Control of Wind Turbines and Wind Farms

Control technology holds much promise for improving the way wind turbines and wind farms are operated, and may contribute significantly to reducing the cost of energy from wind. In fact, as sensors become cheaper and more capable, digital controls can make existing and future assets “smarter”, optimizing the way turbine and farms respond to complex inputs and behave in challenging operational scenarios. These are some of the most interesting and pressing scientific questions we are working on:

  • How can we improve the way wind turbines are controlled, to increase power capture and/or decrease loading to extend life?
  • Can we move away from the greedy control approach used today on board wind turbines, where each turbine is operated individually with little or no consideration of what neighboring machines are doing?
  • What can be gained by using cooperative control strategies of wind turbines within a farm? By the use of cooperative control, can we mitigate wake losses or reduces loading? Does the use of smart cooperative control lead to new ways of designing future wind farms? And, by cooperative control, can we also improve the way existing wind farms operate today?
  • Can we operate wind farms more similarly to what is done for other conventional energy sources, and can this help in the integration of a higher share of wind in the energy grid?
  • What knowledge on the wind and the system response is necessary to enable smart control approaches for turbines and farms? And what sensors can provide such information at a low cost, high availability and moderate complexity?

Related Projects

  • EU H2020 project ‘CL-WINDCON – Closed Loop Wind Farm Control’
  • BMWi project CompactWind ‘Erhöhung des Flächenenergieertrags in Windparks durch avancierte Anlagen- und Parkregelung’
  • Industrial project ‘Wind Farm Control’
  • Industrial project ‘Development and Testing of Scaled Offshore Wind Turbine Models’
  • Industrial Ph.D. ‘LiDAR-Assisted Control of Wind Turbines’ • One PhD position (Chinese Scholarship Council)

Selected References:

  • C.L. Bottasso, C.E.D. Riboldi, `Estimation of Wind Misalignment and Vertical Shear from Blade Loads', Renewable Energy, 62:293-302, doi:10.1016/j.renene.2013.07.021, 2014.
  • C.L. Bottasso, C.E.D. Riboldi, `Validation of a Wind Misalignment Observer using Field Test Data', Renewable Energy, under review, 2013.
  • C.L. Bottasso, P. Pizzinelli, C.E.D. Riboldi, `LiDAR-Enabled Model Predictive Control of Wind Turbines with Real-Time Capabilities', Renewable Energy, under review, 2013.
  • C.L. Bottasso, A. Croce, C.E.D. Riboldi, Y. Nam, `Multi-Layer Control Architecture for the Reduction of Deterministic and Non-Deterministic Loads on Wind Turbines', Renewable Energy, 51:159-169, 2013.
  • C.L. Bottasso, A. Croce, C.E.D. Riboldi, Y. Nam, `Power Curve Tracking in the Presence of a Tip Speed Constraint', Renewable Energy, 40:1-12, doi:10.1016/j.renene.2011.07.045, 2012.