Wind Turbine Active Power Control Based on Multi-Model Adaptive Control

This paper proposes a multiple model adaptive control method of wind turbine active power that considers the complexity of the control model, nonlinear and strong coupling. The model is designed to reduce the negative influences of wind turbines in the process of active power control caused by different uncertain factors. We first build the multiple model of turbine operating by using subtracting cluster algorithm， based on data of a 1.5MW doubly-fed inductor generators (DFIGs) in a wind farm in Gansu, China. We use recursive least squares (RLS) algorithm to identify local model parameters. In addition， the controller is designed by adopting online optimal control model which based on a weighted index of output matching switching strategy. The controller is to realize multi-model adaptive control (MMAC). Results show that the proposed method has good control performance. The method can effectively solve the problems of wind turbines nonlinear modeling and active power control in operation.

[1]  Wang Wei,et al.  LOCALIZATION BASED MULTI-MODEL ADAPTIVE CONTROL , 2000 .

[2]  Wu Zhengqiu,et al.  Power Smoothing Control Strategy of Doubly-fed Induction Generator Based on Constraint Factor Extent-limit Control , 2011 .

[3]  Cheng Yong-mei.  Dynamic Track Cluster Algorithm Based on Subtractive Clustering , 2009 .

[4]  Dong-Choon Lee,et al.  Maximum Output Power Tracking Control in Variable-Speed Wind Turbine Systems Considering Rotor Inertial Power , 2013, IEEE Transactions on Industrial Electronics.

[5]  M Soliman,et al.  Multiple Model Predictive Control for Wind Turbines With Doubly Fed Induction Generators , 2011, IEEE Transactions on Sustainable Energy.

[6]  R. W. De Doncker,et al.  Doubly fed induction generator systems for wind turbines , 2002 .

[7]  M. R. I. Sheikh,et al.  Smoothing control of wind farm output fluctuations by using EWMA pitch controller , 2012, 2nd International Conference on the Developments in Renewable Energy Technology (ICDRET 2012).

[8]  Shan Gao,et al.  A review of different methodologies for solving the problem of wind power's fluctuation , 2009, 2009 International Conference on Sustainable Power Generation and Supply.

[9]  Hamidreza Jafarnejadsani,et al.  Adaptive Control of a Variable-Speed Variable-Pitch Wind Turbine Using Radial-Basis Function Neural Network , 2013, IEEE Transactions on Control Systems Technology.

[10]  Zhuang Kai.  Power Smoothing Control Strategy of Direct-driven Permanent Magnet Synchronous Generator for Wind Turbine With Pitch Angle Control and Torque Dynamic Control , 2009 .

[11]  Zeng Bin.  Optimization Algorithm for Wind Farm Active Power Cutback Control , 2011 .

[12]  Ciprian Lupu,et al.  MULTI-MODEL ADAPTIVE CONTROL FOR NONLINEAR SYSTEMS , 2007 .

[13]  Feng Dong-qing,et al.  Design and Research Based on Fuzzy PID-Parameters Self-Tuning Controller with MATLAB , 2008, 2008 International Conference on Advanced Computer Theory and Engineering.

[14]  Lin Li,et al.  Priority List-Based Output-Restricted Active Power Control Strategy for Wind Farms , 2013 .

[15]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[16]  Tomonobu Senjyu,et al.  Output power smoothing of wind turbine generation system for the 2-MW permanent magnet synchronous generators , 2010, 2010 International Conference on Electrical Machines and Systems.

[17]  Yang Xiyun.  A Study on Dynamic Optimal Control Strategy for Large-scale Wind Power Generation System , 2010 .