发表机构
Technical University of Denmark（丹麦技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
RAPTOR提出基于随机投影的物理信息神经网络积分方法，用于电力系统多时间尺度瞬态仿真，在多个基准上实现超10倍加速并保持高精度。
AI 中文摘要
现代电力系统时域仿真的复杂性显著增加，因为基于变流器的资源引入了控制动态，这些动态必须与较慢的系统级动态和快速电磁动态同时仿真。由此产生的宽时间尺度范围可能迫使经典时域求解器使用小时间步长，使每个时间步长的非线性方程求解复杂化，并在强非线性和多时间尺度瞬态条件下降低求解器的可靠性。本文介绍了RAPTOR，一种首创的电力系统动态仿真时域仿真框架。其核心是，RAPTOR引入了一种新的积分技术，该技术使用由固定高斯径向基函数（RBF）构建的物理信息随机投影神经网络（PIRPNN），在时间间隔上表示混合微分代数方程（DAE）的未知轨迹。非线性求解器（如牛顿-拉夫逊法）确定如何组合固定RBF，使得所得轨迹满足控制方程。通过结合宽泛和局部形状的RBF，RAPTOR可以在相对较长的时间间隔内表示复杂的多时间尺度行为。这使得有效仿真步长更大，整体非线性求解器迭代次数更少，并在长时间范围内实现更快的积分。对刚性RMS模型、IEEE 9、14、39、57和118节点RMS仿真基准以及EMT测试案例的数值研究表明，RAPTOR能够以显著更少的顺序仿真步长准确找到解，同时提供更优的精度-运行时间性能。在某些情况下加速超过10倍，这些结果展示了RAPTOR超越长期广泛使用的积分方法（如Radau方法和梯形法则）的潜力。
英文摘要
The complexity of time-domain simulation of modern power systems has increased significantly because converter-based resources introduce control dynamics that must be simulated alongside slower system-level and fast electromagnetic dynamics. The resulting wide range of timescales may force classical time-domain solvers to use small timesteps, complicate the solution of nonlinear equations at each timestep, and reduce solver reliability under strongly nonlinear and multi-timescale transient conditions. This paper introduces RAPTOR, a first-of-its-kind time-domain simulation framework for power system dynamic simulations. At its core, RAPTOR introduces a new integration technique that represents the unknown trajectory of hybrid differential-algebraic equations (DAEs) over a time interval using a physics-informed random-projection neural network (PIRPNN) built from fixed Gaussian radial basis functions (RBFs). A nonlinear solver, e.g., Newton-Raphson, determines how to combine the fixed RBFs so that the resulting trajectory satisfies the governing equations. By combining RBFs with broad and localized shapes, RAPTOR can represent complex multi-timescale behavior over comparatively long time intervals. This enables larger effective simulation advances, fewer overall nonlinear solver iterations, and faster integration over long time horizons. Numerical studies on stiff RMS models, IEEE 9-, 14-, 39-, 57-, and 118-bus RMS simulation benchmarks, and EMT test cases show that RAPTOR accurately finds solutions with substantially fewer sequential simulation advances while offering superior accuracy-runtime performance. With speedups of over 10x in certain cases, these results showcase RAPTOR's potential to outperform long-standing and widely used integration methods, such as the Radau method and the trapezoidal rule.
