arXiv:2607. 16258v1 Announce Type: cross Abstract: The application of artificial intelligence methods in power electronic converter modeling is becoming increasingly widespread, but existing applications still face many challenges, such as difficulties in multi-time-scale hybrid analysis and the lack of physics-aware evaluation criteria and constraints, resulting in poor performance.
arXiv:2608. 08048v1 Announce Type: cross Abstract: This paper presents, for the first time in power systems literature to our knowledge, analytical tools to explain the training performance of machine learning surrogate models for power system dynamics.
RAPTOR is a novel time‑domain simulation framework for power systems that uses a physics‑informed random‑projection neural network built from fixed Gaussian radial basis functions to represent the trajectory of hybrid differential‑algebraic equations over a time interval. By combining these basis functions with a nonlinear solver such as Newton‑Raphson, RAPTOR can capture complex multi‑timescale dynamics over longer intervals, reducing the number of simulation advances and nonlinear iterations needed. Numerical tests on RMS and EMT benchmarks demonstrate that RAPTOR achieves accurate solutions with significantly fewer steps and can outperform traditional methods like Radau and the trapezoidal rule, achieving speedups of over tenfold in some cases.
arXiv:2608. 03878v1 Announce Type: new Abstract: Synthetic power-grid scenarios are essential for planning, resilience assessment, contingency analysis, and data-driven power-system applications.
arXiv:2608. 15051v1 Announce Type: new Abstract: This paper proposes a Mamba surrogate model with mixture-of-experts (MoE) routing to represent the transient dynamics of inverter-based resources.
arXiv:2301. 12538v2 Announce Type: replace-cross Abstract: This paper develops an Operator Learning framework for approximating the dynamic response of synchronous generators.
arXiv:2608.30328v1 Announce Type: new Abstract: Classical numerical solvers for partial differential equations (PDEs) are computationally expensive to solve repeatedly across varying initial conditio...
arXiv:2607. 27681v1 Announce Type: new Abstract: Transient-stability assessment determines whether a power system can recover after a disturbance and is therefore essential to preventing generator trips and cascading outages.
arXiv:2410.23667v2 Announce Type: replace Abstract: Neural differential equations offer a powerful approach for learning dynamical systems from data. However, they do not inherently respect known con...
arXiv:2607. 14321v1 Announce Type: cross Abstract: Incorporating hysteresis and eddy currents into finite element simulations of laminated-core electrical machines is computationally challenging.
arXiv:2606. 20053v1 Announce Type: new Abstract: The Doyle-Fuller-Newman (DFN) model resolves internal electrochemical states in lithium-ion batteries with high fidelity.
arXiv:2609.13200v1 Announce Type: cross Abstract: Compact transistor models are the mathematical backbone of circuit simulation. However, at advanced nodes such as 3nm, transport physics becomes too...
arXiv:2607. 05280v1 Announce Type: new Abstract: Many real-world systems evolve continuously, yet most machine learning models interpret time series as discrete sequences.