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This paper presents a data-driven approach using artificial neural networks (ANNs) to optimize phase-shifting in parallel-connected buck converters, aimed at minimizing current ripple and enhancing performance. The proposed method allows for real-time identification of the global optimum phase shift without complex calculations, improving system robustness and efficiency. Experimental results demonstrate that the ANN-based phase shift method significantly reduces implementation time while maintaining the ef

Author

Jaja Loco

Language

EN