With the increase in the number of power electronic devices in power systems, various techniques for assessing their stability have emerged. Among these techniques, impedance model-based stability analysis techniques have been widely used. However, conducting such analyses across multiple operating points requires abundant impedance measurement data from power electronic devices. In this paper, we propose a method for constructing impedance models of equipment with fewer impedance measurement data in voltage-source converter (VSC) back-to-back high-voltage direct current (HVDC) systems using physics-informed neural networks. Furthermore, given the power system states, we present a neural network approach to estimate grid stability at different operating points. Validation via PSCAD/EMTDC simulations and a PyTorch neural network confirmed the adequacy of these models.

Virtual power plants (VPPs) have emerged as a key solution for integrating distributed energy resources (DERs) into power systems, offering enhanced flexibility and supporting frequency and voltage stability. While traditional VPP models focus on static optimization and energy management, they fall short in capturing the dynamic responses required for transient stability analysis, especially in systems incorporating both grid‐following (GFL) and grid‐forming (GFM) inverters. The coexistence of GFM and GFL resources introduces complex, nonlinear interactions, which become even more challenging under topological reconfigurations or structural changes in the power systems. This paper proposes a neural network‐based spatiotemporal model for dynamic VPP representation using graph convolutional networks (GCNs) and long short‐term memory (LSTM) networks. The GCN captures both the static and dynamic structural topology of an 8‐bus VPP system, while the LSTM models temporal behavior. The combined architecture effectively learns the interactions among inverter‐based resources under various transient and reconfigured scenarios. High‐fidelity Electro‐Magnetic Transient (EMT) simulations validate the proposed method, demonstrating superior accuracy and better representation of dynamic behavior compared to conventional benchmark approaches. The framework provides a scalable solution for data‐driven transient stability analysis, even under evolving system structures.

Synchronous generators (SGs) provide the inertia and reserve power required to ensure frequency stability in the power system, while renewable resources do not contribute effectively. Maintaining frequency stability is challenging because of the increase in renewable resources and corresponding decline in SGs in response to climate change. Therefore, developing grid operation and planning strategies based on frequency stability evaluation has become increasingly important. In South Korea, frequency stability is evaluated based on frequency nadir during disturbances such as generator failures. This paper proposes a conservative piecewise-linearized frequency nadir (C-PLFN) model based on linearized relationships between the frequency nadir and system inertia, frequency regulating reserve space, governor-free reserve (GFR), and load size. The main features of the proposed methodology include linear regression on the frequency nadir against datasets of system inertia and GFR by load sizes, linear regression on the coefficients in the equation obtained by load sizes, and formalizing the C-PLFN model. This method was validated using the 2024 power system data for South Korea and the transmission security management data saved as real-time steady-state data for power system simulation for engineering simulations in South Korea.