Structure Preserving Aggregation Method for Doubly-Fed Induction Generators in Wind Power Conversion

被引:5
作者
Li, Wei [1 ]
Kaffashan, Iman [1 ]
Gole, Aniruddha M. [1 ]
Zhang, Yi [2 ]
机构
[1] Univ Manitoba, Winnipeg, MB R3T 2N2, Canada
[2] RTDS Technol Inc, Winnipeg, MB R3T 2E1, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Doubly fed induction generators; Mathematical models; Rotors; Computational modeling; Voltage; Wind farms; Aggregates; Aggregation method; DFIG; dynamic model; EMT simulation; structural preserving; COHERENCY IDENTIFICATION; DYNAMIC-MODEL; TURBINES; SYSTEM; CONVERTER; FARM;
D O I
10.1109/TEC.2021.3126571
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
An aggregation method is proposed that transforms the multiple DFIGs into an equivalent DFIG model that retains the major collective dynamic characteristics of a group of DFIGs. It is intended for Electromagnetic Transients Simulation (EMT). The aggregated machine can take into account different speeds and parameters of each of the individual DFIGs and the connecting impedance of individual DFIGs. Starting with a State Variable (SV) model of an individual DFIG, aggregation is carried out recursively, by combining two DFIGs at a time and then reducing the order of the aggregate to match the state variable equations of a single DFIG so that the steady state performances are identical. Validation is carried out by comparing the detailed electromagnetic transient (EMT) simulation of the unreduced system with the reduced aggregate system. It is shown that the proposed aggregation method accurately matches the steady state response and also accurately reproduces the dominant transient response of the unreduced system. As the aggregate DFIG has the same number of state variables as a single DFIG, the overall wind farm's order is reduced significantly to increase the modelling and simulation efficiency.
引用
收藏
页码:935 / 946
页数:12
相关论文
共 41 条
[1]   Model Order Reduction of Wind Farms: Linear Approach [J].
Ali, Husni Rois ;
Kunjumuhammed, Linash P. ;
Pal, Bikash C. ;
Adamczyk, Andrzej G. ;
Vershinin, Konstantin .
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2019, 10 (03) :1194-1205
[2]  
Ali M., 2009, 8 INT WORKSH LARG SC, P14
[3]   Wind Farm Model Aggregation Using Probabilistic Clustering [J].
Ali, Muhammad ;
Ilie, Irinel-Sorin ;
Milanovic, Jovica V. ;
Chicco, Gianfranco .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2013, 28 (01) :309-316
[4]  
[Anonymous], 2011, Back-to-Back Power Electronic Converter, DOI [DOI 10.1002/9781118104965, 10.1002/9781118104965.ch2, DOI 10.1002/9781118104965.CH2]
[5]  
[Anonymous], 2019, GLOB WIND REP ANN MA
[7]   MATRIX REPRESENTATION OF 3-PHASE N-WINDING TRANSFORMERS FOR STEADY-STATE AND TRANSIENT STUDIES [J].
BRANDWAJN, V ;
DOMMEL, HW ;
DOMMEL, II .
IEEE TRANSACTIONS ON POWER APPARATUS AND SYSTEMS, 1982, 101 (06) :1369-1378
[8]   Validation of Single- and Multiple-Machine Equivalents for Modeling Wind Power Plants [J].
Brochu, Jacques ;
Larose, Christian ;
Gagnon, Richard .
IEEE TRANSACTIONS ON ENERGY CONVERSION, 2011, 26 (02) :532-541
[9]   Aggregate modelling of wind farms containing full-converter wind turbine generators with permanent magnet synchronous machines: transient stability studies [J].
Conroy, J. ;
Watson, R. .
IET RENEWABLE POWER GENERATION, 2009, 3 (01) :39-52
[10]  
DELEON F, 1994, IEEE T POWER DELIVER, V9, P231, DOI 10.1109/61.277694