As modern power systems become increasingly complex—integrating diverse inverter-based resources and advanced control schemes—there is a growing need for analysis tools that combine analytical rigor with computational flexibility. Traditional purely numerical simulations often lack transparency. SyNAPS bridges this gap by unifying symbolic computation with numerical simulation, enabling scalable modeling, analysis, and control design for DC and AC power systems.
It has the following key capabilities:
Integrates SymPy and SciPy to connect symbolic derivations with numerical evaluation. Symbolic models capture system structure and parameter dependencies, while numerical routines reduce the modeling complexity and enable scalable evaluation and simulation.
Automatically derives Jacobian matrices and linearized state-space models from nonlinear differential algebraic equations (DAEs). This enables transparent analysis of how operating points, grid conditions, and control strategies influence small-signal dynamics.
Leverages SciPy for numerical linear algebra, eigenvalue analysis, and time-domain simulation.
Defines parameter spaces (e.g., droop gains, PLL gains) and certifies stability using algebraic methods such as Linear Matrix Inequalities (LMIs) and convex optimization. Facilitates control co-design and exploration of stability boundaries.
