English(EN) Sequential Learning-Based Physics-Informed Neural Network Modeling for Parameter Estimation on a Power Converter

新的PINN方法可准确估计DC降压变换器参数

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了一种分阶段的物理信息神经网络（PINN）来准确估计DC降压变换器的参数。该方法将参数识别分解为顺序阶段，首先是电感（L）和电容（C），然后是寄生电阻（rL和rC）。与同时训练方法相比，分阶段方法显著提高了准确性和速度，即使在所有四个组件同时扰动的情况下也能实现高精度。 AI

影响这项研究可能导致更高效、更准确的电力电子元件建模，从而可能改进其设计和性能。

排序理由该条目是一篇学术论文，详细介绍了一种新的参数估计建模方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Samuela Rokocakau, Panagiotis Kakosimos, Giulia Tresca, Pericle Zanchetta · 2026-10-15 04:00

用于电力转换器参数估计的基于序列学习的物理信息神经网络建模

arXiv:2610.20041v1 Announce Type: new Abstract: A staged physics-informed neural network (PINN) is proposed for identifying the inductance, capacitance and parasitic resistances of a DC buck converter from time-domain data. Gradients are released in the order of L to C to rL to r…