Buck, Boost And Buck-Boost Converter Designs with Various Metaheuristic Methods
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
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
Ayrıntılar
Birincil Dil
İngilizce
Konular
Mühendislik
Bölüm
Araştırma Makalesi
Yazarlar
Yiğit Çağatay Kuyu
Bu kişi benim
0000-0002-7054-3102
Türkiye
Yayımlanma Tarihi
30 Nisan 2019
Gönderilme Tarihi
2 Ocak 2019
Kabul Tarihi
3 Nisan 2019
Yayımlandığı Sayı
Yıl 2019 Cilt: 24 Sayı: 1
Cited By
GOZDE: A novel metaheuristic algorithm for global optimization
Future Generation Computer Systems
https://doi.org/10.1016/j.future.2022.05.022