Open AccessDynamic equivalent method of PMSG‐based wind farm for power system stability analysis
Author(s) -
Wang Tong,
Gao Mingyang,
Mi Dengkai,
Huang Shilou,
Wang Zengping
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2020.0006
Subject(s) - control theory (sociology) , wind power , cluster analysis , particle swarm optimization , robustness (evolution) , computer science , electric power system , time domain , mathematical optimization , engineering , mathematics , power (physics) , algorithm , artificial intelligence , biochemistry , chemistry , physics , control (management) , electrical engineering , computer vision , gene , quantum mechanics
This study presents a dynamic equivalent method of wind farms with permanent magnetic synchronous generator (PMSG) wind turbines for power system stability analysis. First, a novel clustering indicator is set up based on the morphology similarity distance and cosine similarity between trajectories with the measurable output characteristics time series of wind turbines, and the K ‐means algorithm is applied to cluster the PMSGs. Combining silhouette coefficient, the robustness analysis of the clustering method under different scenarios is adopted. In each cluster, a trajectory sensitivity based key parameters selection is introduced to reduce the control parameters to be identified. Moreover, a multi‐objective adaptive function is established and the multi‐controllers aggregated parameters are optimised by adaptive evolutionary particle swarm optimisation algorithm. Thus, a wind farm model represented by multi equivalent wind turbines is obtained. The IEEE 39‐bus system with a PMSG‐based wind farm is used as test system to verify the validity of the proposed dynamic equivalent approach in time domain and frequency domain, respectively. It has been demonstrated that the proposed model can reduce the model complexity and improve the calculation efficiency of wind farms.