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JCR Impact Factor: 0.650
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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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2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

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  2/2018 - 12

Application of the Firefly Algorithm for Optimizing a Single-switch Class E ZVS Voltage-Source Inverter's Operating Point

KLEMPKA, R. See more information about KLEMPKA, R. on SCOPUS See more information about KLEMPKA, R. on IEEExplore See more information about KLEMPKA, R. on Web of Science, WARADZYN, Z. See more information about  WARADZYN, Z. on SCOPUS See more information about  WARADZYN, Z. on SCOPUS See more information about WARADZYN, Z. on Web of Science, SKALA, A. See more information about SKALA, A. on SCOPUS See more information about SKALA, A. on SCOPUS See more information about SKALA, A. on Web of Science
 
Click to see author's profile in 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 (1,416 KB) | Citation | Downloads: 208 | Views: 506

Author keywords
artificial intelligence, inverters, melt processing, optimization, power quality

References keywords
inverter(11), resonant(9), induction(9), single(8), electronics(6), quasi(5), power(5), optimization(5), omori(5), heating(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-05-31
Volume 18, Issue 2, Year 2018, On page(s): 93 - 100
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.02012
Web of Science Accession Number: 000434245000012
SCOPUS ID: 85047879787

Abstract
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Full text preview
A continuous development of technology involves a permanent improvement of appliances towards the increase in energy effectiveness, efficiency and precision of operation. This applies also to induction heating equipment. In order to increase the energy effectiveness of the inverter presented in this paper and limit its switching losses, its operating point has been optimized, which required solving a system of two non-linear equations. Due to the complex surface optimization to determine the optimal operating point, a modified firefly algorithm was used, which belongs to the family of intelligent optimization methods. An analysis of the effectiveness of optimization process was carried out, due to the firefly algorithm parameters. A modification of the firefly algorithm was proposed to speed up the optimization and get certainty of finding the global optimum. Theoretical outcomes were compared with the measured experimental results obtained in a real inverter system.


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

[1] H. Omori, H. Yamashita, M. Nakaoka, and T. Maruhashi, "A novel type induction-heating single-ended resonant inverter using new bipolar Darlington-Transistor," IEEE Power Electronics Specialist Conference, Toulouse, 1985, pp. 590-599.
[CrossRef]


[2] H. Yamashita, K. Asada, H. Omori, and H. Kominami, "An induction heating single ended push-pull resonant inverter using IGBT," in Proc. of the First International PCIM’88 Conf., Tokyo, 1988. pp. 82-90.

[3] I. Hirota, H. Omori, and M. Nakaoka, "Performance evaluations of single-ended quasi-load resonant inverter incorporating advanced-2nd generation IGBT for soft switching," Intern. Conf. on Industrial Electronics Control Instrumentation and Automation, San Diego, 1992, pp. 223-228.
[CrossRef] [Web of Science Times Cited 1]


[4] S. Wang, K. Izaki, I. Hirota, H. Yamashita, H. Omori, and M. Nakaoka, "Induction-heated cooking appliance using new quasi-resonant ZVS-PWM inverter with power factor correction," IEEE Trans. Ind. Appl., vol. 34, no. 4, pp. 705-712, Aug. 1998.
[CrossRef] [Web of Science Times Cited 48] [SCOPUS Times Cited 68]


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[CrossRef] [SCOPUS Times Cited 22]


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[CrossRef]


[7] M. Saoudi, D. Puyal, C. Bernal, D. Anton, and A. Mediano, "Induction cooking systems with single switch inverter using new driving techniques," IEEE Intern. Symposium on Industrial Electronics. Bari, 2010, pp. 878-883.
[CrossRef] [SCOPUS Times Cited 13]


[8] A. Chakraborty, P. K. Sadhu, K. Bhaumik, P. Pal, and N. Pal, "Behaviour of a high frequency parallel quasi resonant inverter fitted induction heater with different switching frequencies," International Journal of Electrical and Computer Engineering (IJECE), vol. 6, no. 2, pp. 447-457, 2016.
[CrossRef] [SCOPUS Times Cited 1]


[9] I.-O. Lee, and J.-Y. Lee, "A High-Power DC-DC Converter Topology for Battery Charging Applications," Energies, vol.10, no. 7, 871, 2017.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 4]


