A Revised Classification of Inertia Estimation Considering General and Time Horizon Perspectives

Abstract

The increasing integration of renewable energy sources (RES) and the corresponding decline of synchronous generators (SGs) have significantly reduced power system inertia, posing challenges to frequency stability and grid resilience. Inertia estimation (IE) is essential for understanding system dynamics and enabling effective frequency control. This paper presents a revised classification framework for IE methods that considers two main dimensions: the general methodological approach (model-based vs. measurementbased) and the time horizon of estimation (offline vs. online/realtime). Model-based methods are suited for conventional grids with detailed generator models. In contrast, the measurement-based techniques are better aligned with modern converter-interfaced systems through phasor measurement unit (PMU) data. The paper evaluates the strengths and limitations how different data types—ambient, ringdown, and probing—affect estimation strategies. This classification aims to support system operators and researchers in selecting appropriate IE strategies under diverse operating conditions and guides future development of inertia-aware control frameworks.