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JCR Impact Factor: 0.459
JCR 5-Year IF: 0.442
Issues per year: 4
Current issue: Feb 2017
Next issue: May 2017
Avg review time: 79 days


PUBLISHER

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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Broken Bar Fault Detection in IM Operating Under No-Load Condition, RELJIC, D., JERKAN, D., MARCETIC, D., OROS, D.
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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 "Big Data - " before the paper title in OpenConf.

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2016-Jun-14
Thomson Reuters published the Journal Citations Report for 2015. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.459, and the JCR 5-Year Impact Factor is 0.442.

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  4/2014 - 3

Implementation of Genetic Algorithm in Control Structure of Induction Motor A.C. Drive

BRANDSTETTER, P. See more information about BRANDSTETTER, P. on SCOPUS See more information about BRANDSTETTER, P. on IEEExplore See more information about BRANDSTETTER, P. on Web of Science, DOBROVSKY, M. See more information about  DOBROVSKY, M. on SCOPUS See more information about  DOBROVSKY, M. on SCOPUS See more information about DOBROVSKY, M. on Web of Science, KUCHAR, M. See more information about KUCHAR, M. on SCOPUS See more information about KUCHAR, M. on SCOPUS See more information about KUCHAR, M. 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 (891 KB) | Citation | Downloads: 525 | Views: 1,269

Author keywords
Artificial intelligence, genetic algorithms, induction motor, variable speed drive, vector control

References keywords
genetic(9), motor(8), drive(7), control(7), algorithm(7), systems(6), speed(5), intelligent(5), induction(5), controllers(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-11-30
Volume 14, Issue 4, Year 2014, On page(s): 15 - 20
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.04003
Web of Science Accession Number: 000348772500003
SCOPUS ID: 84921640821

Abstract
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Full text preview
Modern concepts of control systems with digital signal processors allow the implementation of time-consuming control algorithms in real-time, for example soft computing methods. The paper deals with the design and technical implementation of a genetic algorithm for setting proportional and integral gain of the speed controller of the A.C. drive with the vector-controlled induction motor. Important simulations and experimental measurements have been realized that confirm the correctness of the proposed speed controller tuned by the genetic algorithm and the quality speed response of the A.C. drive with changing parameters and disturbance variables, such as changes in load torque.


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

[1] P. Vas, Artificial-intelligence-based electrical machines and drives. Oxford science publication, 1999.

[2] P. Fedor, D. Perdukova, "Energy optimization of a dynamic system controller," in Proc. International Joint Conference CISIS'12-ICEUTE'12-SOCO'12 Special Sessions, Book Series: Advances in Intelligent Systems and Computing, 2013, vol. 189, pp. 361-369.

[3] T. Orlowska-Kowalska, M. Kaminski, "FPGA implementation of the multilayer neural network for the speed estimation of the two-mass drive system," IEEE Transactions on Industrial Informatics, vol. 7, no. 3, pp. 436-445, 2011.
[CrossRef] [Web of Science Times Cited 57] [SCOPUS Times Cited 82]


[4] S. Maiti, V. Verma, C. Chakraborty, Y. Hori, "An adaptive speed sensorless induction motor drive with artificial neural network for stability enhancement," IEEE Transactions on Industrial Informatics, vol. 8, no. 4, pp.757-766, 2012.
[CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 45]


[5] A. Saghafinia, H. W. Ping, M. A. Rahman, "High performance induction motor drive using hybrid fuzzy-PI and PI controllers: A review," International Review of Electrical Engineering - IREE, vol. 5, no. 5, pp. 2000-2012, 2010.

[6] P. Girovsky, J. Timko, J. Zilkova, "Shaft sensor-less FOC control of an induction motor using neural estimators," Acta Polytechnica Hungarica, vol. 9, no. 4, pp. 31-45, 2012.

[7] K. M. V. Chandrakala, S. Balamurugan, K. Sankaranarayanan, "Genetic algorithm tuned optimal variable structure system controller for enhanced load frequency control," International Review of Electrical Engineering - IREE, vol. 7, no. 2, pp. 4105-4112, 2012.

[8] N. Ozturk, "Speed control for dc motor drive based on fuzzy and genetic PI controller - A comparative study," Elektronika Ir Elektrotechnika, no. 7, pp. 43-48, 2012.

[9] M. Abachizadeh, M. R. H. Yazdi, A. Yousefi-Koma, "Optimal tuning of PID controllers using artificial bee colony algorithm," in Proc. International Conference on Advanced Intelligent Mechatronics, Montreal, Canada, 2010, pp. 379-384.

[10] A. Rajasekhar, A. Abraham, R. K. Jatoth RK, "Controller tuning using a Cauchy mutated artificial bee colony algorithm," Advances in Intelligent and Soft Computing, Springer Verlag Berlin, vol. 87, pp. 11-18, 2011.

[11] D. E. Goldberg, Genetic algorithms in search, optimization and machine learning. Boston: Addison-Wesley Publishing Comp., 1989.

[12] M. Viteckova, A. Vitecek, Selected methods of adjusting controllers. VSB-Technical University of Ostrava, 2011.

[13] P. Brandstetter, T. Krecek, "Speed and current control of permanent magnet synchronous motor drive using IMC controllers," Advances in Electrical and Computer Engineering, 2012, vol. 12, no. 4, pp. 3-10.
[CrossRef] [Full Text] [Web of Science Times Cited 24] [SCOPUS Times Cited 28]


[14] H. Ben Jmaa Derbel, "Design of PID controllers for time-delay systems by the pole compensation technique," in Proceedings of the 6th International Multi-Conference on Systems, Signals and Devices, 2009, pp. 1-6.

[15] A. Rezazadeh, "Genetic algorithm based servo system parameter estimation during transients," Advances in Electrical and Computer Engineering, vol. 10, no. 2, pp. 77-81, 2010.
[CrossRef] [Full Text] [Web of Science Times Cited 4] [SCOPUS Times Cited 6]


[16] P. Palacky, P. Hudecek, A. Havel, "Real-time estimation of induction motor parameters based on the genetic algorithm," in Proc. International Joint Conference CISIS'12-ICEUTE'12-SOCO'12 Special Sessions, Book Series: Advances in Intelligent Systems and Computing, 2013, vol. 189, pp. 401-409.

[17] M. Mitchell, An Introduction to Genetic Algorithms. Fifth printing, A Bradford Book The MIT Press, Cambridge, Massachusetts; London, England, 1999.

[18] K. F. Man, K. S. Tang, S. Kwong, W. A. Halang, Genetic Algorithms for Control and Signal Processing, series Advances in Industrial Control, Springer Verlag, 211 p., 2011.

[19] C. Elmas, T. Yigit, "Genetic algorithm based on-line tuning of a PI controller for a switched reluctance motor drive," Electric Power Components and Systems, vol.35, no.6, pp. 675-691, 2007.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 19]


[20] P. Brandstetter, M. Dobrovsky, "Speed Control of A. C. Drive with Induction Motor Using Genetic Algorithm," in Proc. International Joint Conference CISIS'12-ICEUTE'12-SOCO'12 Special Sessions, Book Series: Advances in Intelligent Systems and Computing, 2013, vol. 189, pp. 341-350.



References Weight

Web of Science® Citations for all references: 134 TCR
SCOPUS® Citations for all references: 180 TCR

Web of Science® Average Citations per reference: 6 ACR
SCOPUS® Average Citations per reference: 9 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 2017-04-21 20:19 in 36 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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