[10] Z. Waradzyn, A. Skala, B. Swiatek, R. Klempka, and R. Kieronski, "ZVS single-switch inverter for induction heating - optimum operation," Prz Elektrotechniczn, 2014, 90(2), pp. 32-35.
[CrossRef] [SCOPUS Times Cited 3]


[11] A. Skala, and Z. Waradzyn, "A single-switch class E voltage-source inverter for induction heating - influence of the parameters of the resonant circuit elements on its performance at optimal control," Prz Elektrotechniczn, vol. 1, no. 1, pp. 33-36, 2017.
[CrossRef] [SCOPUS Times Cited 1]


[12] A. Skala, and Z. Waradzyn, "Determination of efficiency in a single-switch class E ZVS-1S quasi-resonant inverter in application for induction heating," Prz Elektrotechniczn, vol. 1, no.3, pp. 99-102, 2016.
[CrossRef] [SCOPUS Times Cited 3]


[13] J. Kwiecien, and B. Filipowicz, "Comparison of firefly and cockroach algorithms in selected discrete and combinatorial problems," Bull Pol Ac: Tech, vol. 62, no. 4, pp. 797- 804, 2014.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 11]


[14] R. Klempka, and B. Filipowicz, "Comparison of using the Genetic Algorithm and Cuckoo Search for multi-criteria optimisation with limitation," Turk J Electr Eng Co, vol. 25, pp. 1300-1310, 2017.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 6]


[15] Y. Huang, C. Yang, and S. Gong, "Energy Optimization for Train Operation Based on an Improved Ant Colony Optimization Methodology," Energies, vol. 9, no. 8, 627, 2016.
[CrossRef] [Web of Science Times Cited 5]


[16] R. Klempka, "Design of C-type passive filter for arc furnaces," Metalurgija, vol. 56, no. 1-2, pp. 161-163, 2017.

[17] M. Huang, "Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting," Energies, vol. 9, no. 6, 426, 2016.
[CrossRef] [Web of Science Times Cited 16]


[18] S. Vrkalovic, T.-A. Teban and I.-D. Borlea, "Stable Takagi-Sugeno Fuzzy Control Designed by Optimization," International Journal of Artificial Intelligence, vol. 15, no. 2, pp. 17- 29, 2017.

[19] R.-E. Precup, M.-C. Sabau, and E. M. Petriu. "Nature-inspired optimal tuning of input membership functions of Takagi-Sugeno-Kang fuzzy models for Anti-lock Braking Systems," Appl. Soft Comput, vol. 27, pp. 575-589, 2015.
[CrossRef] [Web of Science Times Cited 59] [SCOPUS Times Cited 72]


[20] S. H. E. Abdel Aleem, A. F. Zobaa, and M. E. Balci, "Optimal resonance-free third-order high-pass filters based on minimization of the total cost of the filters using Crow Search Algorithm," Electr Pow Syst Res, vol 151, pp. 381-394, 2017.
[CrossRef]x [Web of Science Times Cited 17] [SCOPUS Times Cited 19]


[21] J. Saadat, P. Moallem and H. Koofigar, Training Echo Estate Neural Network Using Harmony Search Algorithm, International Journal of Artificial Intelligence, vol. 15, no. 1, pp. 163-179, 2017.

[22] M. A. Hosen, A. Khosravi, S. Nahavandi and D. Creighton, "Improving the Quality of Prediction Intervals Through Optimal Aggregation," IEEE Transactions on Industrial Electronics, vol. 62, no. 7, pp. 4420-4429, 2015.
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 23]


[23] X. S. Yang, "Nature-inspired metaheuristic algorithms," 2nd ed., Luniver Press, Bristol, UK, 2008. ISBN-13: 978-1905986286

[24] X. S. Yang, "Firefly algorithm, stochastic test functions and design optimization," Int J of Bio-Inspired Computation, vol. 2, no. 2, pp. 78-84, 2010.
[CrossRef] [Web of Science Times Cited 755] [SCOPUS Times Cited 1010]




References Weight

Web of Science® Citations for all references: 936 TCR
SCOPUS® Citations for all references: 1,256 TCR

Web of Science® Average Citations per reference: 37 ACR
SCOPUS® Average Citations per reference: 50 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 2019-07-16 10:54 in 138 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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Stefan cel Mare University of Suceava, Romania


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