提升物理信息神经网络，加速高阶电网动态模拟
Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamics
- 融合物理规律与神经网络，优化高阶微分方程求解
- 在同步发电机暂态仿真中显著提高精度与训练稳定性
- 适合电力系统仿真与可再生能源并网研究者使用
我们开发了改进的物理信息神经网络（PINNs），用于求解由非线性常微分方程描述的高阶、高维电力系统模型。通过提出若干新增强机制，并整合文献中近期提出的有效方法，显著提升了PINN的训练效果与求解精度。成功应用于同步发电机暂态动力学分析，并在先进逆变器建模应用上取得进展。该增强型PINNs可大幅加速高保真仿真，为构建稳定可靠的高比例可再生能源电网提供技术支撑。
原文摘要 · Abstract (English)
We develop improved physics-informed neural networks (PINNs) for high-order and high-dimensional power system models described by nonlinear ordinary differential equations. We propose some novel enhancements to improve PINN training and accuracy and also implement several other recently proposed ideas from the literature. We successfully apply these to study the transient dynamics of synchronous generators. We also make progress towards applying PINNs to advanced inverter models. Such enhanced PINNs can allow us to accelerate high-fidelity simulations needed to ensure a stable and reliable renewables-rich future grid.
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