Buck, Boost And Buck-Boost Converter Designs with Various Metaheuristic Methods

Yıl 2019, , 383 - 390, 30.04.2019

Fahri Vatansever , Yiğit Çağatay Kuyu

https://doi.org/10.17482/uumfd.506766

Cited By: 1

Öz

One of the basic circuit structures in the field of power electronics is
DC-DC converters. As these design steps require many mathematical operations,
these problems are hard to solve by hand. In addition, choosing the proper
component values is always curial when adopting the computer-based designs to
the real-world. In this study, the software is developed for the designs of
buck, boost and buck-boost DC-DC converters via metaheuristic algorithms that
calculate the parameters of  the circuits.
The components of the specified DC-DC converters are selected via the software with a user-friendly interface, under the desired criteria from the
industrial series (E12, E24 and E96), by using eight different metaheuristic
algorithms (artificial bee colony, differential evolution, genetic algorithm,
particle swarm optimization, cucko search, harmony search, lightning search and
gray wolf optimizer). The designs and analyses of DC-DC converters that are
chosen according to the type and features (determining/selecting the components
in accordance with the specified industrial series) can perform easily, fast
and effectively through the software developed for this purpose.

Anahtar Kelimeler

Power electronic, DC-DC converter, metaheuristic algorithms

ÇEŞİTLİ METASEZGİSEL YÖNTEMLERLE ALÇALTAN, YÜKSELTEN VE ALÇALTAN-YÜKSELTEN DÖNÜŞTÜRÜCÜ TASARIMLARI

Öz

Güç elektroniği alanındaki temel devre yapılarından birisi DA-DA (DC-DC)
dönüştürücüleridir. Farklı türleri olan bu devrelerin tasarım aşamalarında,
elle çözümü zor olan birçok matematiksel işlemler gerekmektedir. Ayrıca
bilgisayar tabanlı tasarımları; gerçek dünyaya uyarlarken, uygun bileşen
değerlerinin seçilmesi her zaman çok önemlidir. Gerçekleştirilen çalışmada; alçaltan,
yükselten ve alçaltan-yükselten DA-DA dönüştürücü devrelerin tasarımı için
metasezgisel algoritmalarla hesaplamaları gerçekleştiren yazılım
geliştirilmiştir. Kullanıcı dostu arayüze sahip yazılım ile seçilen türdeki
DA-DA dönüştürücü devre elemanları, istenilen endüstriyel serilere (E12, E24 ve
E96) uygun olarak sekiz farklı metasezgisel algoritma (yapay arı kolonisi, diferansiyel
gelişim, genetik algoritma, parçacık sürü optimizasyonu, guguk kuşu arama,
harmoni arama, yıldırım arama ve gri kurt optimizasyonu) ile kolay, hızlı ve
etkin bir şekilde elde edilmektedir.

Anahtar Kelimeler

Güç elektroniği, DC-DC dönüştürücü, metasezgisel algoritmala

Kaynakça

• 1. Dasgupta, D., Michalewicz, Z. (Eds.) (1997) Evolutionary Algorithms in Engineering Applications, Springer, Berlin.
• 2. Du, K. and Swamy, M. N. S. (2016) Search and Optimization by Metaheuristics, Birkhauser, Switzerland.
• 3. Geem, Z.W., Kim, J.H., Loganathan, G.V. (2001) A new heuristic optimization algorithm: harmony search, Simulation, 76 (2), 60-68. doi: 10.1177/003754970107600201
• 4. Goldberg, D. E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA, USA.
• 5. Gürdal, O. (2008) Güç Elektroniği, Seçkin Yayıncılık, Ankara.
• 6. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization, Technical report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, Kayseri.
• 7. Kennedy J. and Eberhart R. (1995) Particle swarm optimization, IEEE International Conference on Neural Networks, 1942-1948. doi:10.1109/ICNN.1995.488968
• 8. Kuyu, Y.C. and Vatansever, F. (2016) A new intelligent decision making system combining classical methods, evolutionary algorithms and statistical techniques for optimal digital FIR filter design and their performance evaluation, International Journal of Electronics and Communications, 70, 1651-1666. doi: 10.1016/j.aeue.2016.10.004
• 9. Kuyu, Y.C. and Vatansever, F. (2016) Optimization of the Rectifier Circuit Components with Evolutionary Algorithms, International Scientific Symposium "Electrical Power Engineering 2016", Varna/Bulgaria, 6-8. 100-103.
• 10. León-Aldaco, D., Estefany, S., Calleja, H., Jesús, A. A. (2015) Metaheuristic optimization methods applied to power converters: A review, IEEE Transactions on Power Electronics, 30 (12), 6791-6803. doi: 10.1109/TPEL.2015.2397311.
• 11. MATLAB, The MathWorks, Inc. , https://www.mathworks.com/
• 12. Mirjalili, S., Mirjalili, S.M., Lewis, A. (2014) Grey wolf optimizer, Advances in Engineering Software, 69, 46-61. doi: 10.1016/j.advengsoft.2013.12.007
• 13. Price, K.V., Storn, R.M., Lampinen, J.A. (2005) Differential Evolution: A Practical Approach to Global Optimization, Springer, Berlin.
• 14. Rashid, M.H. (2013) Power Electronics: Circuits, Devices & Applications, 4th ed., Pearson Education.
• 15. Shareef, H., Ibrahim, A.A., Mutlag, A.H. (2015) Lightning search algorithm, Applied Soft Computing, 36, 315-333. doi: 10.1016/j.asoc.2015.07.028
• 16. Simon, D. (2013) Evolutionary Optimization Algorithms, Wiley, New Jersey.
• 17. Storn, R. and Price, K. (1995). Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report-TR95, International Computer Science Institute.
• 18. Vasant, P. M. (2012) Meta-heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance, IGI Global, USA.
• 19. Vatansever, F., Yalcin, N.A., Kuyu, Y.C. (2015) Evrimsel Algoritmalarla Tristörlü Doğrultucu Devrelerindeki Tetikleme Açılarının Hesaplanması, Uludağ University Journal of The Faculty of Engineering, 20(2), 67-77. doi: 10.17482/uujfe.13975
• 20. Yang, X.-S. (2010) Engineering Optimization: An Introduction with Metaheuristic Applications, Wiley.
• 21. Yang, X.-S. (2014) Nature-Inspired Metaheuristic Algorithms, Elsevier.
• 22. Yang, X.S., Deb, S. (2009) Cuckoo search via lévy flights, World congress on nature & biologically inspired computing (NABIC), 210-214.