Optimising the control of wind generators by means of intelligent microsensorsThe School of Engineering at Bayonne (ESTIA) is working on a research project on control optimisation for the latest-generation wind generators using intelligent microsensors.
The latest-generation wind generators work at variable speed and with pitch regulation based on the pitch angle of the rotor blades. These degrees of freedom (the rotation speed and the pitch angle of each blade) enable an increase in energy yield, a decrease in fatigue due to mechanical loads and an enhancement in the quality of the electrical potential with respect to fixed-speed wind turbines. The rotation speed and the pitch angle of the blades are controlled continuously by control algorithms and the quality of these algorithms have a determining influence on the price of the energy produced by the wind generators. Although a large amount of research work on wind generator control has been undertaken, it still remains for more "intelligence" to be introduced into their functioning.
The reduction in the price of wind energy is made possible through:
- increasing the reliability and robustness of wind generators
- increasing the energy yield
- manufacturing lighter wind generators.
This weight reduction can be achieved only by reducing the mechanical forces on the blades, the transmission axle and the tower. Moreover, the current trend in increasing the size of wind generators means having mechanically more flexible machines. The control has to take into account this flexibility, especially avoiding the resonance frequencies of the various mechanical elements.
Thus, the classical criteria linked to the optimisation of control algorithms for variable-speed and pitch-regulated wind generators are:
- the quality of the electrical potential produced
- the reduction in the dynamic loads to which the wind generator structure is subjected
- the energy yield
- the robustness of the control algorithms developed.
Within the framework of this question, the ESTIA School of Engineering is particularly focusing on criteria dealing with the reduction of the mechanical stresses arising from fatigue in the elements of a wind generator (tower, blades, transmission axle/gearbox).
Fatigue in a wind generator's elements is mainly linked with the external dynamic forces that these parts undergo and with the fluctuations in their resonance frequencies. Control of the wind generator should enable an overall reduction in these stresses in order to increase reliability and, thereby, the usefulness of wind generators, and enable a reduction in the overall weight of their components. The main loads are those withstood by the blades, the tower and the transmission axle/gearbox. The design of the control algorithms has to take into account this reduction in the mechanical stresses in these components. Several degrees of freedom exist in order to achieve this target: the two of the electric motor, the pitch angle for each blade (three for the classical wind generator) and the orientation (the "yaw") of the whole turbine with respect to the tower axis. This last parameter is not within the remit of this current research project.
The design of this control should take into account not just one of the four criteria previously mentioned. Although the main objective here is the reduction of dynamic stresses experienced by the wind generator, the other criteria are not forgotten.
Intelligent microsensors (acceleration sensors with wireless communication) located at the blade tips, above the surface of the wind generator axis and in the tower will be used to optimise the control.
Thanks to these acceleration sensors, the control system will have supplementary information in real time, thus enabling reduction in fatigue stresses using multivariable optimisation algorithms.
Commercial wind generators are fitted with a number of sensors (voltage, current, rotation velocity, etc.). Nevertheless, they are currently not fitted with acceleration or with stress sensors.
Last reviewed: By John M. Grohol, Psy.D. on 30 Apr 2016
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