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

Date

2023-01-01

Author

Alemdar, Ozturk Sahin
Oner, Mustafa Umit
Altun, Ogün
Keysan, Ozan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Artificial neural networks, artificial neural networks, asymmetric, Capacitors, DC-DC power converters, Harmonic analysis, interleaving, multiphase converters, Optimized production technology, Real-time systems, ripple minimization, Switches

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85179784092&origin=inward
https://hdl.handle.net/11511/108142

Collections

Department of Electrical and Electronics Engineering, Article