Power systems increasingly rely on converter-based resources such as solar inverters, wind turbines, and energy storage systems. Understanding how these converters interact with the grid is essential for maintaining stability, particularly as traditional synchronous generators are retired. A key analytical parameter is the converter's output admittance—essentially how the device responds to voltage disturbances across different frequencies.
Researchers have now released a comprehensive Python-based tool that identifies converter output admittance without requiring expensive proprietary software. Built entirely on open-source libraries and released under an MIT license, the tool performs frequency-domain analysis by injecting sinusoidal test signals into time-domain simulations and extracting admittance characteristics.
The methodology supports both major converter control architectures: grid-forming systems using disturbance observers and grid-following systems using phase-locked loops. The team validated their approach by comparing results directly against commercial PSCAD simulations, confirming accuracy across diverse operating conditions and control strategies.
The practical impact is significant. Network operators, equipment manufacturers, and academic researchers can now conduct rigorous converter stability studies without licensing barriers. This democratization of analysis tools encourages reproducibility and enables faster iteration on new control designs. The freely available code and worked examples lower the barrier to entry for institutions in developing nations or smaller utilities with limited software budgets.
For the power industry, this tool addresses a genuine bottleneck in converter-driven grid studies. As penetration of renewable energy and distributed resources accelerates, the ability to rapidly characterize converter behavior becomes increasingly valuable for grid planning and protection coordination. The open-source model also invites community contributions, likely driving continuous improvement in identification methods and expanding the tool's capabilities.
