标题： 基于ANN的电网阻抗估计用于动态电网条件下VSG的自适应增益调度 显示英文标题

标题： ANN-Based Grid Impedance Estimation for Adaptive Gain Scheduling in VSG Under Dynamic Grid Conditions

摘要： 与跟踪电网的逆变器相反，虚拟同步发电机（VSGs）在弱电网条件下表现良好，但在电网较强时可能会变得不稳定。电网强度取决于电网阻抗，而电网阻抗不幸会随时间变化。在本文中，我们为VSGs提出了一种新颖的自适应增益调度控制方案。首先，人工神经网络（ANN）估计基频电网阻抗；然后将这些估计值输入到自适应增益调度函数中，以在不同电网条件下重新计算控制器参数。所提出的方法在Simulink中进行了验证，并与采用固定控制器增益的传统VSG进行了比较。结果表明，不同电网条件下的调节时间和超调百分比保持一致。此外，以前未见过的电网阻抗值被高精度且低时延地估计出来，使该方法非常适合实时增益调度控制。 显示英文摘要

摘要： In contrast to grid-following inverters, Virtual Synchronous Generators (VSGs) perform well under weak grid conditions but may become unstable when the grid is strong. Grid strength depends on grid impedance, which unfortunately varies over time. In this paper, we propose a novel adaptive gain-scheduling control scheme for VSGs. First, an Artificial Neural Network (ANN) estimates the fundamental-frequency grid impedance; then these estimates are fed into an adaptive gain-scheduling function to recalculate controller parameters under varying grid conditions. The proposed method is validated in Simulink and compared with a conventional VSG employing fixed controller gains. The results demonstrate that settling times and overshoot percentages remain consistent across different grid conditions. Additionally, previously unseen grid impedance values are estimated with high accuracy and minimal time delay, making the approach well suited for real-time gain-scheduling control.