Journal of Shanghai Jiao Tong University ›› 2024, Vol. 58 ›› Issue (6): 798-805.doi: 10.16183/j.cnki.jsjtu.2022.476

• New Type Power System and the Integrated Energy • Previous Articles     Next Articles

Dynamic Equivalence Modeling of Short-Circuit Faults in Wind Farms Considering Wake Effects

YU Hao1, LI Canbing2(), YE Zhiliang2, PENG Sui1, REN Wanxin1, CHEN Sijie2, TANG Binwei3, CHEN Dawei2   

  1. 1. Power Grid Planning and Research Center, Guangdong Power Grid Co., Ltd., Guangzhou 510080, China
    2. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    3. Mingyang Smart Energy Group Co., Ltd., Zhongshan 528437, Guangdong, China
  • Received:2022-11-25 Revised:2023-05-29 Accepted:2023-07-31 Online:2024-06-28 Published:2024-07-05

Abstract:

Fast and accurate analysis of the short-circuit characteristics of large wind farms has important engineering application value, and the short-circuit characteristics of wind farms under the influence of the wake effect vary greatly. Therefore, it is necessary to establish a wind farm short-circuit fault time equivalence model. A wind farm short-circuit fault dynamic equivalence method considering the effect of wake effect is proposed. First, the wake effect factor is defined to reflect the degree of the unit affected by the wake effect. Then, the wake effect factor is used as the grouping basis to reduce the variability of operating state of the units within the group under the influence of the wake effect. A positive- negative- zero-sequence network equivalence method is analyzed to improve the effectiveness of the equivalence model in asymmetric short-circuit faults. An equivalence method suitable for zero-sequence network is proposed and a platform is built for verification. The simulation results show that the dynamic short-circuit fault equivalence model proposed can accurately reflect the active and reactive short-circuit output characteristics of wind farms under the influence of the wake effect.

Key words: wake effect, wind farm, short-circuit fault, clustering algorithm, dynamic equivalence model

CLC Number: