Convex Model Predictive Control for Down-regulation Strategies in Wind Turbines
Gonzalez Silva, J. (TU Delft Team Riccardo Ferrari)
Ferrari, Riccardo M.G. (TU Delft Team Riccardo Ferrari)
van Wingerden, J.W. (TU Delft Team Jan-Willem van Wingerden)
Wind turbine (WT) controllers are often geared towards maximum power extraction, while suitable operating constraints should be guaranteed such that WT components are protected from failures. Control strategies can be also devised to reduce the generated power, for instance to track a power reference provided by the grid operator. They are called down-regulation strategies and allow to balance power generation and grid loads, as well as to provide ancillary grid services, such as frequency regulation. Although this balance is limited by the wind availability and grid demand, the quality of wind energy can be improved by introducing down-regulation strategies that make use of the kinetic energy of the turbine dynamics. This paper shows how the kinetic energy in the rotating components of turbines can be used as an additional degree-of-freedom by different down-regulation strategies. In particular we explore the power tracking problem based on convex model predictive control (MPC) at a single wind turbine. The use of MPC allows us to introduce a further constraint that guarantees flow stability and avoids stall conditions. Simulation results are used to illustrate the performance of the developed down-regulation strategies. Notably, by maximizing rotor speeds, and thus kinetic energy, the turbine can still temporarily guarantee tracking of a given power reference even when occasional saturation of the available wind power occurs. In the study case we proved that our approach can guarantee power tracking in saturated conditions for 10 times longer than with traditional down-regulation strategies.
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Wind power generation
Proceedings of the IEEE 61st Conference on Decision and Control (CDC 2022)
IEEE 61st Conference on Decision and Control (CDC 2022), 2022-12-06 → 2022-12-09, Cancún, Mexico
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© 2022 J. Gonzalez Silva, Riccardo M.G. Ferrari, J.W. van Wingerden