MaWind - Tool for the Aggregation of Wind Farm Models

被引：0
|
作者：
Rudion, K. [1 ]
Styczynski, Z. A. [2 ]
Orths, A. [3 ]
Ruhle, O. [4 ]
机构：
[1] Otto VonGuericke Univ Magdegurg, Magdeburg, Germany
[2] Otto Von Guericke Univ, Fac Elect Engn & Informat Technol, Magdeburg, Germany
[3] Energinet dk Danish TSO, Frederiksberg, Denmark
[4] Siemens AG, Erlangen, Germany
来源：
2008 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, VOLS 1-11 | 2008年
关键词：
Power system; wind farm; model aggregation; dynamic simulation;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
The existing software for the simulation of power system operation was mainly developed and optimized for the analysis of conventional systems that are characterized by a low number of large, central synchronous generators. New forms of power generation, like wind turbines, that are characterized by a high number of small units can not be analyzed effectively with this software. In this paper a new software tool, MaWind, for the aggregation of wind farm models for dynamic system analysis is described. The MaWind tool uses a new mathematical approach to represent wind generation in system analysis. The background of this method, the method itself and some representative results of the calculation with MaWind are presented in the paper. MaWind allows for significant reduction of the model complexity while retaining a good approximation of dynamic farm behavior at the same time.
引用
收藏
页码：552 / 559
页数：8
相关论文
共 50 条
  • [1] Synthetic turbulence modelling for offshore wind farm engineering models using coherence aggregation
    Chabaud, Valentin
    WIND ENERGY, 2024, 27 (02) : 111 - 130
  • [2] The wind farm aggregation impact on power quality
    Bialasiewicz, Jan T.
    Muljadi, Eduard
    IECON 2006 - 32ND ANNUAL CONFERENCE ON IEEE INDUSTRIAL ELECTRONICS, VOLS 1-11, 2006, : 1953 - +
  • [3] TOPFARM - a tool for wind farm optimization
    Larsen, Gunner Chr
    Rethore, Pierre-Elouan
    DEEPWIND'2013 - SELECTED PAPERS FROM 10TH DEEP SEA OFFSHORE WIND R&D CONFERENCE, 2013, 35 : 317 - 324
  • [4] Probabilistic Models for Wind Turbine and Wind Farm Performance
    Arwade, Sanjay R.
    Lackner, Matthew A.
    Grigoriu, Mircea D.
    JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME, 2011, 133 (04):
  • [5] Models for monitoring wind farm power
    Kusiak, Andrew
    Zheng, Haiyang
    Song, Zhe
    RENEWABLE ENERGY, 2009, 34 (03) : 583 - 590
  • [6] Comparison between Aggregation Techniques for PMSG Wind Farm
    Badr, Mohammed. A.
    Atallah, Ahmed. M.
    Bayoumi, Mona A.
    INTERNATIONAL CONFERENCE ON TECHNOLOGIES AND MATERIALS FOR RENEWABLE ENERGY, ENVIRONMENT AND SUSTAINABILITY -TMREES15, 2015, 74 : 1162 - 1173
  • [7] Wind Farm Model Aggregation Using Probabilistic Clustering
    Ali, Muhammad
    Ilie, Irinel-Sorin
    Milanovic, Jovica V.
    Chicco, Gianfranco
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2013, 28 (01) : 309 - 316
  • [8] A Novel Aggregation Method for Doubly Fed Wind Farm
    Li, Lulu
    Han, Xueshan
    Li, Wenbo
    Jiang, Zhe
    2018 INTERNATIONAL SYMPOSIUM ON POWER ELECTRONICS AND CONTROL ENGINEERING (ISPECE 2018), 2019, 1187
  • [9] A New Tool for Wind Farm Optimal Design
    Serrano Gonzalez, J.
    Gonzalez Rodriguez, A. G.
    Castro Mora, J.
    Riquelme Santos, J.
    Burgos Payan, M.
    2009 IEEE BUCHAREST POWERTECH, VOLS 1-5, 2009, : 272 - +
  • [10] Clustering of Wind Speed Time Series as a Tool for Wind Farm Diagnosis
    Martins, Ana Alexandra
    Vaz, Daniel C.
    Silva, Tiago A. N.
    Cardoso, Margarida
    Carvalho, Alda
    MATHEMATICAL AND COMPUTATIONAL APPLICATIONS, 2024, 29 (03)
12345