Ripple Minimization in Asymmetric Interleaved DC-DC Converters Using Neural Networks

Alemdar O. S., Oner M. U., ALTUN O., KEYSAN O.

IEEE Transactions on Power Electronics, cilt.39, sa.3, ss.3411-3421, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 39 Sayı: 3
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1109/tpel.2023.3339699
  • Dergi Adı: IEEE Transactions on Power Electronics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.3411-3421
  • Anahtar Kelimeler: Artificial neural networks (ANNs), asymmetric, interleaving, multiphase converters, ripple minimization
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Interleaving can be employed to reduce ripples in multiphase DC-DC converters although phases are operated under asymmetric conditions such as different input voltages or loads. To allow ripple minimization under asymmetric conditions, phase shifts between the switch timings of phases have to be appropriately adjusted. This study presents a method based on artificial neural networks (ANN) that can provide the required phase shifts to minimize ripple under asymmetric conditions. To obtain a machine learning dataset, the set of optimal phaseshift angles minimizing the common output capacitor current ripple is analytically obtained for two asymmetric interleaved Boost converters. Then, a small-scale, computationally efficient ANN is developed using the dataset to predict the optimal phase shift according to real-time operating conditions. The proposed method is experimentally validated. The proposed method predicts the optimal phase-shift angle in 28.4μs and the prediction is updated every 100μs to achieve ripple minimization.