On turbulence models and lidar measurements for wind turbine control

To provide comprehensive information that will assist in making decisions regarding the adoption of lidar-assisted control (LAC) in wind turbine design, this paper investigates the impact of different turbulence models on the coherence between the rotor-effective wind speed and lidar measurement. First, the differences between the Kaimal and Mann models are discussed, including the power spectrum and spatial coherence. Next, two types of lidar systems are examined to analyze the lidar measurement coherence based on commercially available lidar scan patterns. Finally, numerical simulations have been performed to compare the lidar measurement coherence for different rotor sizes. This work confirms the association between the measurement coherence and the turbulence model. The results indicate that the lidar measurement coherence with the Mann turbulence model is lower than that with the Kaimal turbulence model. In other words, the potential value creation of LAC based on simulations during the wind turbine design phase, evaluated using the Kaimal turbulence model, will be diminished if the Mann turbulence model is used instead. In particular, the difference in coherence is more significant for larger rotors. As a result, this paper suggests that the impacts of different turbulence models should be considered uncertainties while evaluating the benefits of LAC.

Electrohydraulic proportional valve-controlled vane type semi-rotary actuated wind turbine control by feedforward fractional-order feedback controller

In this study, proportional valve-controlled semi-rotary electrohydraulic actuator proposed for horizontal axis wind turbine pitch movement. Semi-rotary actuator can be connected directly to the wind turbine blade, which reduces mechanical complexity compare to linear electrohydraulic actuator system. Adaptive torque control scheme has been adopted for the horizontal axis wind turbine in region II; however, adaptive pitch control has been adopted for region III. Optimum pitch demand and torque demand have been estimated through blade element momentum theory based on predicted wind speed. The control objective is to track maximum power through torque control in region II and to maintain rated power with structural safety by limiting thrust force in the region III. The proposed wind turbine model has been validated with 1.5-MW wind turbine experimental data. Feedforward fractional-order proportional–integral–derivative controller with adaptive teaching–learning based optimization algorithm has been developed for wind turbine control application. In region II, feedforward control signal generates due to torque demand and feedback control signal generates due to combined torque and pitch error. However, in region III, feedforward and feedback control estimated with pitch demand and combined pitch and torque error, respectively. The proposed controller performance has been tested with sinusoidal, step and actual wind data. The controller performance also compared with respect to other conventional controllers. Performance of the adaptive teaching–learning-based optimization has been compared with genetic algorithm and teaching–learning-based optimization process. Sensitivity analysis has been performed with proposed controller to check the effectiveness of the optimization. Furthermore, the proposed controller response has been compared with existing data of 1.5-MW wind turbine. Lyapunov-based stability analysis has been performed to ensure stability and convergence of the proposed system. Proposed controller performance has been found better compare to the existing result.

Neural Network-Based Cost-Effective Estimation of Useful Variables to Improve Wind Turbine Control

The estimation of variables that are normally not measured or are unmeasurable could improve control and condition monitoring of wind turbines. A cost-effective estimation method that exploits machine learning is introduced in this paper. The proposed method allows a potentially expensive sensor, for example, a LiDAR sensor, to be shared between multiple turbines in a cluster. One turbine in a cluster is equipped with a sensor and the remaining turbines are equipped with a nonlinear estimator that acts as a sensor, which significantly reduces the cost of sensors. The turbine with a sensor is used to train the estimator, which is based on an artificial neural network. The proposed method could be used to train the estimator to estimate various different variables; however, this study focuses on wind speed and aerodynamic torque. A new controller is also introduced that uses aerodynamic torque estimated by the neural network-based estimator and is compared with the original controller, which uses aerodynamic torque estimated by a conventional aerodynamic torque estimator, demonstrating improved results.

On Turbulence Models and LiDAR Measurements for Wind Turbine Control

To provide comprehensive information that will assist in making decisions regarding the adoption of LiDAR assisted control (LAC) in wind turbine design, this paper investigates the impact of different turbulence models on the coherence between the rotor effective wind speed and LiDAR measurement. First, the differences between the Kaimal and Mann models are discussed, including the power spectrum and spatial coherence. Next, two types of LiDAR systems are examined to analyze the LiDAR measurement coherence based on commercially available LiDAR scan patterns. Finally, numerical simulations have been performed to compare the LiDAR measurement coherence for different rotor sizes. This work confirms the association between the measurement coherence and the turbulence model. The results indicate that the LiDAR measurement coherence with the Mann turbulence model is lower than that with the Kaimal turbulence model. In other words, the value creation of LAC, evaluated using the Kaimal turbulence model, will be diminished if the Mann turbulence model is used instead. In particular, the difference in coherence is more significant for larger rotors. As a result, this paper suggests that the impacts of different turbulence models should be considered as uncertainties while evaluating the benefits of LAC.