An Online Equivalent Method of Large-Scale Wind Power Based on Multi-source Data Fusion

  • Minghui YanEmail author
  • Zhen Yuan
  • Haifeng Zhou
  • Wei Xu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 585)


With the rapid increase of wind power centralized grid-connection scale, the security and stability of power grid are greatly affected. In order to simulate the dynamic characteristics of wind farm accurately, a large-scale wind power online equivalent method based on multi-source data fusion is proposed. The operating state of the state estimation modeling network is identified by the fusion of multi-source real-time data such as SCADA, PMU and security and stability control system. When the error of branch power flow or node voltage at the boundary is large, the active power regulation range of wind power generators is determined by comprehensively considering the actual measurement collected by the wind farm centralized control system and the prediction information of the wind power prediction system. The active power of each wind farm is determined by quadratic programming model. The wind power generators are grouped according to the topology in the wind farm, then each group is subdivided according to static characteristic and operating state in turn. The dynamic models and static parameters of equivalent wind power generators and equivalent transformers are calculated. The proposed method is proved to be fast and effective through the analysis of a practical power grid example.


Large-scale wind power Multi-source data fusion Quadratic programming Equivalent wind power generators 



The work described in this paper was supported by “Key Technologies Research and Application of Global Analysis and Control Adapt to Active Dispatching Demand of Power Grid” program.


  1. 1.
    Annual Report on the Development of China’s Power Sector. China Electricity Council, 2018/06/14Google Scholar
  2. 2.
    Liu Q, Fang J, Zhao J et al (2019) Layered wind power dispatching control strategy based on DSA power prediction model. Electr Power Constr 40(2):63–70Google Scholar
  3. 3.
    Chen N, He W, Qian M et al (2011) Design and application of reactive power control system for wind farm. Autom Eletr Power Syst 35(23):32–36Google Scholar
  4. 4.
    Zhao J, Zhang G, Huang Y (2014) Status and prospect of state estimation for power system containing renewable energy. Electr Power Autom Equip 34(5):7–20, 34Google Scholar
  5. 5.
    Yang B, Shu H, Qiu D et al (2019) Nonlinear robust state estimation feedback control of doubly-fed induction generator under variable wind speeds. Autom Electr Power Syst 43(4):60–69Google Scholar
  6. 6.
    Zhang X, Qiu W, Fang R et al (2019) Impedance modeling and sub-synchronous resonance mitigation strategy of doubly-fed induction generator based wind turbine in static reference frame. Autom Electr Power Syst 43(6):41–48Google Scholar
  7. 7.
    Liang H, Cheng Z, Sun H et al (2019) Optimization of power network reconstruction with wind farm considering uncertainty of wind power prediction error. Autom Electr Power Syst 43(7):151–158Google Scholar
  8. 8.
    Zhang G, Li F, Zhou S et al (2019) Wind power real-time active dispatch considering friendliness of wind farm integration. Power Syst Technol 43(2):664–669Google Scholar
  9. 9.
    Li X, Xing Z, Chen Z et al (2010) Design of large clusters of wind power active intelligent control system. Autom Electr Power Syst 34(17):59–63Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.NARI Group CorporationState Grid Electric Power Research InstituteNanjingChina

Personalised recommendations