Nonlinear Maximum Power Point Tracking Control Method for Wind Turbines Considering Dynamics

A combined strategy of torque error feed-forward control and blade-pitch angle servo control is proposed to improve the dynamic power capture for wind turbine maximum power point tracking (MPPT). Aerodynamic torque is estimated using the unscented Kalman filter (UKF). Wind speed and tip speed ratio (TSR) are estimated using the Newton–Raphson method. The error between the estimated aerodynamic torque and the steady optimal torque is used as the feed-forward signal to control the generator torque. The gain parameters in the feed-forward path are nonlinearly regulated by the estimated generator speed. The estimated TSR is used as the reference signal for the optimal blade-pitch angle regulation at non-optimal TSR working points, which can improve the wind power capture for a wider non-optimal TSR range. The Fatigue, Aerodynamics, Structures, and Turbulence (FAST) code is used to simulate the aerodynamics and mechanical aspects of wind turbines while MATLAB/SIMULINK is used to simulate the doubly-fed induction generator (DFIG) system. The example of a 5 MW wind turbine model reveals that the new method is able to improve the dynamic response of wind turbine MPPT and wind power capture.

[1]  N. Ertugrul,et al.  Effects of inertia on dynamic performance of wind turbines , 2008, 2008 Australasian Universities Power Engineering Conference.

[2]  R Vepa,et al.  Nonlinear, Optimal Control of a Wind Turbine Generator , 2011, IEEE Transactions on Energy Conversion.

[3]  Fangxing Li,et al.  Maximum Power Point Tracking Strategy for Large-Scale Wind Generation Systems Considering Wind Turbine Dynamics , 2015, IEEE Transactions on Industrial Electronics.

[4]  A. O. Troickiy,et al.  Performance Assessment of Perturbation and Observation Algorithm for Wind Turbine , 2018, 2018 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM).

[5]  J. Jonkman,et al.  Definition of a 5-MW Reference Wind Turbine for Offshore System Development , 2009 .

[6]  Yun Zou,et al.  Optimal torque control based on effective tracking range for maximum power point tracking of wind turbines under varying wind conditions , 2017 .

[7]  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.

[8]  R. Moses,et al.  Taylor expansion of the differential range for monostatic SAR , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[9]  Yun Zou,et al.  A Multi-Point Method Considering the Maximum Power Point Tracking Dynamic Process for Aerodynamic Optimization of Variable-Speed Wind Turbine Blades , 2016 .

[10]  Muyang Liu,et al.  A Novel Approach to Model a Gas Network , 2019, Applied Sciences.

[11]  Chee Wei Tan,et al.  A review of maximum power point tracking algorithms for wind energy systems , 2012 .

[12]  Kathryn E. Johnson,et al.  METHODS FOR INCREASING REGION 2 POWER CAPTURE ON A VARIABLE SPEED HAWT , 2004 .

[13]  Nesimi Ertugrul,et al.  Dynamic wind turbine output power reduction under varying wind speed conditions due to inertia , 2013 .

[14]  Jan Pierik,et al.  Inertial response of variable speed wind turbines , 2006 .

[15]  Tomonobu Senjyu,et al.  Control of a Stand-Alone Variable Speed Wind Energy Supply System † , 2013 .

[16]  Reza Kazemi Golkhandan,et al.  Control Strategies for Enhancing Frequency Stability by DFIGs in a Power System with High Percentage of Wind Power Penetration , 2017 .

[17]  Yun Zou,et al.  Inverse Aerodynamic Optimization Considering Impacts of Design Tip Speed Ratio for Variable-Speed Wind Turbines , 2016 .

[18]  N. Hosseinzadeh,et al.  Effects of wind speed variations and machine inertia constants on variable speed wind turbine dynamics , 2010, 2010 20th Australasian Universities Power Engineering Conference.