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

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Improved Wind Speed Prediction Using Empirical Mode Decomposition, ZHANG, Y., ZHANG, C., SUN, J., GUO, J.
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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
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Download PDF pdficon (1,416 KB) | Citation | Downloads: 313 | Views: 1,426

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), heating(5), switch(4)
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

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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

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[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.

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[CrossRef] [SCOPUS Times Cited 1]

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[CrossRef] [SCOPUS Times Cited 6]

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[CrossRef] [SCOPUS Times Cited 1]

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[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 14]

[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 3] [SCOPUS Times Cited 9]

[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.
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[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.
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[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.
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[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.
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[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 1073] [SCOPUS Times Cited 1403]

References Weight

Web of Science® Citations for all references: 1,310 TCR
SCOPUS® Citations for all references: 1,716 TCR

Web of Science® Average Citations per reference: 52 ACR
SCOPUS® Average Citations per reference: 69 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 2021-03-31 02:40 in 131 seconds.

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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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