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At least 109 records · Page 6

Modeling, Validation, and Control of the IEA‐15 MW Reference Wind Turbine and VolturnUS‐S Platform

This paper presents the acausal modeling, validation, and control of floating offshore wind turbines (FOWTs). The model simulates the IEA‐15 MW reference turbine and the semi‐submersible VolturnUS‐S platform utilizing a Control‐oriented, Reconfigurable, and Acausal Floating Turbine Simulator (CRAFTS), which integrates the key coupled aero‐hydro‐elasto‐servo dynamics and is being developed by authors at the University of Central Florida. Verification and validation are conducted using numerical data from the industry‐standard simulation platform OpenFAST and experimental data from the Floating Offshore‐wind and Controls Advanced Laboratory (FOCAL) project, in which the authors were involved. Numerical results demonstrate the model's ability to qualitatively capture loads and responses across various load cases, highlighting the impact of the control system under different wind and wave conditions and opening new opportunities for optimizing FOWT designs. This paper provides wind turbine researchers with valuable insights into system characteristics, system frequencies, damping effects, and internal reaction forces, serving as a reference for future studies in FOWT modeling and control.

17 WIND ENERGY↗

On turbulence models and lidar measurements for wind turbine control

Abstract. 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.

17 WIND ENERGY↗

Aeroservoelastic Stability of a Floating Wind Turbine

The problem of negative damping undermines the aeroservoelastic stability of floating offshore wind turbines. The negative damping problem is most prevalent around rated wind speed, where the sensitivity of thrust to wind speed is the largest. This paper investigates the implementation of peak shaving, a controller feature that limits the rated thrust by pitching the blades before rated wind speed is reached. Two controller designs are investigated: a de-tuned controller and a nacelle-feedback controller. A time-domain metric is defined, inspired by Lyapunov theory, in order to compute and assess the stability of floating offshore wind turbines. The model of the International Energy Agency 15-MW reference wind turbine mounted on the University of Maine VolturnUS-S floater is simulated in HAWC2 with the National Renewable Energy Laboratory reference open-source controller. Peak shaving is applied to the two controller designs and stability is assessed. According to the chosen metric, peak shaving does not improve the stability of the system. This is due to the trade-off between loads and error tracking: although the loads and displacements in the fore-aft direction of the turbine are reduced, the rotor-speed tracking is poorer, which increases the shaft torsion fatigue load.

17 WIND ENERGY↗

Advanced Distributed Wind Turbine Controls Series: Part 1-Flatirons Campus Model Overview – Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL)

Wind turbines are typically deployed to provide energy, reduce diesel-fuel consumption, reduce carbon emissions, and reduce costs for energy and fuel transportation. However, in addition to solely providing energy to the power system, wind turbines contain rotating masses and inverter-based controls that can enable various reliability and resilience services through advance controls. As part of the Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL), it is demonstrated that advanced wind turbine controls can be employed to support higher contributions of wind, and to demonstrate ways that wind can play a role in supporting grid stability in islanded or grid-connected configurations. This paper documents models of various subsystem comprising a portion of NREL's Flatirons campus that will be used in three subsequent reports to demonstrate capabilities of advanced wind turbine controls. The series of reports will detail advanced capabilities of distributed wind turbines to provide support to isolated grids, distribution grids, and microgrids. We developed models to simulate a wind turbine (600 kW), solar PV (430 kW), battery energy storage system (1 MW/1MWh), a diesel generator (2 MW) and various types of loads (critical, dynamic). The model of the subsystems in MATLAB/Simulink are validated with available data from real-world components on NREL's Flatirons Campus. These validated models can be configured for various studies including four MIRACL use cases: 1) isolated grids, 2) microgrids, and 3) behind-the-meter, and 4) front-of-the-meter wind turbine deployments.

17 WIND ENERGY↗

WISDEM® v3.4.1(Wind Plant Integrated System Design and Engineering Model) [SWR-14-05]

The Wind Plant Integrated System Design and Engineering Model (WISDEM®) is a set of models for assessing overall wind plant cost of energy (COE). The models use wind turbine and plant cost and energy production as well as financial models to estimate COE and other wind plant system attributes. WISDEM® is accessed through Python, is built using OpenMDAO, and uses several sub-models that are also implemented within OpenMDAO. These sub-models can be used independently but they are required to use the overall WISDEM® turbine design capability. Please install all of the pre-requisites prior to installing WISDEM® by following the directions below. For additional information about the NWTC effort in systems engineering that supports WISDEM® development, please visit the official NREL systems engineering for wind energy website. https://www.nlr.gov/wind/systems-engineering

