Estimation of Power System Inertia Using Discrete Dynamic Regressor Extension and Mixing

With the increasing penetration of renewable energy sources (RES) in power systems, the traditional inertia characteristics based on synchronous generators (SG) are undergoing changes. The limited energy storage capacity of RES poses challenges in achieving sufficient inertia, leading to deeper frequency drops and severe incidents. Aims to address the need for accurate estimation of system inertia, this paper proposes an online inertia estimation method based on the ambient noise information of active power increment and frequency increment. The method utilizes an auto-regressive with extra inputs (ARX) model and discrete dynamic regressor extension and mixing (DREM) estimation to achieve accurate and online inertia estimation. The effectiveness of the proposed method is validated through simulations on the WSCC 9-bus system for both single-machine and regional scenarios. The results demonstrate the method's good accuracy in environments with large and small disturbances and in the presence of changes in regional inertia.