Wind Farm Model Aggregation Using Probabilistic Clustering

被引：154

作者：

Ali, Muhammad ^{[1
]}

Ilie, Irinel-Sorin ^{[2
]}

Milanovic, Jovica V. ^{[1
]}

Chicco, Gianfranco ^{[3
]}

机构：

[1] Univ Manchester, Dept Elect & Elect Engn, Sch Elect & Elect Engn, Manchester, Lancs, England

[2] Univ Edinburgh, Inst Energy Syst, Edinburgh, Midlothian, Scotland

[3] Politecn Torino, Dipartimento Ingn Elettr, Turin, Italy

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2013年 / 28卷 / 01期

基金：

英国工程与自然科学研究理事会;

关键词：

Aggregation; clustering methods; dynamics; transient stability; wind farm modeling; wind power; POWER;

D O I：

10.1109/TPWRS.2012.2204282

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The paper proposes an innovative probabilistic clustering concept for aggregate modeling of wind farms (WFs). The proposed technique determines the number of equivalent turbines that can be used to represent large WF during the year in system studies. Support vector clustering (SVC) technique is used to cluster wind turbines (WTs) based on WF layout and incoming wind. These clusters are then arranged into groups, and finally through analysis of wind at the site, equivalent number ofWTs for WF representation is determined. The method is demonstrated on a WF consisting of 49 WTs connected to the grid through two transmission lines. Dynamic responses of the aggregate model of the WF are compared against responses of the full WF model for various wind scenarios.

引用

页码：309 / 316

页数：8

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