eess.SYAug 31, 2026

RAPTOR: RAndom-projection Physics-informed Transient sOlveR

Authors: Petros Ellinas, Benjamin Vilmann, Spyros Chatzivasileiadis, Johanna Vorwerk

Organizations: Department of Wind and Energy Systems, Technical University of Denmark, Lyngby, Denmark

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Abstract

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.

Figures & tables

Fig. 2 : Workflow of the RAPTOR solver, including interval proposal, PIRPNN coefficient initialization, nonlinear residual minimization, residual-based acceptance, and PI-based interval-length update.
Figure 2
MetricIEEE 9IEEE 14IEEE 39IEEE 57IEEE 118
State MAE ( 10−810^{-8} )5.448.5418.79.702.42
∣V∣|V| MAE ( 10−810^{-8} )1.652.526.601.810.671
Time [ms]26.039.080.962.0311.5
Intervals2822272332
Relative Speed Up
BDF ( ×\times )2.792.451.911.962.19
TABLE I : IEEE RMS DAE benchmark performance for tf=1t_{f}=1 s. Errors are RAPTOR MAEs, and speedup of RAPTOR is relative to the fastest accuracy-qualified configuration of each solver.
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