Quantification of Wind Farm Stabilization Areas with the Grey Wolf Optimizer and Support Vector Machine

The performance of small signal stability in wind farms varies significantly due to fluctuations in power output and wind speeds. Developing a real-time representation of the stability region for these operational points is essential for maintaining stable operations. However, this task presents two primary challenges: the quantification of wind farm models in the frequency domain and the estimation of stability boundaries across multiple parameters. To overcome these obstacles, this paper initially conducts offline impedance modeling, followed by an online approach that utilizes machine learning to define the stability region boundaries for wind farms. By calculating the zeros of the nodal admittance matrix, we can ascertain the stability margins at different operating conditions. Furthermore, this paper proposes a hybrid algorithm that integrates the Grey Wolf Optimizer with Support Vector Machine to effectively delineate the multi-parameter stability region. The efficacy of these methods has been confirmed through time domain simulation with wind turbines and farms.