Quantitative Inertia Estimation Method for Wind Farm Based on AFF-RLS

• Published in: 2022 8th International Conference on Control Science and Systems Engineering (ICCSSE) pp. 74 - 78
• Main Authors: Qin, Hao, Cao, Yongji, Zhang, Hengxu, Gao, Zhimin, Zhang, Xiaoning, Li, Changgang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.07.2022
• Subjects: Adaptation models; equivalent inertia; Estimation; forgetting factor recursive least squares; fuzzy control; Heuristic algorithms; Power system stability; Stability analysis; wind farm; Wind farms; Wind power generation
• DOI: 10.1109/ICCSSE55346.2022.10079818

In order to guarantee the frequency security and stability of the power system with high proportional of wind generation, virtual inertia control techniques are gradually being promoted and applied. However, there is still a lack of methods and techniques to accurately estimate the equivalent virtual inertia of wind farms in real time, which makes it difficult to quantitatively evaluate the effect of virtual inertia control and to dynamically monitor the inertia level of power systems. Therefore, in this paper, an AR model with eXogenous input is applied to the time-varying characteristics of the virtual inertia of wind farm, and a recursive least squares identification algorithm based on a fuzzy control algorithm with an adaptive forgetting factor is proposed to estimate the virtual inertia of wind farms online. The proposed identification model and solution algorithm have superior tracking ability and good convergence. All that is required is to obtain perturbation data on the frequency and active power of the point of common coupling from the phasor measurement unit. A case study is carried out to verify the effectiveness of the proposed method.