Load Reduction for Wind Turbines: an Output Constrained, Subspace Predictive Repetitive Control Approach

Abstract. Individual Pitch Control (IPC) is a well-known approach to reduce blade loads on wind turbines. Although very effective, IPC usually requires high levels of actuator activities, which significantly increases the pitch Actuator Duty Cycle (ADC). This will subsequently result in an increase of the wear on the bearings of the blades and make the current IPC design not economical viable. An alternative approach to this issue is to reduce the actuator activities by incorporating the output constraints in IPC. In this paper, a fully data driven IPC approach, which is called constrained Subspace Predictive Repetitive Control (cSPRC) is introduced. The output constraints can be explicitly considered in the control problem formulation via a Model Predictive Control (MPC) approach. The cSPRC approach will actively produce the IPC action for the necessary load reduction when the blade loads violate the output constraints. In this way, actuator activities can be significantly reduced. Two kinds of scenarios are simulated to illustrate the unique applications of the proposed method: wake-rotor overlap and turbulent wind conditions. Simulation results show that the developed cSPRC is able to account for the output constraints into the control problem formulation. Since the IPC action from cSPRC is only triggered to prevent violating the output constraints, the actuator activities are significantly reduced. This will help to reduce the pitch ADC, thus leading to an economical viable load control strategy. In addition, this approach allows the wind farm operator to design conservative bounds to guarantee the safety of the wind turbine control system.

[1]  Liuping Wang,et al.  Model Predictive Control System Design and Implementation Using MATLAB , 2009 .

[2]  Michel Verhaegen,et al.  Closed-loop subspace identification methods: an overview , 2013 .

[3]  Jan-Willem van Wingerden,et al.  Analysis and optimal individual pitch control decoupling by inclusion of an azimuth offset in the multi-blade coordinate transformation , 2018, Wind Energy.

[4]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[5]  Mate Jelavić,et al.  MPC framework for constrained wind turbine individual pitch control , 2019, Wind Energy.

[6]  Michel Verhaegen,et al.  Wind turbine load reduction by rejecting the periodic load disturbances , 2013 .

[7]  Yuan Yuan,et al.  Multivariable robust blade pitch control design to reject periodic loads on wind turbines , 2020 .

[8]  Filippo Campagnolo,et al.  Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments , 2019 .

[9]  M. Rotea,et al.  Wind energy research: State-of-the-art and future research directions , 2018, Renewable Energy.

[10]  Yaoyu Li,et al.  Individual pitch control for wind turbine load reduction including wake modeling , 2010 .

[11]  Torben J. Larsen,et al.  Wake meandering: a pragmatic approach , 2008 .

[12]  Maureen Hand,et al.  Multivariable control strategy for variable speed, variable pitch wind turbines , 2007 .

[13]  Jan-Willem van Wingerden,et al.  Fast Adaptive Fault Accommodation in Floating Offshore Wind Turbines via Model-Based Fault Diagnosis and Subspace Predictive Repetitive Control , 2020, IFAC-PapersOnLine.

[14]  Jan-Willem van Wingerden,et al.  Experimental wind tunnel testing of linear individual pitch control for two-bladed wind turbines , 2014 .

[15]  Ervin Bossanyi,et al.  Further load reductions with individual pitch control , 2005 .

[16]  Alessandro Chiuso,et al.  The role of vector autoregressive modeling in predictor-based subspace identification , 2007, Autom..

[17]  Jan-Willem van Wingerden,et al.  Feedback-feedforward individual pitch control design for wind turbines with uncertain measurements , 2019, 2019 American Control Conference (ACC).

[18]  Kathryn E. Johnson,et al.  Simulation comparison of wake mitigation control strategies for a two‐turbine case , 2015 .

[19]  G. Bir Multi-Blade Coordinate Transformation and its Application to Wind Turbine Analysis , 2008 .

[20]  Tae Tom Oomen,et al.  Subspace predictive repetitive control to mitigate periodic loads on large scale wind turbines , 2014 .

[21]  Morten Nielsen,et al.  Modelling and measurements of power losses and turbulence intensity in wind turbine wakes at Middelgrunden offshore wind farm , 2007 .

[22]  Michel Verhaegen,et al.  Feedback–feedforward individual pitch control for wind turbine load reduction , 2009 .

[23]  Lorenzo Fagiano,et al.  Future emerging technologies in the wind power sector: A European perspective , 2019, Renewable and Sustainable Energy Reviews.

[24]  Michel Verhaegen,et al.  Two-Degree-of-Freedom Active Vibration Control of a Prototyped “Smart” Rotor , 2011, IEEE Transactions on Control Systems Technology.

[25]  Jan-Willem van Wingerden,et al.  Data-driven repetitive control: Wind tunnel experiments under turbulent conditions , 2018, Control Engineering Practice.

[26]  Yaoyu Li,et al.  Multi-model predictive control for wind turbine operation under meandering wake of upstream turbines , 2015 .

[27]  Alberto Bemporad,et al.  The explicit linear quadratic regulator for constrained systems , 2003, Autom..

[28]  Dirk Söffker,et al.  State-of-the-art in wind turbine control: Trends and challenges , 2016 .

[29]  Martin Kühn,et al.  Optimal multivariable individual pitch control for load reduction of large wind turbines , 2016, 2016 American Control Conference (ACC).

[30]  Paul Fleming,et al.  Incorporating Atmospheric Stability Effects into the FLORIS Engineering Model of Wakes in Wind Farms , 2016 .

[31]  Ervin Bossanyi,et al.  Individual Blade Pitch Control for Load Reduction , 2003 .

[32]  Jan-Willem van Wingerden,et al.  Wind Tunnel Testing of Subspace Predictive Repetitive Control for Variable Pitch Wind Turbines , 2015, IEEE Transactions on Control Systems Technology.

[33]  Carlo L. Bottasso,et al.  Optimization‐based study of bend–twist coupled rotor blades for passive and integrated passive/active load alleviation , 2012 .