Transfer function based equivalent modeling method for wind farm

作     者：Feng WU Junxia QIAN Ping JU Xiaoping ZHANG Yuqing JIN Dan XU Michael STERLING 

作者机构：College of Energy and Electrical EngineeringHohai UniversityNanjing 210098China Department of ElectronicElectrical and Computer EngineeringUniversity of BirminghamBirminghamB152TTUK 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2019年第7卷第3期

页      面：549-557页

核心收录：

学科分类：08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：supported by National Natural Science Foundation of China (No. 51422701) the support of Chinese National ‘‘111’’ Project of ‘‘Renewable Energy and Smart Grid’’ at Hohai University 

主　　题：Wind farm Equivalent modeling Transfer function Parameter estimation Particle swarm optimization 

摘      要：To effectively study the dynamics of power systems with large-scale wind farms(WFs), an equivalent model needs to be developed. It is well known that back-toback converters and their controllers are important for the dynamic responses of the wind turbine(WT) under disturbances. However, the detailed structure and parameters of the back-to-back converters and their controllers are usually unknown to power grid operators. Hence, it is difficult to build an accurate equivalent model for the WF using the component model-based equivalent modeling method. In this paper, a transfer function based equivalent modeling method for the WF is proposed. During modeling, the detailed structure and parameters of the WF are not required. The objective of the method is reproducing the output dynamics of the WF under the variation of the wind speed and power grid faults. A decoupled parameter-estimation strategy is also developed to estimate the parameters of the equivalent model. A WF that consists of 16 WTs is used to test the proposed equivalent model. Additionally,the proposed equivalent modeling method is applied to build the equivalent model for a real WF in Northwest China. The effectiveness of the proposed method is validated by the real measurement data.