A Pure Data-Driven Method for Online Inertia Estimation in Power Systems Using Local Rational Model Approach

Paper in proceeding, 2022

This paper proposes a novel data-driven method for estimating inertia constants of synchronous generators (SGs) connected to the power system. The proposed method only requires ambient data which can be continuously measured by PMUs in the normal operation of the system. A non-parametric technique based on the local rational model (LRM) is used to identify frequency response functions (FRFs) of SGs from the measured ambient data. Then, the inertia constants of SGs are estimated from FRFs. Numerical studies on the WSCC 9-bus system demonstrate that the proposed method can accurately estimate the inertia constants of SGs in short time windows, which enables tracking total system inertia. Likewise, the robustness of the proposed method against measurement noises is confirmed.