Lidars and wind turbine control. Pt. 1

Abstract

In recent years lidar technology found its way into wind energy. The main application is still the site assessment, but the possibility to optimize the energy production and reduce the loads by nacelle or spinner based lidar systems is becoming an important issue. In terms of control the inflowing wind field is the main disturbance to the wind turbine and most of the wind turbine control is designed to deal with variations in this disturbance. From control theory, the control performance can be improved with the knowledge of the disturbance. Due to the measurement principle and the complexity of the wind lidar assisted control is a wide field of research. The main idea is to divide the problem in a measurement and a control problem. The presented work describes first how wind characteristics, such as wind speed, direction and shears, can be reconstructed from the limited provided information (see Section 9.2). Based on the models of the wind turbines (see Section 9.3) it is investigated in Section 9.4, how well the lidar information can be correlated to the turbines reaction. In the next sections, several controllers are presented, see Table 15. All controllers are designed first for the case of perfect measurement and then adjusted for realistic measurements. The most promising approach is the collective pitch feedforward controller using the knowledge of the incoming wind speed providing an additional control update to assist common collective pitch control. Additional load reduction compared to the sophisticated feedback controllers could be archived (Schlipf et al., 2010a). The concept has been successfully tested on two research wind turbines (Schlipf et al., 2012a; Scholbrock et al., 2013). Then a feedforward control strategy to increase the energy production by tracking optimal inflow conditions is presented. The comparison to existing indirect speed control strategies shows a marginal increase in energy output at the expense of raised fluctuations of the generator torque (Schlipf et al., 2011). A Nonlinear Model Predictive Control (NMPC) is also presented, which predicts and optimizes the future behavior of a wind turbine using the wind speed preview adjusting simultaneously the pitch angle and the generator torque. The NMPC achieves further load reductions especially for wind conditions near rated wind speed (Schlipf et al., 2012b). Furthermore, a cyclic pitch feedforward controller using the measured horizontal and vertical shear is introduced to assist common cyclic pitch control for further reduction of blade loads. Simulations results from Dunne et al. (2012) are promising, but they have to be further investigated under more realistic conditions. Finally, the benefit of lidar assisted yaw control is explored. A promising way to obtain a accurate measurement of the wind direction is to measure it over the full rotor plane ahead of the turbine by lidar. The expected increase of the energy output is about one percent of the annual energy production, when using the wind direction signal from the lidar system instead of the sonic anemometer (Schlipf et al., 2011).