [SCOPUS Times Cited 139]


[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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References Weight

Web of Science® Citations for all references: 2,253 TCR
SCOPUS® Citations for all references: 4,146 TCR

Web of Science® Average Citations per reference: 80 ACR
SCOPUS® Average Citations per reference: 148 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 2017-09-20 19:05 in 216 seconds.




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