Intelligent Impedance Identification and Stability Assessment of Wind Farm Based on Steady-state Information and Conditional Distributed Adaptive Method

There are potential small disturbance stability risks in large-scale wind power centralized grid-connected systems. On-line stability assessment technology based on steady-state operation data can provide key support for oscillation early warning. To evaluate the stability of wind farms, data-driven impedance stability evaluation methods are proposed. However, these methods require a large amount of data to train the model, which is difficult to apply to the scene with less measured data. Therefore, an intelligent impedance identification and stability assessment of wind farms is based on steady-state information and a conditionally distributed adaptive method. This method is based on steady-state data combined with state estimation to evaluate the stability of wind farms, which avoids the delay problem of stability judgment by direct measurement. To alleviate the contradiction between the data required by the data-driven method and the insufficient measurement data, a wind turbine impedance identification model is constructed by applying the conditional distribution adaptive method, combining a large amount of simulation data and a small amount of measured data. The phase angle obtained by state estimation is used to construct a wind farm impedance model and realize online evaluation, and the cost is measured without significantly reducing the accuracy. Finally, taking the grid-connected wind farm with 25 wind turbines as an example, the correctness of the proposed method is verified.