Wind Turbine Model Research Articles

Dynamic response of a wind turbine wake subjected to surge and heave step motions under different inflow conditions

The development of floating offshore wind turbines poses new challenges since the floating platform introduces complex dynamics in the wind turbine wake. These wake dynamics are intricately tied to the advection velocity, referring to the velocity of the downstream propagation of the air flow, and used in the context of wind farm modelling. The present article investigates the far-wake dynamic response of a wind turbine model subjected to heave (up-down translation) and surge (fore-aft translation) step motions under two distinct inflow conditions. Wind tunnel experiments were conducted with hot-wires in a realistic turbulent inflow and a low shear and no ground effect inflow, achieved by varying the hub height of the wind turbine model in the atmospheric boundary layer developed in the test section. The results show that the dynamic response of the wake under the low shear and no ground effect inflow conditions aligns with a second-order system with the presence of undershoots and overshoots. In contrast, under realistic conditions, it appears like a first-order system with undershoots and overshoots less evident in most cases. Despite these variations the determined advection velocity remains roughly the same and consistent with the literature for both heave and surge step motions, regardless of the inflow conditions.

On the impact of different static induction control strategies on a wind turbine wake

This study provides a wind tunnel investigation to identify effective ways of derating an upstream turbine for mitigating wake-induced power losses. Short-range continuous-wave lidar measurements are used to remotely map the time-averaged wake characteristics of a controllable model wind turbine under uniform inflow with negligible turbulence. The analysis focuses on comparing four distinct static induction control (SIC) strategies, each at targeted derating levels of 5 % and 10 %, against a conventional greedy strategy. The results indicate that pitch-to-feather combined with an increased tip-speed ratio is the most favourable approach for minimising wake losses in a two-turbine setup. Notably, power gains of up to 3.8 % are achieved for partial and full wake overlap conditions at 5 % derating and small turbine spacing. These observations are consistent with previous field experiments, suggesting that SIC may be advantageous in wind farms with closely spaced turbines. Overall, these findings underscore the importance of carefully selecting and understanding the implemented derating strategy for supporting wind farm flow control applications.

Model wind turbine performance in turbulent–non-turbulent boundary layer flow

With increasing distance from the coast and greater hub heights, wind turbines expand into unknown, hardly researched environmental conditions. As height increases, laminar flow conditions become more likely. With the simultaneous increase in rotor diameter, very different flow conditions, from laminar to turbulent, occur over the rotor area. It is crucial to understand the effects of these different flow conditions on wind turbines. We approach this through wind tunnel experiments, presenting a setup with two different active grids. This setup enables the generation of four different flows – homogeneous, shear, turbulent–non-turbulent, and turbulent–non-turbulent shear flow – each with four different turbulence levels. The turbulent–non-turbulent flows exhibit a turbulence intensity gradient between the quasi-laminar flow at the upper and turbulent flow at the lower rotor half, establishing a turbulent–non-turbulent interface between the two rotor halves. In a second step, we investigate the Model Wind Turbine Oldenburg with a rotor diameter of 1.8 m (MoWiTO 1.8) under these conditions and analyze their effects on power output and blade loads. While the power fluctuations depend directly on the turbulence intensity, an additional turbulence intensity gradient shows no significant effect. A stronger effect can be observed for the blade root bending moments, the fluctuations of which increase with shear and also in turbulent–non-turbulent flow.

