Frequency-domain analysis based on converter output admittance is a key tool for studying converter-driven stability in power grids. This paper presents a Python-based identification tool built on a completely open-source simulator, eliminating the need for commercial licenses such as MATLAB or PSCAD and improving configurability through an MIT-licensed stack. The identification method uses steady-state signal injection with a sinusoidal sweep, deriving frequency-domain admittance from time-domain simulations. Analytical output-admittance models are developed for both grid-forming (disturbance-observer-based) and grid-following (phase-locked-loop-based) control to verify the numerical results. The tool's results are compared against a commercial PSCAD-based alternative, demonstrating accurate admittance identification across control methods. Code and examples are available online to support reproducibility.
Indoor robotics research increasingly relies on micro-UAVs whose airframe, electronics, and control software are fully open to modification. Off-the-shelf platforms rarely expose the low-level access required for such modifications, while building a custom alternative typically r…
The current-constrained power-angle curve (PAC) is crucial for the transient synchronization stability (TSS) analysis of virtual admittance-based (VA) grid-forming (GFM) converters. Its formulation and application rely on the quasi-steady-state assumption critically, i.e., the ac…
This work presents an examination of the main interactions between grid-following (GFL) and grid-forming (GFM) voltage source converters (VSCs) and synchronous generators (SGs), capturing the dynamics of a real power grid and pointing out the limitations of considering an ideal o…
To ensure power system stability and security, it is vital to understand the complex nonlinear power system dynamics related to converter-interfaced generators. For example, grid-forming (GFM) converters are expected to be a key asset for maintaining a strong and stable power sys…
This paper investigates physics-informed neural networks for modeling the full dynamic behavior of droop-controlled grid-forming converters. The approach is trained on synthetic data generated via numerical solvers and benchmarked against both traditional integration methods and…
Reachability analysis of safety-critical systems requires computing rigorous enclosures of all possible state trajectories. Taylor Model (TM)-based methods have proved effective at mitigating the so-called wrapping effect which leads to overly conservative enclosures of reachable…