Salvato in:
| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2412.03626 |
| Tags: |
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Sommario:
- Current ripple minimization is one of the challenges in parallel converters to increase the capacitor lifetime in various applications. In this paper, a deep neural network-based phase-shifting (PS) technique is proposed for parallel-connected buck converters to minimize the amplitude of a selective harmonic component and facilitate a classic optimum PS at the same time. The proposed method identifies the global optimum point in real time, without the need for complicated computations. The common-link current, common-link voltage, and the duty ratios are selected as the inputs of the neural network to provide the proper phase shifts for the switching signals. To accumulate the required dataset, a Different Start-Same Step (DSSS) technique is also introduced to generate the training data and test/validation data in a separate way. The effect of the number of hidden layers on the network output error is investigated, and a proper number of hidden layers is designed based on a compromise between accuracy and computation efficiency (and execution time). Experimental results prove that the proposed artificial neural network-based PS method preserves the performance of classic optimum PS and minimizing the implementation time significantly.