Bonnie Jonkman

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.

Horizontal Velocity and Variance Measurements in the Stable Boundary Layer Using Doppler Lidar: Sensitivity to Averaging Procedures

Abstract Quantitative data on turbulence variables aloft—above the region of the atmosphere conveniently measured from towers—have been an important but difficult measurement need for advancing understanding and modeling of the stable boundary layer (SBL). Vertical profiles of streamwise velocity variances obtained from NOAA’s high-resolution Doppler lidar (HRDL), which have been shown to be approximately equal to turbulence kinetic energy (TKE) for stable conditions, are a measure of the turbulence in the SBL. In the present study, the mean horizontal wind component U and variance σ2u were computed from HRDL measurements of the line-of-sight (LOS) velocity using a method described by Banta et al., which uses an elevation (vertical slice) scanning technique. The method was tested on datasets obtained during the Lamar Low-Level Jet Project (LLLJP) carried out in early September 2003, near the town of Lamar in southeastern Colorado. This paper compares U with mean wind speed obtained from sodar and sonic an...

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.

LIDAR Wind Speed Measurements of Evolving Wind Fields

Light Detection and Ranging (LIDAR) systems are able to measure the speed of incoming wind before it interacts with a wind turbine rotor. These preview wind measurements can be used in feedforward control systems designed to reduce turbine loads. However, the degree to which such preview-based control techniques can reduce loads by reacting to turbulence depends on how accurately the incoming wind field can be measured. Past studies have assumed Taylors frozen turbulence hypothesis, which implies that turbulence remains unchanged as it advects downwind at the mean wind speed. With Taylors hypothesis applied, the only source of wind speed measurement error is distortion caused by the LIDAR. This study introduces wind evolution, characterized by the longitudinal coherence of the wind, to LIDAR measurement simulations to create a more realistic measurement model. A simple model of wind evolution is applied to a frozen wind field used in previous studies to investigate the effects of varying the intensity of wind evolution. LIDAR measurements are also evaluated with a large eddy simulation of a stable boundary layer provided by the National Center for Atmospheric Research. Simulation results show the combined effects of LIDAR errors and wind evolution for realistic turbine-mounted LIDAR measurement scenarios.

Doppler lidar measurements of the great plains low-level jet: applications to wind energy

The southerly low-level jet (LLJ) of the Great Plains of the United States is a recurrent flow feature of the nighttime boundary layer of the region, which has been identified as a region of high potential for wind energy. The acceleration of the LLJ after sunset produces an enhancement of the wind speed over daytime values, and provides a dependable resource for wind energy. On the negative side, occasional bursts of strong turbulence may be generated that can be of just the right frequency to excite strong oscillatory response in the turbine rotors, thereby accelerating the fatigue of the rotor parts. High resolution Doppler lidar has been used in two studies of the LLJ over the U.S. Great Plains. In this paper we show the usefulness of this remote sensing tool in documenting the mean and turbulent vertical structure, and the evolution of these vertical structures through entire nights. This leads to implications about potential usefulness of Doppler lidar in monitoring mean winds and turbulence in real time to aid in turbine operations.

BeamDyn: a high‐fidelity wind turbine blade solver in the FAST modular framework

This paper presents a numerical implementation of the geometrically exact beam theory based on the Legendre-spectral-finite-element (LSFE) method. The displacement-based geometrically exact beam theory is presented, and the special treatment of three-dimensional rotation parameters is reviewed. An LSFE is a high-order finite element with nodes located at the Gauss–Legendre–Lobatto points. These elements can be an order of magnitude more computationally efficient than low-order finite elements for a given accuracy level. The new module, BeamDyn, is implemented in the FAST modularization framework for dynamic simulation of highly flexible composite-material wind turbine blades within the FAST aeroelastic engineering model. The framework allows for fully interactive simulations of turbine blades in operating conditions. Numerical examples are provided to validate BeamDyn and examine the LSFE performance as well as the coupling algorithm in the FAST modularization framework. BeamDyn can also be used as a stand-alone high-fidelity beam tool. Copyright

Inflow Measurement in a Tidal Strait for Deploying Tidal Current Turbines: Lessons, Opportunities and Challenges