Dykes, Katherine↗

WISDEM® v3.15.2 2024 (Wind-Plant Integrated System Design and Engineering Model) [SWR-14-05]

The Wind-Plant Integrated System Design and Engineering Model (WISDEM®) is a set of models for assessing overall wind plant cost of energy (COE). The models use wind turbine and plant cost and energy production as well as financial models to estimate COE and other wind plant system attributes. WISDEM® is accessed through Python, is built using OpenMDAO, and uses several sub-models that are also implemented within OpenMDAO. These sub-models can be used independently but they are required to use the overall WISDEM® turbine design capability. Please install all of the pre-requisites prior to installing WISDEM® by following the directions below. For additional information about the NWTC effort in systems engineering that supports WISDEM® development, please visit the official NREL systems engineering for wind energy website. https://www.nrel.gov/wind/systems-engineering.html

Dykes, Katherine↗

Evolution of eddy viscosity in the wake of a wind turbine

The eddy viscosity hypothesis is a popular method in wind turbine wake modeling for estimating turbulent Reynolds stresses. We document the downstream evolution of eddy viscosity in the wake of a wind turbine from experimental and large-eddy-simulation data. Wake eddy viscosity is isolated from its surroundings by subtracting the inflow profile, and the driving forces are identified in each wake region. Eddy viscosity varies in response to changes in turbine geometry and nacelle misalignment with larger turbines generating stronger velocity gradients and shear stresses. We propose a model for eddy viscosity based on a Rayleigh distribution. Model parameters are obtained from scaling the eddy viscosity hypothesis and demonstrate satisfactory agreement with the reference data. The model is implemented in the curled wake formulation in the FLOw Redirection and Induction in Steady State (FLORIS) framework and assessed through comparisons with the previous formulation. Our approach produced more accurate flow field estimates with lower total error for the majority of cases.

17 WIND ENERGY↗

A simple method for modelling fatigue spectra of small wind turbine blades

Small-scale wind turbines have market opportunities in distributed energy generation applications but face future challenges in remaining cost competitive compared with solar photovoltaic systems. High unit costs can be attributed to design conservatism when calculating fatigue loads of key structural components such as the blades. In this study, we use the aeroelastic software FAST to highlight limitations of the International Electrotechnical Commission 61400-2:2013 small wind turbine design standard for calculating fatigue life using the simplified load model. We present a modified method for calculating the fatigue spectra of small wind turbine blades. An advantage of this method is that it does not require complex aeroelastic simulations or field measurements. This modified method is intended to be implemented early in the blade design stage, such as during rotor optimization simulations, allowing for multiple rotor configurations to be rapidly compared.

17 WIND ENERGY↗

Prognosis of Wind Turbine Gearbox Bearing Failures Using SCADA and Modeled Data

Predictive maintenance and condition monitoring systems for wind turbines have seen increased adoption to minimize downtime, reducing operation and maintenance costs. On today’s wind power plants, the integrated supervisory control and data acquisition (SCADA) system provides low- frequency operational data that can be leveraged to quantify a wind turbine’s health. The aim of this study is to utilize machine-learning techniques to predict axial cracking failures in wind turbine gearbox bearings up to 1 month ahead of time. The failures are assumed to have occurred when the investigated bearing was replaced. While current SCADA systems show the overall condition of a wind turbine, often they do not allow for the investigation of specific gearbox bearings’ health. To enrich bearing fault signatures, additional data are computed through physics-based models using gearbox design information. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score. Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision and F1 score is about 33% and 12% respectively, based on the best modeling case in this study.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

Aeroelastic code comparison using the IEA 22MW reference turbine

Reference wind turbine designs and the associated aeroelastic models are widely used in both research and industry. Reference models representing future concepts are of particular interest. Current state of the art aeroelastic tools are relied upon to design the next generation of large wind turbines. However, modelling assumptions may be invalidated by upcoming very large turbines, and different aeroelastic tools may give inconsistent results. A 22MW turbine model has been defined as part of International Energy Agency (IEA) Wind Task 55 on Reference Wind Turbines and Farms to represent future turbines to be deployed in the 2030s. In this study, an aeroelastic model of this turbine has been created in four tools; Bladed, HAWC2, OpenFAST, and QBlade. Code comparisons are presented for steady state operation, linear stability analysis, and time domain power production simulations in steady and turbulent wind. Generally, the codes show a good agreement, but with some differences present in the linear stability analysis, periodic azimuthal variation, and time domain simulations. The models are a good basis for further study with the IEA 22MW turbine, and further code comparison exercises.

