Alan D. Wright

Control of wind turbines: Past, present, and future

We review the objectives and techniques used in the control of horizontal axis wind turbines at the individual turbine level, where controls are applied to the turbine blade pitch and generator. The turbine system is modeled as a flexible structure operating in the presence of turbulent wind disturbances. Some overview of the various stages of turbine operation and control strategies used to maximize energy capture in below rated wind speeds is given, but emphasis is on control to alleviate loads when the turbine is operating at maximum power. After reviewing basic turbine control objectives, we provide an overview of the common basic linear control approaches and then describe more advanced control architectures and why they may provide significant advantages.

FX-RLS-Based Feedforward Control for LIDAR-Enabled Wind Turbine Load Mitigation

An adaptive feedforward controller based on a filtered-x recursive least square (FX-RLS) algorithm and a non-adaptive feedforward controller based on a zero-phase-error tracking control (ZPETC) technique have been designed to augment a collective pitch proportional-integral (PI) feedback controller for wind turbine rotor speed regulation and component load reduction when the wind turbine is operating above rated wind speed. The inputs to the adaptive feedforward controller include measurements of the rotor speed error and the incoming wind speed, where wind speed would be provided by a commercial light detection and ranging (LIDAR) system. Simulation results are based on comparison with a PI feedback only controller. Simulations show that augmenting the baseline PI feedback control with ZPETC feedforward control improves the blade loads but worsens the tower loads. The FX-RLS feedforward algorithm gives better performance than both the baseline PI feedback and the ZPETC feedforward in both tower (fore-aft and side-to-side) and blade (flapwise and edgewise) bending moment mitigation. Even with realistic 1 Hz LIDAR data update rate, the FX-RLS feedforward strategy can effectively mitigate the tower and blade bending moment while providing better rotor speed tracking and only a small energy drop.

Design of State-Space-Based Control Algorithms for Wind Turbine Speed Regulation

Control can improve the performance of wind turbines by enhancing energy capture and reducing dynamic loads.At the National Renewable Energy Laboratory, we are beginning to design control algorithms for regulation of turbine speed and power using state-space control designs. In this paper, we describe the design of such a control algorithm for regulation of rotor speed in full-load operation (region 3) for a two-bladed wind turbine. We base our control design on simple linear models of a turbine, which contain rotor and generator rotation, drivetrain torsion, and rotor flap degrees of freedom (first mode only). We account for wind-speed fluctuations using disturbance-accommodating control. We show the capability of these control schemes to stabilize the modeled turbine modes via pole placement while using state estimation to reduce the number of turbine measurements that are needed for these control algorithms. We incorporate these controllers into the FAST-AD code and show simulation results for various conditions. Finally, we report conclusions to this work and outline future studies.

Field Testing LIDAR Based Feed-Forward Controls on the NREL Controls Advanced Research Turbine

Wind turbines are complex, nonlinear, dynamic systems driven by aerodynamic, gravitational, centrifugal, and gyroscopic forces. The aerodynamics of wind turbines are nonlinear, unsteady, and complex. Turbine rotors are subjected to a chaotic three-dimensional (3-D) turbulent wind inflow field with imbedded coherent vortices that drive fatigue loads and reduce lifetime. In order to reduce cost of energy, future large multimegawatt turbines must be designed with lighter weight structures, using active controls to mitigate fatigue loads, maximize energy capture, and add active damping to maintain stability for these dynamically active structures operating in a complex environment. Researchers at the National Renewable Energy Laboratory (NREL) and University of Stuttgart are designing, implementing, and testing advanced feed-back and feed-forward controls in order to reduce the cost of energy for wind turbines.

Validation of Individual Pitch Control by Field Tests on Two- and Three-Bladed Wind Turbines

Further improvements in the cost-effectiveness of wind turbines drive designers toward larger, lighter, and more flexible structures, in which more intelligent control systems play an important part in actively reducing the applied structural loads. These improvements also help to eliminate the need for wind turbines to simply withstand the full force of the applied loads through the use of stronger, heavier, and therefore more expensive structures. One way to reduce the loads is to use individual pitch control (IPC), whereby each blade receives a different pitch command to compensate for asymmetrical loads caused by nonuniform flow across the rotor. Originating in helicopters, the use of IPC for wind turbines was suggested for many years. Although many simulation studies have shown that significant load reductions are possible, confirmation of this using field tests on a real turbine in natural turbulence is important to give wind turbine designers the confidence to design to the reduced loads that IPC can deliver. This paper presents the results of field tests on two different 600-kW wind turbines, one two-bladed and one three-bladed. The results demonstrate convincingly that the predicted load reductions can be achieved in practice.

