Assessment of the Equivalent Inertia of the Power System Considering the Virtual Inertia of Wind Farms

Background: With the large-scale integration of wind turbines into the grid and the implementation of virtual inertia control for participation in system frequency regulation, virtual inertia has become a significant component of system inertia. However, there is currently a lack of precise theoretical methods for estimating the equivalent virtual inertia of wind farms, which hinders the quantitative assessment of their contributions to grid inertia. Objective: The goal is to achieve online monitoring of the equivalent time-varying inertia in new energy power systems utilizing wind power virtual inertia, to minimize the impact of model order on inertia estimation, and to enhance the accuracy of online evaluations of system equivalent inertia. Methods: Phasor Measurement Units (PMUs) are utilized to obtain active power-frequency data at generator nodes. The active output power of the generators serves as the input to the identification model, while frequency disturbances act as the output, thereby constructing a controlled autoregressive identification model for the generators. The model order is determined using the Akaike Information Criterion (AIC). Finally, a diminishing memory recursive least squares algorithm is employed to calculate the equivalent inertia of the system. Results: The proposed method can determine the order of the identification model and estimate the equivalent inertia of synchronous machines and wind farms. Conclusion: The proposed identification model and algorithm demonstrate rapid tracking capability and strong convergence, facilitating precise estimation of the quickly time-varying equivalent virtual inertia of wind farms and continuous monitoring of the system's time-varying equivalent inertia during disturbances.