Real-Time Inertia and Damping Estimation for AC Microgrids With Virtual Synchronous Generators

The rapid integration of inverter-based resources (IBRs) in ac microgrids (MGs) has fundamentally altered system dynamics, leading to reduced inertia and increased complexity in parameter estimation. Accurate real-time identification of inertia and damping is critical for ensuring system stability, yet remains challenging due to limited system observability and the presence of both conventional and power electronic sources. This article addresses these challenges by proposing a comprehensive real-time inertia and damping estimation technique for ac MGs, applicable to both conventional and IBR systems. The proposed technique estimates the aggregated dynamic parameters, including inertia, damping, and mechanical power, at a specific bus using only local voltage and current measurements. The technique follows a hierarchal structure using three main components: 1) recursive least squares (RLS) algorithm to estimate the inertia; 2) Kalman filter (KF) damping coefficient estimation; and 3) ordinary least square (OLS) algorithm to estimate the equivalent frequency restoration gains. The estimated gains are then used to obtain the total mechanical power, which is then fed-back to enhance other estimations, forming a closed-loop self-reinforcing process. Extensive experimental validations are conducted on both single- and multi-IBR systems with varying power ratings and inertia constants including cases such as load-generation imbalance, nonlinear load and unbalanced load. The results are curried out to demonstrate the accuracy and robust performance at different operating scenarios.