Wind tunnel investigations of an individual pitch control strategy for wind farm power optimization

Abstract. This article presents the results of an experimental wind tunnel study which investigates a new control strategy named Helix. The Helix control employs individual pitch control for sinusoidally varying yaw and tilt moments to induce an additional rotational component in the wake, aiming to enhance wake mixing. The experiments are conducted in a closed-loop wind tunnel under low-turbulence conditions to emphasize wake effects. Highly sensorized model wind turbines with control capabilities similar to full-scale machines are employed in a two-turbine setup to assess wake recovery potential and explore loads on both upstream and downstream turbines. In a single-turbine study, detailed wake measurements are carried out using a fast-response five-hole pressure probe. The results demonstrate a significant improvement in energy content within the wake, with distinct peaks for clockwise and counterclockwise movements at Strouhal numbers of approximately 0.47. Both upstream and downstream turbine dynamic equivalent loads increase when applying the Helix control. The time-averaged wake flow streamwise velocity and rms value reveal a faster wake recovery for actuated cases in comparison to the baseline. Phase-locked results with azimuthal position display a leapfrogging behavior in the baseline case in contrast to the actuated cases, where distorted shedding structures in the longitudinal direction are observed due to a changed thrust coefficient and an accompanying lateral vortex shedding location. Additionally, phase-locked results with the additional frequency reveal a tip vortex meandering, which enhances faster wake recovery. Comparing the Helix cases with clockwise and counterclockwise rotations, the latter exhibits slightly higher gains and faster wake recovery. This difference is attributed to Helix' additional rotational component acting in either the same or the opposite direction as the wake rotation. Overall, both Helix cases exhibit significantly faster wake recovery compared to the baseline, indicating the potential of this technique for improved wind farm control.

Combined Seismic and Scoured Numerical Model for Bucket-Supported Offshore Wind Turbines

Numerous offshore wind turbines (OWTs) with bucket foundations have been installed in seismic regions. Compared to the relative development of monopiles (widely installed), seismic design guidelines for bucket-supported OWTs still need to be developed. Moreover, scour around bucket foundations induced by water–current actions also creates more challenges for the seismic design of OWTs. In this study, a simplified seismic analysis method is proposed that incorporates the soil–structure interaction (SSI) for the preliminary design of scoured bucket-supported OWTs, aiming to balance accuracy and efficiency. The dynamic SSI effects are represented using lumped parameter models (LPMs), which are developed by fitting impedance functions of the soil–bucket foundation obtained from the four-spring Winkler model. The water–structure interaction is also considered by the added mass in seismic analysis. Based on the OpenSees 3.3.0 platform, an integral model is established and validated using the three-dimensional finite element method. The results indicate that the bucket-supported OWT demonstrates greater dynamic impedance and first-order natural frequency compared to the monopile-supported OWT, which has an increased seismic response. Seismic spectral characteristics and intensities also play an important role in the responses. Additionally, scour can change the bucket impedance functions and the frequency characteristics of the OWT system, leading to a significant alteration in the seismic response. Scour effects may be advantageous or disadvantageous, depending on the spectral characteristics of seismic excitations. These findings provide insights into the seismic response of bucket-supported OWTs under scoured conditions.

A new analytical wind turbine wake model considering the effects of coriolis force and yawed conditions

Wind turbine wakes significantly affect power production and impose higher loads on downstream turbines. Therefore, the development of accurate and efficient wake models is important for optimizing wind farm layouts and predicting wind turbine performance. This study introduces a novel analytical wake model for yawed wind turbines that incorporates the effects of the Coriolis force. The wake deflection in the far wake region is derived through the application of the principles of mass and momentum conservation. In the near wake, the deflection is assumed to be linear with distance. A Gaussian distribution is assumed for the velocity deficit within the wind turbine wake. Two approaches have been proposed to estimate the onset of the far wake region. While the first approach employs a simplified empirical formula, the second approach utilizes an iteration-based method. The proposed analytical wake model has been validated against computational fluid dynamics (CFD) results. Subsequently, the effects of several important parameters on the wake deflection have been systematically investigated. Overall, the simulation results showed a satisfactory agreement between the CFD results and those obtained from the proposed model. Furthermore, the study concluded that the Coriolis force can exert significant effects on wake deflection, particularly in the far wake region, confirming previous findings from numerical simulations. Due to its simplicity and computational efficiency, the proposed model can be readily used in several applications, including wind farm layout optimization, control and risk assessment.

