Dynamic Clustering and Operation Evaluation of Renewable Energy Station

Junsong Yu, Heyan Huang, Yaohui Xiao, Weifeng Ding, Jie Li, Hanxuan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Aiming at the difference of dynamic characteristics about doubly-fed wind turbines and the complexity of equivalence problems, a multi-machine dynamic equivalence model for wind farms based on improved K-means algorithm is proposed. Considering the input wind speed and the different state parameters of the units, several indexes are selected to dynamically group the units in the wind farm. Based on the clustering results, the proposed wind farm's equivalent model is established in MATLAB/Simulink. According to the simulation results, the multi-machine equivalent model has higher accuracy and better application effect than the single-machine equivalent model, which can more effectively depict the grid-connected characteristics and operational state of doubly-fed wind farms.

Original languageEnglish
Title of host publication2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4176-4181
Number of pages6
ISBN (Electronic)9798350345094
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023 - Hangzhou, China
Duration: 15 Dec 202318 Dec 2023

Publication series

Name2023 IEEE 7th Conference on Energy Internet and Energy System Integration, EI2 2023

Conference

Conference7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023
Country/TerritoryChina
CityHangzhou
Period15/12/2318/12/23

Keywords

  • equivalent modeling
  • genetic algorithm
  • group division
  • K-means
  • wind farm

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