17 WIND ENERGY↗

Impacts of wind field characteristics and non-steady deterministic wind events on time-varying main-bearing loads

Abstract. This work considers the characteristics and drivers of the loads experienced by wind turbine main bearings. Simplified load response models of two different hub and main-bearing configurations are presented, representative of both inverting direct-drive and four-point-mounted geared drivetrains. The influences of deterministic wind field characteristics, such as wind speed, shear, yaw offset, and veer, on the bearing load patterns are then investigated for similarity scaled 5, 7.5, and 10 MW reference wind turbine models. Main-bearing load response in cases of deterministic gusts and extreme changes in wind direction are also considered for the 5 MW model. Perhaps surprisingly, veer is identified as an important driver of main-bearing load fluctuations. Upscaling results indicate that similar behaviour holds as turbines become larger, but with mean loads and load fluctuation levels increasing at least cubically with the turbine rotor radius. Strong links between turbine control and main-bearing load response are also observed.

17 WIND ENERGY↗

The inclusion of uncertainty in circularity transition modeling: A case study on wind turbine blade end-of-life management

The transition to a more circular economy (CE) is complex and hard to predict, including in sustainable energy technologies. Many sources of uncertainty make it challenging to model CE scenarios and their potential benefits. As an example, the high variability in costs and revenues of different recycling options makes future wind turbine blade recycling highly uncertain. To better understand this challenge, the circular economy, life cycle assessment and visualization (CELAVI) framework - a discrete event simulation and life cycle assessment framework - is modified to incorporate uncertainty analysis capabilities. Moreover, a 3-step procedure that covers different aspects of uncertainty in CE studies and includes a Monte-Carlo analysis is proposed. The procedure is tested in a case study on wind turbine blade recycling using CELAVI. Results highlight that grinding and landfilling costs are the most influential parameters for wind power circularity. The model's output coefficients of variation (when input parameter uncertainties are propagated) are between 92% and 384% depending on the indicator. The approach developed in this study may help researchers and decision-makers who study circularity prioritize their data collection effort. Finally, our method contributes to a mounting yet critical body of research: the measurement of uncertainty in circularity transitions.

17 WIND ENERGY↗

pyNuMAD v.0.1

SAND2024-08606O The pyNuMAD software is used for managing wind turbine blade model data. pyNuMAD specializes in defining the geometry, materials, and boundary conditions for structural analysis of wind turbine blades. This includes loading in data files and providing an interface for users to make updates to the model. The software also features meshing functionality, which takes the blade model and creates a shell or brick mesh for use in finite element analysis. A typical user workflow might be: load in blade information from a yaml file, make adjustments to the blade properties, update the blade based on the adjustments, create a mesh of the blade, export this blade to another software for structural analysis. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Paquette, Joshua↗

Open-Loop Control Co-Design of Semisubmersible Floating Offshore Wind Turbines Using Linear Parameter-Varying Models

Here, this paper discusses a framework to design elements of the plant and control systems for floating offshore wind turbines in an integrated manner using linear parameter-varying models. Multiple linearized models derived from aero-elastic simulation software in different operating regions characterized by the incoming wind speed are combined to construct an approximate low-fidelity model of the system. The combined model is then used to generate open-loop, optimal control trajectories as part of a nested control co-design strategy that explores the system’s power production and stability using the platform pitch tilt as a proxy in the context of crucial plant and control design decisions. The radial distance between the central and outer columns and the diameter of the outer columns of the semisubmersible platform are the plant design variables. The platform stability and power production are studied for different plant design decisions. The effect of plant decisions on subsequent power production and stability response of the floating wind turbine is quantified in terms of the levelized cost of energy. The results show that the inner-loop constraints and the plant design decisions affect the turbine’s power and, subsequently, the cost of the system.