Tidal energy has received increasing attention over the past decade. This increasing focus on capturing the energy from tidal currents has brought about the development of many designs for tidal current turbines. Several of these turbines are progressing rapidly from design to prototype and pre-commercial stages. As these systems near commercial development, it becomes increasingly important that their performance be validated through laboratory tests (e.g., towing tank tests) and sea tests. Several different turbine configurations have been tested recently. The test results show significant differences in turbine performance between laboratory tests, numerical simulations, and sea tests. Although the mean velocity of the current is highly predictable, evidence suggests a critical factor in these differences is the unsteady inflow. To understand the physics and the effect of the inflow on turbine performance and reliability, Verdant Power (Verdant) and the National Renewable Energy Laboratory (NREL) have engaged in a partnership to address the engineering challenges facing marine current turbines. As part of this effort, Verdant deployed Acoustic Doppler Current Profiler (ADCP) equipment to collect data from a kinetic hydropower system (KHPS) installation at the Roosevelt Island Tidal Energy (RITE) project in the East River in New York City. The ADCP collected data for a little more than one year, and this data is critical for properly defining the operating environment needed for marine systems. This paper summarizes the Verdant-NREL effort to study inflow data provided by the fixed, bottom-mounted ADCP instrumentation and how the data is processed using numerical tools. It briefly reviews previous marine turbine tests and inflow measurements, provides background information from the RITE project, and describes the test turbine design and instrumentation setup. This paper also provides an analysis of the measured time domain data and a detailed discussion of shear profiling, turbulence intensity, and time-dependent fluctuations of the inflow. The paper concludes with suggestions for future work. The analysis provided in this paper will benefit future turbine operation studies. In addition, this study, as well as future studies in this topic area, will be beneficial to environmental policy makers and fishing communities.Copyright

FAST v8 Verification and Validation for a MW-scale Wind Turbine with Aeroelastically Tailored Blades

This paper presents findings from a verification and validation exercise on the latest version of the Department of Energy/National Renewable Energy Laboratory wind turbine aeroelastic engineering simulation tool FAST. Results from a set of 1141 FAST simulations were compared to those from the Siemens’ aeroelastic simulation tool BHawC, as well as experimental data from a heavily instrumented 2.3 megawatt Siemens wind turbine located at the National Wind Technology Center. The code validation was performed following the IEC-61400-13 standard, where a set of select quantities of interest from simulations at various wind speeds and atmospheric turbulence conditions were used for a three-way comparison between FAST, BHawC, and the measurements. Results highlight many improvements of the latest version of FAST over its previous versions. This paper also provides comments from the authors on the data quality, and avenues for potential future work using these results.

FAST Modular Framework for Wind Turbine Simulation: New Algorithms and Numerical Examples

Over the past few years, the FAST wind turbine simulation tool has undergone a major restructuring. FAST is now, at its core, an algorithm and software framework for coupling time-dependent multi-physics modules relevant to computer-aided engineering (CAE) of wind turbines. Each module, which represents one or more turbine components or physics control volumes, is constituted by a mathematical model composed of time-dependent constraint and/or differential equations that are typically nonlinear. Under this new modular form, modules can interact through matching or non-matching spatial meshes and can be time advanced with different time steps and different time integrators. Sharing of data between modules is accomplished with a predictor-corrector approach, which allows for either implicit or explicit time integration within each module. This new modularity positions FAST as a backbone for coupling both high-fidelity and engineering-level wind turbine physics models. In this paper, we describe new features of the FAST modular framework. In particular, we describe a new mixed-time-step algorithm, sparse-matrix storage, a direct solver for sparse linear systems, and interpolation of rotation fields in space for mesh mapping and in time for time advancement. We also show several numerical examples that demonstrate the performance and flexibility of the FAST framework, and we use those results to provide modeling guidance to users.

FAST Modular Wind Turbine CAE Tool: Nonmatching Spatial and Temporal Meshes

In this paper we propose and examine numerical algorithms for coupling time-dependent multi-physics modules relevant to computer-aided engineering (CAE) of wind turbines. In particular, we examine algorithms for coupling modules where spatial grids are nonmatching at interfaces and module solutions are time advanced with different time increments and different time integrators. The new mesh-mapping algorithm supports mapping between spatial meshes that are highly disparate. Sharing of data between modules is accomplished with a predictor-corrector approach, which allows for either implicit or explicit time integration within each module. Algorithms are presented in a general framework, but are applied to simple problems that are representative of the systems found in a wholeturbine analysis. Numerical experiments are used to explore the stability, accuracy, and efficiency of the proposed algorithms. This work is motivated by an in-progress major revision of FAST, the National Renewable Energy Laboratory’s (NREL’s) premier aero-elastic CAE simulation tool. The algorithms described here will greatly increase the flexibility and efficiency of FAST.