基于高斯混合模型聚类的双馈风电场动态等值建模方法

邓俊; 张阳; 李怡然; 夏楠; 戚正浩; 高桐

doi:10.19912/j.0254-0096.tynxb.2022-1571

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (1) : 342-350. DOI: 10.19912/j.0254-0096.tynxb.2022-1571

基于高斯混合模型聚类的双馈风电场动态等值建模方法

邓俊¹, 张阳², 李怡然¹, 夏楠¹, 戚正浩², 高桐²

作者信息 +

DYNAMIC EQUIVALENCE MODELING OF DOUBLY-FED WIND FARM BASED ON GAUSSIAN MIXTURE MODEL CLUSTERING ALGORITHM

Deng Jun¹, Zhang Yang², Li Yiran¹, Xia Nan¹, Qi Zhenghao², Gao Tong²

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文章历史 +

摘要

针对风电场动态运行条件下等值建模精度偏低、聚类依据不足的难题,提出一种基于高斯混合模型聚类思想的风电场等值建模方法。首先,分析单台双馈感应式风力发电机在低电压穿越期间的动态响应特性,根据响应特性的集群特征构建聚类指标。然后,提出基于高斯混合模型动态初步聚类、优化聚类数目的两阶段等值建模方法,推导出赤池信息和贝叶斯信息准则下聚类数目的寻优算法。以典型中等规模风电场为例,在Matlab/Simulink平台进行不同故障穿越条件的仿真测试,结果表明所提风电场等值建模方法聚类有效、精度高。

Abstract

In response to the difficulties of low accuracy of equivalence modeling and insufficient clustering basis under dynamic operating conditions of wind farms, a wind farm equivalence modeling method based on the idea of Gaussian mixture model clustering is proposed. First, the dynamic response characteristics of a single doubly-fed induction wind turbine during LVRT are analyzed, and the clustering indexes are constructed based on the clustering characteristics of the response characteristics. Then, a two-stage equivalence modeling method based on Gaussian mixture model with dynamic preliminary clustering and optimized number of clusters is proposed, and an optimization search algorithm for the number of clusters under the criteria of red pool information and Bayesian information is derived. Simulation tests with different fault ride-through conditions are carried out in Matlab/Simulink platform for a typical medium-scale wind farm, and the results show that the proposed equivalence modeling method for wind farms is effective in clustering with high accuracy.

导出引用

邓俊, 张阳, 李怡然, 夏楠, 戚正浩, 高桐. 基于高斯混合模型聚类的双馈风电场动态等值建模方法[J]. 太阳能学报. 2024, 45(1): 342-350 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1571

Deng Jun, Zhang Yang, Li Yiran, Xia Nan, Qi Zhenghao, Gao Tong. DYNAMIC EQUIVALENCE MODELING OF DOUBLY-FED WIND FARM BASED ON GAUSSIAN MIXTURE MODEL CLUSTERING ALGORITHM[J]. Acta Energiae Solaris Sinica. 2024, 45(1): 342-350 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1571

中图分类号： TM614

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