Decentralized Dynamic Parameter Identification of Modern Power Systems Using Ambient Measurements

With the integration of high-penetration power electronics, the dynamic characteristics of modern power systems are jointly dominated by synchronous generators (SGs) and virtual synchronous machines (VSMs). However, the accuracy of these system parameters cannot always be guaranteed in real-world scenarios. To tackle this issue, we propose a robust parameter identification and correction framework based on trajectory sensitivity analysis and Pareto smoothed importance sampling (PSIS). First, to identify the sources of dynamic anomalies, we employ trajectory sensitivity analysis to quantify the impact of parameter variations and screen out key parameters for targeted identification. Subsequently, to utilize the readily available ambient measurements, we incorporate successive variational mode decomposition (SVMD). This method adaptively extracts the dominant variation modes, thereby recovering high-quality data for the identification process. Finally, to circumvent the weight degradation problem inherent in traditional particle filters, we propose a cost-effective PSIS algorithm to obtain the robust posterior distribution of modern system parameters. Simulation results demonstrate the excellent performance of the proposed method. It can not only precisely estimate the key parameters of both SGs and VSMs but also realize the automatic correction of dynamic models under complex operating scenarios.