Linear Power System Modeling and Analysis Across Wide Operating Ranges: A Hierarchical Neural State-Space Equation Approach
As modern power systems exhibit increasingly high-dimensional, nonlinear, and uncertain characteristics, the applicability of classical linear state-space methods is severely challenged. Existing paradigms struggle to reconcile the analytical transparency of physics-based models with the continuous nonlinear generalization of AI. To address this, the Hierarchical Neural State-Space Equation (HNSSE) framework is proposed. At the component level, the formulated Neural State-Space Equation (NSSE) extends neural ordinary differential equations (NODEs) to learn continuous dynamic manifolds across varying conditions while strictly preserving local analytical transparency. At the system level, a hierarchical architecture analytically fuses components via network constraints, constructing an interaction-consistent global NODE while circumventing the curse of dimensionality. To ensure robust convergence under noisy measurements, a training strategy synergizing spatiotemporal slicing, physics-informed curriculum learning, and Expectation-Maximization-based refinement is established. Validation on the large-scale Guangdong Power Grid demonstrates the framework's remarkable performance in interpretable state-space reconstruction, high-fidelity trajectory prediction, continuous stability perception, and noise robustness. Comprehensive comparisons substantiate HNSSE's superiority as a unified, interpretable paradigm for complex power system modeling.