A Unified Approach for Learning the Dynamics of Power System Generators and Inverter-based Resources

Abstract

The growing prevalence of inverter-based resources (IBRs) for renewable energy integration and electrification greatly challenges power system dynamic analysis. To account for both synchronous generators (SGs) and IBRs, this work presents an approach for learning the model of an individual dynamic component. The recurrent neural network (RNN) model is used to match the recursive structure in predicting the key dynamical states of a component from its terminal bus voltage and set-point input. To deal with the fast transients especially due to IBRs, we develop a Stable Integral (SI-)RNN to mimic high-order integral methods that can enhance the stability and accuracy for the dynamic learning task. We demonstrate that the proposed SI-RNN model not only can successfully predict the component's dynamic behaviors, but also offers the possibility of efficiently computing the dynamic sensitivity relative to a set-point change. These capabilities have been numerically validated based on full-order Electromagnetic Transient (EMT) simulations on a small test system with both SGs and IBRs, particularly for predicting the dynamics of grid-forming inverters.

Figures (8)

• Figure 1: Schematic diagram of the averaged full-order GFM-inverter model with the reference signals, control system, and electrical system johnson2022generic.
• Figure 2: Modular architecture for modeling grid dynamics with each component represented as individual NNs. Each component has independent dynamic states, control inputs, and electrical input from the network venkatramanan2022integrated.
• Figure 3: Comparison of SG and GFM state responses to a three-phase grounded fault of the 5-bus system in Fig. \ref{['fig:GFM_diagram']}.
• Figure 4: Diagram of the proposed SI-RNN dynamics learning framework.
• Figure 5: One-line diagram of the SMIB system for learning the SG dynamics.