Modal-Augmented Chebyshev Physics-Informed Kolmogorov-Arnold Networks for Power Systems

Abstract

Physics-informed neural networks (PINNs) offer a promising alternative to numerical integration for power system dynamic simulation; however, their conventional architectures can struggle to capture electromechanical oscillations. Although physics-informed Kolmogorov-Arnold networks (PIKANs) offer an alternative, their B-spline implementations incur substantial training costs. To address these limitations, this paper investigates Chebyshev PIKANs (c-PIKANs) and proposes a modal-augmented c-PIKAN (mac-PIKAN) based on a trial solution that incorporates the closed-form modal response of the linearized swing equation. First, the modal response is used to capture the dominant oscillatory dynamics. Then, a c-PIKAN learns a nonlinear correction weighted by a quadratic time-dependent factor, ensuring that the initial rotor angle and speed are satisfied exactly. Evaluations on the single-machine infinite-bus benchmark demonstrate the computational efficiency of c-PIKAN and show that mac-PIKAN reduces PINN rotor-angle and frequency errors from 1.59% to 0.29% and from 5.26% to 0.99%, respectively, while maintaining higher accuracy across training-data sizes and at lower damping levels.