Wednesday, 28 September 2016
14:30 - 16:00 Advanced control strategies for wind plants
Turbine technology  
          

Modern control design methods like non-linear model predictive control can take account of complex dynamics and actuator constraints, while making use of richer information input from advanced sensors such as LIDARs. They contribute to the coordinated control of entire wind farms while meeting new operational demands from the electricity system. The session addresses advanced state estimation methods, an important component of such controllers. You will hear about field test results from a controller using wind preview from a LIDAR sensor as well as changes in design and certification guidelines needed to account for the use of LIDARs.

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Learning objectives

• Advanced non-linear state estimation techniques for wind turbines that run in real time;
• Non-linear model predictive control that rung in real time and improve performances;
• A LIDAR-assisted flatness-based controller validated in field tests;
• Requirements for design and certification of wind turbines with LIDAR assisted control;
• A wind farm control framework that can mitigate wake effects and provide grid balancing services.

Co-chair(s):

Abstract ID: 57
Bastian Ritter
Control engineer & PhD candidate, Industrial Science GmbH, Germany
Making nonlinear state estimation techniques ready for use in industrial wind turbine control systems

Abstract ID: 132
Nikolai Hille
Senior Expert , DNV GL Energy, Germany
Guidance for design and certification of wind turbines with LIDAR-assisted control

Abstract ID: 282
Jonas Kazda
Doctoral Researcher, Technical University of Denmark, Denmark
Framework of multi-objective wind farm controller applicable to real wind farms

Abstract ID: 317
Axel Schild
Automation Solutions Energy Sector, IAV GmbH, Germany
High-performance non-linear model predictive control for wind turbines

Abstract ID: 485 science & research
David Schlipf
Research Group Leader, Stuttgart Wind Energy (SWE), Germany
Field testing of flatness-based feedforward control on the CART2