No abstract

This article demonstrates a strategy to design multivariable and multi-objective controllers based on the H ∞ norm reduction applied to a wind turbine. The wind turbine model has been developed in the GH Bladed software and it is based on a 5 MW wind turbine defined in the Upwind European project. The designed control strategy works in the above rated power production zone and performs generator speed control and load reduction on the drive train and tower. In order to do this, two robust H ∞ MISO (Multi-Input Single-Output) controllers have been developed. These controllers generate collective pitch angle and generator torque set-point values to achieve the imposed control objectives. Linear models obtained in GH Bladed 4.0 are used, but the control design methodology can be used with linear models obtained from any other modelling package. Controllers are designed by setting out a mixed sensitivity problem, where some notch filters are also included in the controller dynamics. The obtained H  controllers have been validated in GH Bladed and an exhaustive analysis has been carried out to calculate fatigue load reduction on wind turbine components, as well as to analyze load mitigation in some extreme cases. The analysis compares the proposed control strategy based on H  controllers to a baseline control strategy designed using the classical control methods implemented on the present wind turbines.

No abstract

No abstract

The non-linear behaviour of wind turbines demands control strategies that guarantee the robustness of the closedloop system. Linear parameter-varying (LPV) controllers adapt their dynamics to the system operating points, and the robustness of the closed loop is guaranteed in the controller design process. An LPV collective pitch controller has been developed within this work to regulate the generator speed in the above rated power production control zone. The performance of this LPV controller has been compared with two baseline control strategies previously designed, on the basis of classical gain scheduling methods and linear time-invariant robust H ∞ controllers. The synthesis of the LPV controller is based on the solution of a linear matrix inequalities system, proposed in a mixed-sensitivity control scenario where not only weight functions are used but also an LPV model of the wind turbine is necessary. As a contribution, the LPV model used is derived from a family of linear models extracted from the linearization process of the wind turbine non-linear model. The offshore wind turbine of 5 MW defined in the Upwind European project is the used reference non-linear model, and it has been modelled using the GH Bladed 4.0 software package. The designed LPV controller has been validated in GH Bladed, and an exhaustive analysis has been carried out to calculate fatigue load reductions on wind turbine components, as well as to analyse the load mitigation in some extreme cases.

No abstract

No abstract

The design of a wind turbine implies the simulation of definite conditions as specified in the standards. Among those operational conditions, rare events such as extreme gusts or external faults are included, which may cause high structural loads. Such extreme design load cases usually drive the design of some of the main components of the wind turbine: tower, blades and mainframe. Two different strategies are hence presented to mitigate the loads, deriving from extreme load cases, on the basis of the detection of wind gusts by means of ad hoc synthesized artificial neural networks. This tool is embedded into the main control algorithm and allows it to detect the gust in advance, to anticipate the control reaction, and by doing so reducing extreme loads. One of the strategies performs a controlled stop when wind gust is detected. The other rides through wind gusts without stopping, i.e., without affecting the wind turbine normal operation. Aeroelastic simulations of the Alstom Wind's wind turbines using these techniques have shown significant reductions in the extreme loads for all standard IEC 61400-1, edition 2 DLC 1.6 cases. In particular, the overall ultimate loads are largely reduced for blade root and tower base bending moments, with a direct impact on the structural design of those components.