17 WIND ENERGY↗

Rigid-flexible coupling multi-body dynamics modeling of a semi-submersible floating offshore wind turbine

A 14-degree-of-freedom (14-DOF) flexible multibody dynamics model is developed and verified for a semi-submersible floating offshore wind turbine (FOWT). The model considers the coupled dynamics of the platform, tower, nacelle, blades, and mooring subjected to external wind and wave loads. The platform is simplified as a rigid body connected to the seabed by the mooring system. The tower and blade are modeled as flexible cantilever beams. The energy method is used to derive the governing equations of motion, where the kinetic energy, potential energy, and work done by external wind-wave forces are all deduced in a global coordinate system. The 5-MW baseline semi-submersible FOWT is used to verify the derived model against the results simulated from FAST developed by the US National Renewable Energy Laboratory (NREL) at two scenarios: free decay state and different wind-wave load cases. Results show that the established FOWT model can well reflect the vibration characteristics of FAST model. The application of the model to the control of platform pitch with a tuned mass damper is studied. Furthermore, the simplified model could provide a low-order method for the structural dynamics analysis and advanced vibration control design for the multi-body components of the semi-submersible FOWT in the future.

17 WIND ENERGY↗

Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models

Abstract. Wake measurements of a scanning Doppler lidar mounted on the nacelle of a full-scale wind turbine during a wake-steering experiment were used for the characterization of the wake flow, the evaluation of the wake-steering set-up, and the validation of analytical wake models. Inflow-scanning Doppler lidars, a meteorological mast, and the supervisory control and data acquisition (SCADA) system of the wind turbine complemented the set-up. Results from the wake-scanning Doppler lidar showed an increase in the wake deflection with the yaw angle and that the wake deflection was not in all cases beneficial for the power output of a downstream turbine due to a bias of the inflow wind direction perceived by the yawed wind turbine and the wake-steering design implemented. Both observations could be reproduced with an analytical model that was initialized with the inflow measurements. Error propagation from the inflow measurements that were used as model input and the power coefficient of a waked wind turbine contributed significantly to the model uncertainty. Lastly, the span-wise cross section of the wake was strongly affected by wind veer, masking the effects of the yawed wind turbine on the wake cross sections.

17 WIND ENERGY↗

A vortex sheet based analytical model of the curled wake behind yawed wind turbines

Motivated by the need for compact descriptions of the evolution of non-classical wakes behind yawed wind turbines, we develop an analytical model to predict the shape of curled wakes. Interest in such modelling arises due to the potential of wake steering as a strategy for mitigating power reduction and unsteady loading of downstream turbines in wind farms. We first estimate the distribution of the shed vorticity at the wake edge due to both yaw offset and rotating blades. By considering the wake edge as an ideally thin vortex sheet, we describe its evolution in time moving with the flow. Vortex sheet equations are solved using a power series expansion method, and an approximate solution for the wake shape is obtained. The vortex sheet time evolution is then mapped into a spatial evolution by using a convection velocity. Apart from the wake shape, the lateral deflection of the wake including ground effects is modelled. Our results show that there exists a universal solution for the shape of curled wakes if suitable dimensionless variables are employed. For the case of turbulent boundary layer inflow, the decay of vortex sheet circulation due to turbulent diffusion is included. Finally, we modify the Gaussian wake model by incorporating the predicted shape and deflection of the curled wake, so that we can calculate the wake profiles behind yawed turbines. Model predictions are validated against large-eddy simulations and laboratory experiments for turbines with various operating conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Going beyond BEM with BEM: an insight into dynamic inflow effects on floating wind turbines

Blade element momentum (BEM) theory is the backbone of many industry-standard wind turbine aerodynamic models. To be applied to a broader set of engineering problems, BEM models have been extended since their inception and now include several empirical corrections. These models have benefitted from decades of development and refinement and have been extensively used and validated, proving their adequacy in predicting aerodynamic forces of horizontal-axis wind turbine rotors in most scenarios. However, the analysis of floating offshore wind turbines (FOWTs) introduces new sets of challenges, especially if new-generation large and flexible machines are considered. In fact, due to the combined action of wind and waves and their interaction with the turbine structure and control system, these machines are subject to unsteady motion and thus unsteady inflow on the wind turbine's blades, which could put BEM models to the test. Consensus has not been reached on the accuracy limits of BEM in these conditions. This study contributes to the ongoing research on the topic by systematically comparing four different aerodynamic models, ranging from BEM to computational fluid dynamics, in an attempt to shed light on the unsteady aerodynamic phenomena that are at stake in FOWTs and whether BEM is able to model them appropriately. Simulations are performed on the UNAFLOW 1:75 scale rotor during imposed harmonic surge and pitch motion. Experimental results are available for these conditions and are used for baseline validation. The rotor is analyzed in both rated operating conditions and low wind speeds, where unsteady aerodynamic effects are expected to be more pronounced. Results show that BEM, despite its simplicity, can adequately model the aerodynamics of FOWTs in most conditions if augmented with a dynamic inflow model.

17 WIND ENERGY↗