Combining Standard Feedback Controllers with Feedforward Blade Pitch Control for Load Mitigation in Wind Turbines

Combined feedback/feedforward blade pitch control is compared to industry standard feedback control when simulated in realistic turbulent winds. The feedforward controllers are designed to reduce fatigue loads, increasing turbine lifetime and therefore reducing the cost of energy. Various collective pitch and individual pitch versions of two feedforward designs are studied: Gain-Scheduled Model-Inverse and Gain-Scheduled Shaped Compensator. The input to the feedforward controller is a measurement of incoming wind speed, which could potentially be provided by LIDAR. Three of the designs reduce structural loading compared to standard feedback control, without reducing power production.

Field testing of feedforward collective pitch control on the CART2 using a nacelle-based Lidar scanner

This work presents the results from a field test of LIDAR assisted collective pitch control using a scanning LIDAR device installed on the nacelle of a mid-scale research turbine. A nonlinear feedforward controller is extended by an adaptive filter to remove all uncorrelated frequencies of the wind speed measurement to avoid unnecessary control action. Positive effects on the rotor speed regulation as well as on tower, blade and shaft loads have been observed in the case that the previous measured correlation and timing between the wind preview and the turbine reaction are accomplish. The feedforward controller had negative impact, when the LIDAR measurement was disturbed by obstacles in front of the turbine. This work proves, that LIDAR is valuable tool for wind turbine control not only in simulations but also under real conditions. Furthermore, the paper shows that further understanding of the relationship between the wind measurement and the turbine reaction is crucial to improve LIDAR assisted control of wind turbines.

Adding Feedforward Blade Pitch Control for Load Mitigation in Wind Turbines: Non-Causal Series Expansion, Preview Control, and Optimized FIR Filter Methods

Combined feedback/feedforward blade pitch control is compared to industry standard feedback control when simulated in realistic turbulent winds. The feedforward controllers are designed to reduce fatigue loads, increasing turbine lifetime and therefore reducing the cost of energy. Three feedforward designs are studied: Non-Causal Series Expansion, Preview Control, and Optimized FIR Filter. The input to the feedforward controller is a measurement of incoming wind speed, which could potentially be provided by lidar. Noncausal series expansion and Preview Control methods reduce blade root loads but increase tower bending in simulation results. The optimized FIR filter reduces loads overall, keeps pitch rates low, and maintains rotor speed regulation and power capture, while using imperfect wind measurements provided by a lidar model.

Comparison of Strategies for Enhancing Energy Capture and Reducing Loads Using LIDAR and Feedforward Control

In this paper, we investigate strategies to enhance turbine energy capture and mitigate fatigue loads using pulsed light detection and ranging (LIDAR) system-enabled torque control strategies. To enhance energy capture when a turbine is operating below rated wind speed, three advanced LIDAR-enabled torque controllers are proposed: the disturbance tracking control (DTC) augmented with LIDAR, the optimally tracking rotor (OTR) control augmented with LIDAR, and LIDAR-based preview control. The DTC with LIDAR and LIDAR-based preview control is combined with a linear quadratic regulator in the feedback path, while OTR is a strategy adapted from a quadratic kΩ2 torque feedback control. These control strategies are simulated in turbulent wind files and their performance is compared against the baseline kΩ2 control scheme. We also consider the effects of different LIDAR update rates and range gates. It is shown that LIDAR-enabled controllers have only a small effect on energy capture at the cost of increased control action and low-speed shaft torque load. However, when considering a combination of fatigue load mitigation, power capture enhancement, and control authority requirements, the LIDAR-enabled preview controller outperforms the baseline kΩ2 controller.

Dynamics and control of horizontal axis wind turbines

This tutorial paper describes the field of wind turbine control. It begins with the simplest turbine dynamic models and progresses to more advanced models which incorporate structural resonances in the blades and supporting tower. Disturbance accommodating control techniques are used to provide power control in fluctuating wind fields.