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电网技术  2013 

风电机组驱动系统参数辨识

, PP. 1990-1994

Keywords: 定速风电机组,驱动系统,参数辨识,粒子群优化算法

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Abstract:

提出了风速激励下的定速风电机组驱动系统参数辨识方法。首先以含定速风电机组的单机无穷大系统为例,仿真了风速激励风电机组的受扰轨线,表明由于感应发电机电气部分动态较快,因此在辨识驱动系统模型参数时,可将电气部分用准稳态模型近似,以降低模型的阶数以及参数辨识的个数。进一步采用轨迹灵敏度方法,分析了驱动系统模型参数的可辨识性。基于粒子群优化算法进行了参数辨识,结果验证了该定速风电机组驱动系统参数辨识方法的可行性。

References

[1]  Asmine M,Brochu J,Fortmann J,et al.Model validation for wind turbine generator models[J].IEEE Trans on Power Systems,2011,26(3):1769-1782.
[2]  国家电网公司,风电并网运行控制技术规定[R].北京:国家电网公司,2009.
[3]  Trilla L,Gomis-Bellmunt O,Junyent-Ferré A,et al.Modeling and validation of DFIG 3-MW wind turbine using field test data of balanced and unbalanced voltage sags[J].IEEE Trans on Power Systems,2011,2(4):509-519.
[4]  金宇清,赵泽,鞠平,等.双馈感应风力发电机的参数辨识分析[J].高电压技术,2011,37(7):1700-1705.
[5]  Brochu J,Larose C,Gagnon R.Validation of single-and multiple-machine equivalents for modeling wind power plants[J].IEEE Trans Energy Conversion,2010,26(2):532-541.
[6]  Novak P,Jovik I,Schmidtbauer B.Modeling and identification of drive-system dynamics in a variable-speed wind turbine[C]//Proceedings of the Third IEEE Conference on Control Application.Glasgow Control Application,Glasgow,UK:IEEE,1994:233-238.
[7]  鞠平.电力系统建模理论与方法[M].北京:科学出版社,2010:215-300.
[8]  陈其云,孙才新,周湶,等.粗糙集信息熵与自适应神经网络模糊系统相结合的电力短期负荷预测模型及方法[J].电网技术,2004,28(17):72-75.
[9]  周湶,李健,孙才新,等.基于粗糙集和元胞自动机的配电网空间负荷预测[J].中国电机工程学报,2008,28(25):68-73.
[10]  张文彬,赵强,周萌,等.基于粗糙集神经网络和元胞自动机的空间负荷预测[J].现代电力,2012,29(2):32-36.
[11]  黎静华,栗然,牛东晓.基于粗糙集的默认规则挖掘算法在电力系统短期负荷预测中的应用[J].电网技术,2006,30(5):18-23.
[12]  Langlois C E,Lefebvre D,Dubé L,et al.Developing a type-III wind turbine model for stability studies of the Hydro-Quebec network[C]//Proceedings of 8th International Workshop Large-Scale Integration of Wind Power into Power Systems.Bremen,Germany:Energynautics,2009:674-679.
[13]  Seman S,Niiranen J,Virtanen R,et al.Low voltage ride-through analysis of 2MW DFIG wind turbine-grid code compliance validations[C]//IEEE PES General Meeting.Pittsburgh,USA:IEEE,2008:1-6.
[14]  Miller N,Clark K,MacDowell J,et al.Experience with field and factory testing for model validation of GE wind plants[C]//Proceedings of European Wind Energy Conference and Exhibition.Brussels,Belgium:EWEA,2008:1-9.
[15]  张仰飞,袁越,陈小虎,等.风力机参数的可辨识分析[J].电力系统自动化,2009,33(6):86-88.
[16]  Son G T,Lee H J,Park J W.Estimation of wind turbine rotor power coefficient using RMP model[C]//IEEE Industry Applications Society Annual Meeting.Texas,USA:IEEE,2009:1-8.
[17]  Mok K.Identification of the power coefficient of wind turbines[C]//Proceedings of IEEE PES General Meeting.California,USA:IEEE,2005(2):2078-2082.
[18]  Kélouwani S,Agbossou K.Nonlinear model identification of wind turbine with a neural network[J].IEEE Trans on Energy Conversion,2004,19(3):607-612.
[19]  Monroy A,Alvarez-Icaza L.Real-time identification of wind turbine rotor power coefficient[C]//Proceedings of the 45th IEEE Conference on Decision & Control.San Diego,USA:IEEE,2006:3690-3695.
[20]  Littler T,Fox B,Flynn D.Measurement-based estimation of wind farm inertia[J].IEEE Power Technology Conference.Petersburg,Russia:IEEE,2005:1-5.
[21]  Kayikci M,Milanovic J V.Dynamic contribution of DFIG-based wind plants to system frequency disturbances[J].IEEE Trans on Power Systems,2009,24(2):859-867.
[22]  Kennedy J M,Fox B,Littler T,et al.Validation of fixed speed induction generator models for inertial response using wind farm measurements[J].IEEE Trans on Power Systems,2011,26(3):1454-1461.
[23]  Pedersen J K,Helgelsen-Pedersen K O,Kjølstad Poulsen N,et al.Contribution to a dynamic wind turbine model validation from a wind farm islanding experiment[J].Electric Power Systems Research,2003(64):41-51.

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