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考虑LVRT功率特性的直驱永磁风电场多机聚合辨识等值建模方法

左佳鑫 杨秀 赵晓莉 熊雪君 张雅君 冯煜尧

电力系统保护与控制2025,Vol.53Issue(2):14-26,13.
电力系统保护与控制2025,Vol.53Issue(2):14-26,13.DOI:10.19783/j.cnki.pspc.240595

考虑LVRT功率特性的直驱永磁风电场多机聚合辨识等值建模方法

Equivalence modeling method for multi-machine aggregation identification of direct-drive permanent magnet wind farm considering LVRT power characteristics

左佳鑫 1杨秀 1赵晓莉 1熊雪君 2张雅君 2冯煜尧2

作者信息

  • 1. 上海电力大学电气工程学院,上海 200090
  • 2. 国网上海市电力公司电力科学研究院,上海 200437
  • 折叠

摘要

Abstract

Aiming at the problem of low accuracy of wind farm equivalent models under fault scenarios,a multi-machine aggregation identification equivalence modeling method for wind farm considering low voltage ride through(LVRT)power characteristics is proposed.Firstly,the wind farm is initially grouped based on the typical differences in active power dynamic characteristics during LVRT of wind turbines.Secondly,the Ng-Jordan-Weiss(NJW)algorithm based on dynamic time warping(DTW)metric is used to realize the secondary division of the cluster,and the final two-stage clustering results are obtained.Then,for the aggregated multi-machine equivalent model of the wind farm,parameter sensitivity analysis is used to determine the key parameters to be optimized.The aggregated values of each parameter are taken as the initial values,and the overall equivalent parameters of the wind farm are optimized using the three strategies of single-machine step-by-step identification,multi-machine sequential identification,and equivalent impedance identification.Finally,the fitting curves and equivalent errors of different methods are compared,and the results show that the proposed method effectively improves the accuracy and adaptability of the equivalent model.

关键词

直驱永磁风电场/低压穿越/分群聚类/多机等值/参数辨识

Key words

direct-drive permanent magnet wind farm/LVRT/grouping and clustering/multi-machine equivalence/parameter identification

引用本文复制引用

左佳鑫,杨秀,赵晓莉,熊雪君,张雅君,冯煜尧..考虑LVRT功率特性的直驱永磁风电场多机聚合辨识等值建模方法[J].电力系统保护与控制,2025,53(2):14-26,13.

基金项目

This work is supported by the National Natural Science Foundation of China(No.52177098). 国家自然科学基金项目资助(52177098) (No.52177098)

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