Dergiler / Uludağ Üniversitesi Mühendislik Fakültesi Dergisi / 2019 / Cilt: 24 - Sayı: 1

BUCK, BOOST AND BUCK-BOOST CONVERTER DESIGNS WITH VARIOUS METAHEURISTIC METHODS

Çeşitli Metasezgisel Yöntemlerle Alçaltan, Yükselten ve Alçaltan-Yükselten Dönüştürücü Tasarımları

Sayfa
383–390
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:One of the basic circuit structures in the field of power electronics is DC-DC converters.
📊
Temel Bulgular & Veri: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, cuckoo search, harmony search, lightning search and gray wolf optimizer).
💡
Sonuç & Katkı: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.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Özet

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, cuckoo 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.

Abstract

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.

🔖

Bu Makaleye Atıf Yap

VATANSEVER, F., KUYU, Y. Ç. (2019). BUCK, BOOST AND BUCK-BOOST CONVERTER DESIGNS WITH VARIOUS METAHEURISTIC METHODS. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, Cilt: 24 - Sayı: 1, 383–390. https://ajindex.com/articles/329799

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (12)

  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
  4. Gürdal, O. (2008) Güç Elektroniği, Seçkin Yayıncılık, Ankara
  5. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization, Technical report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, Kayseri
  6. 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
  7. 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
  8. MATLAB, The MathWorks, Inc.,
  9. 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
  10. Rashid, M.H. (2013) Power Electronics: Circuits, Devices & Applications, 4th ed., Pearson Education
  11. 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
  12. Storn, R. and Price, K. (1995). Differential evolution-a simple and efficient adaptive sc