The future electric grid is supported by a vast number of smart inverters interfacing with distributed energy resources at the edge. These inverters’ dynamics are typically characterized as impedances, which are crucial for ensuring grid stability and resiliency. However, the physical implementation of these inverters may change significantly from inverters to inverters and may be kept confidential. Existing analytical impedance models require a complete and precise understanding of system parameters. They can hardly capture the complete electrical behaviors when the inverters are performing complex functions. Online impedance measurements for many inverters across multiple operating points are not scalable. To address these issues, we present InvNet, a machine learning framework to systematically evaluate the effectiveness of data-driven methods for modeling inverter impedance patterns across a wide operation range, even with limited impedance data. Leveraging transfer learning, the InvNet can extrapolate from physics-based models to real-world ones and from one inverter to another with very limited data. This framework demonstrates machine  learning as a powerful tool for modeling and analyzing black-box characteristics of grid-tied inverter systems that cannot be accurately described by traditional analytical methods, such as inverters under model predictive control. Comprehensive evaluations were conducted to verify the effectiveness of the InvNet in various scenarios. All data and models were open-sourced.
