Time-varying inertia estimation for grid-connected DFIG-based wind farms using sensitivity-guided clustering and aggregation

Abstract

Accurate inertia estimation for grid-connected doubly fed induction generator (DFIG)-based wind farms is essential for providing inertia support and maintaining frequency stability. However, due to the diversity of operating conditions and control parameters among individual wind turbines, a DFIG-based wind farm cannot be represented as a single equivalent device. In this paper, a time-varying inertia estimation framework based on sensitivity-guided clustering and aggregation is proposed. First, a sensitivity analysis framework is proposed to analyse the impact factors affecting the time-varying inertia of individual DFIGs, using the extended Fourier amplitude sensitivity test. After that, the dominant factor, identified as the virtual inertia control parameter, is selected as the clustering indicator. Subsequently, DFIGs with similar dominant factors are clustered based on limited measurement data, utilizing the unscented Kalman filter to reduce the requirement for extensive measurement devices. The time-varying inertia of the wind farm is then estimated using streaming dynamic mode decomposition with information from each cluster. Simulation and experimental results demonstrate that the proposed framework achieves high-accuracy inertia estimation with limited measurements, reducing the relative error by nearly 10% compared with existing methods. Moreover, it exhibits strong robustness to noise and disturbances, confirming the effectiveness of the inertia estimation.