基于可微分物理仿真的电力电子变换器参数估计
Parameter Estimation of Power Electronic Converters with Differentiable Physics Simulation
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中文总结 AI 辅助
该研究提出基于可微分物理仿真的方法，用于电力电子变换器无创参数估计，经仿真与30种硬件配置实验验证，可有效追踪关键部件健康参数变化，为相关领域提供新视角。
中文摘要 AI 辅助
本文提出一种基于可微分物理仿真（DP仿真）的电力电子变换器状态监测参数估计方法。该方法将变换器动态的时域仿真嵌入可微分计算图，直接关联器件参数与观测到的电压、电流轨迹。通过构建可微分时间步长算子，以统一可微分方式仿真不同电路拓扑下变换器的非线性动态。以DC-DC降压变换器为代表性案例，利用现有传感通道的稀疏瞬态样本，该方法无需额外传感硬件即可实现无创参数估计。开展全面仿真研究评估时间步长方案、正则化约束及各类不确定性来源对估计精度与鲁棒性的影响，随后对30种不同硬件配置进行实验验证。结果表明，该方法可有效追踪关键部件健康相关参数的相对变化，此DP仿真框架为电力电子应用中物理信息机器学习提供了新视角。
英文摘要
This article proposes a differentiable physics simulation (DP simulation)-based parameter estimation method for the condition monitoring of power electronic converters. In the proposed method, the time-domain simulation of converter dynamics is embedded into a differentiable computational graph, directly linking device parameters to observed voltage and current trajectories. By formulating differentiable time-stepping operators, the nonlinear dynamics of the converter across different circuit topologies are simulated in a unified, differentiable manner. A dc-dc buck converter is used as a representative case study. Using sparse transient samples from existing sensing channels, the method enables noninvasive parameter estimation without additional sensing hardware. Comprehensive simulation studies are conducted to evaluate the impacts of time-stepping schemes, regularization constraints, and various uncertainty sources on estimation accuracy and robustness. Subsequently, 30 distinct hardware configurations are experimentally tested for validation. The results show that the proposed method can effectively track the relative variations of health-related parameters across the critical components. This DP simulation framework provides a novel perspective for physics-informed machine learning in power electronic applications.
