Health‐aware model predictive control of wind turbines using fatigue prognosis

Wind turbines components are subject to considerable fatigue due to extreme environmental conditions to which are exposed, especially those located offshore. Interest in the integration of control with fatigue load minimization has increased in recent years. The integration of a system health management module with the control provides a mechanism for the wind turbine to operate safely and optimize the trade-off between components life and energy production. The research presented in this paper explores the integration of model predictive control (MPC) with fatigue-based prognosis approach to minimize the damage of wind turbine components (the blades). The controller objective is modified by adding an extra criterion that takes into account the accumulated damage. The scheme is implemented and tested using a high fidelity simulator of a utility scale wind turbine.

[1]  Richard B. Hathaway,et al.  Fatigue testing and analysis , 2005 .

[2]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[3]  Thomas Bak,et al.  Simple model for describing and estimating wind turbine dynamic inflow , 2013, 2013 American Control Conference.

[4]  Susan A. Frost,et al.  Integrating Structural Health Management with Contingency Control for Wind Turbines , 2013 .

[5]  N. Peric,et al.  Individual pitch control of wind turbine based on loads estimation , 2008, 2008 34th Annual Conference of IEEE Industrial Electronics.

[6]  Vicenç Puig,et al.  Fault Diagnosis of Advanced Wind Turbine Benchmark using Interval-based ARRs and Observers , 2014 .

[7]  M. Matsuichi,et al.  Fatigue of metals subjected to varying stress , 1968 .

[8]  Rafael Wisniewski,et al.  On Using Pareto Optimality to Tune a Linear Model Predictive Controller for Wind Turbines , 2016 .

[9]  Iulian Munteanu Wind turbine control systems. Principles, modelling and gain scheduling design. Fernando D. Bianchi, Hernán De Battista and Ricardo J. Mantz, Springer, London, 2006. No. of pages: XIX + 207. Price: $119 , 2008 .

[10]  Kathryn E. Johnson,et al.  Wind turbine fault detection and fault tolerant control - An enhanced benchmark challenge , 2013, 2013 American Control Conference.

[11]  Adam Niesłony,et al.  Determination of fragments of multiaxial service loading strongly influencing the fatigue of machine components , 2009 .

[12]  Rafael Wisniewski,et al.  Gain-scheduled model predictive control of wind turbines using Laguerre functions , 2013, 2013 American Control Conference.

[13]  Fernando D. Bianchi,et al.  Wind Turbine Control Systems: Principles, Modelling and Gain Scheduling Design , 2006 .

[14]  Peter Fogh Odgaard,et al.  Optimized Control Strategy For Over Loaded Offshore Wind Turbines , 2015 .

[15]  Jwo Pan,et al.  Fatigue Testing and Analysis: Theory and Practice , 2004 .

[16]  David Schlipf,et al.  Nonlinear model predictive control of wind turbines using LIDAR , 2013 .

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

[18]  Ervin Bossanyi,et al.  Wind Energy Handbook , 2001 .

[19]  Tobias Gybel Hovgaard,et al.  On Practical tuning of Model Uncertainty in Wind Turbine Model Predictive Control , 2015 .

[20]  Herbert J. Sutherland,et al.  On the Fatigue Analysis of Wind Turbines , 1999 .

[21]  Basil Kouvaritakis,et al.  Robust MPC Tower Damping for Variable Speed Wind Turbines , 2015, IEEE Transactions on Control Systems Technology.

[22]  Rafael Wisniewski,et al.  Fatigue damage estimation and data-based control for wind turbines , 2015 .

[23]  Stephen P. Boyd,et al.  Load reduction of wind turbines using receding horizon control , 2011, 2011 IEEE International Conference on Control Applications (CCA).

[24]  Simon J. Watson,et al.  Physics of Failure approach to wind turbine condition based maintenance , 2009 .

[25]  E. W. C. Wilkins,et al.  Cumulative damage in fatigue , 1956 .

[26]  Y. Wang,et al.  Nonlinear Model Predictive control (NMPC) of fixed pitch variable speed wind turbine , 2008, 2008 IEEE International Conference on Sustainable Energy Technologies.

[27]  Mahera Musallam,et al.  An Efficient Implementation of the Rainflow Counting Algorithm for Life Consumption Estimation , 2012, IEEE Transactions on Reliability.

[28]  Peter Fogh Odgaard,et al.  Fault-Tolerant Control of Wind Turbines: A Benchmark Model , 2009, IEEE Transactions on Control Systems Technology.

[29]  Niels Kjølstad Poulsen,et al.  A fatigue approach to wind turbine control , 2006 .

[30]  Jason Jonkman,et al.  FAST User's Guide , 2005 .

[31]  Anastasios P. Vassilopoulos,et al.  Fatigue life prediction of wind turbine blade composite materials , 2013 .

[32]  Takashi Yoneyama,et al.  Model Predictive Control using Prognosis and Health Monitoring of actuators , 2010, 2010 IEEE International Symposium on Industrial Electronics.

[33]  Torben Knudsen,et al.  Importance of Dynamic Inflow in Model Predictive Control of Wind Turbines , 2015 .

[34]  Stefan Ivanell,et al.  Analysis of the effect of curtailment on power and fatigue loads of two aligned wind turbines using an actuator disc approach , 2014 .

[35]  Darrell F. Socie,et al.  Simple rainflow counting algorithms , 1982 .

[36]  J. A. Rossiter,et al.  A review on applications of model predictive control to wind turbines , 2014, 2014 UKACC International Conference on Control (CONTROL).

[37]  Tobias Gybel Hovgaard,et al.  Selection of References in Wind Turbine Model Predictive Control Design , 2015 .

[38]  Nikola Hure Model Predictive Control of a Wind Turbine , 2012 .

[39]  J. C. Marín,et al.  Study of damage and repair of blades of a 300 kW wind turbine , 2008 .