Aerodynamic characterisation of a thrust-scaled IEA 15 MW wind turbine model: experimental insights using PIV data

Abstract. This study presents results from a wind tunnel experiment on a three-bladed horizontal axis wind turbine. The model turbine is a scaled-down version of the IEA 15 MW reference wind turbine, preserving the non-dimensional thrust distribution along the blade. Flow fields were captured around the blade at multiple radial locations using particle image velocimetry. In addition to these flow fields, this comprehensive dataset contains spanwise distributions of bound circulation, inflow conditions and blade forces derived from the velocity field. As such, the three blades' aerodynamics are fully characterised. It is demonstrated that the lift coefficient measured along the span agrees well with the lift polar of the airfoil used in the blade design, thereby validating the experimental approach. This research provides a valuable public experimental dataset for validating low- to high-fidelity numerical models simulating state-of-the-art wind turbines. Furthermore, this article establishes the aerodynamic properties of the newly developed model wind turbine, creating a baseline for future wind tunnel experiments using this model.

A control-oriented LPV model of large-scale offshore wind turbines for deloading operation

ABSTRACT This article develops a new control-oriented linear parameter varying (LPV) model, which is a linear state-space model with varying parameters aimed at efficiently representing large-scale offshore wind turbines (LOWTs). Firstly, the LPV model is constructed by the mechanism analysis of a 15-MW direct-drive permanent magnet wind turbine. Subseuently, based on the operational data from FAST, the parameters with unknown values in the LPV model are determined by gain scheduling and parameter estimation. Furthermore, a deloading operation for the active output power regulation of LOWTs is proposed based on coordinating the rotor over-speed control (ROC) and the pitch angle control (PAC).

WaLBerla‐wind: A lattice‐Boltzmann‐based high‐performance flow solver for wind energy applications

SummaryThis article presents the development of a new wind turbine simulation software to study wake flow physics. To this end, the design and development of waLBerla‐wind, a new simulator based on the lattice‐Boltzmann method that is known for its excellent performance and scaling properties, will be presented. Here it will be used for large eddy simulations (LES) coupled with actuator wind turbine models. Due to its modular software design, waLBerla‐wind is flexible and extensible with regard to turbine configurations. Additionally it is performance portable across different hardware architectures, another critical design goal. The new solver is validated by presenting force distributions and velocity profiles and comparing them with experimental data and a vortex solver. Furthermore, waLBerla‐wind's performance is compared to a theoretical peak performance, and analyzed with weak and strong scaling benchmarks on CPU and GPU systems. This analysis demonstrates the suitability for large‐scale applications and future cost‐effective full wind farm simulations.

Using Transfer Learning and XGBoost for Early Detection of Fires in Offshore Wind Turbine Units

To improve the power generation efficiency of offshore wind turbines and address the problem of high fire monitoring and warning costs, we propose a data-driven fire warning method based on transfer learning for wind turbines in this paper. This paper processes wind turbine operation data in a SCADA system. It uses an extreme gradient-boosting tree (XGBoost) algorithm to build an offshore wind turbine unit fire warning model with a multiparameter prediction function. This paper selects some parameters from the dataset as input variables for the model, with average cabin temperature, average outdoor temperature, average cabin humidity, and average atmospheric humidity as output variables. This paper analyzes the distribution information of input and output variables and their correlation, analyzes the predicted difference, and then provides an early warning for wind turbine fires. This paper uses this fire warning model to transfer learning to different models of offshore wind turbines in the same wind farm to achieve fire warning. The experimental results show that the prediction performance of the multiparameter is accurate, with an average MAPE of 0.016 and an average RMSE of 0.795. It is better than the average MAPE (0.051) and the average RMSE (2.020) of the prediction performance of a backpropagation (BP) neural network, as well as the average MAPE (0.030) and the average RMSE (1.301) of the prediction performance of random forest. The transfer learning model has good prediction performance, with an average MAPE of 0.022 and an average RMSE of 1.469.

Modelling of linear large-capacity wind turbines with application to distributed control in wind farms

ABSTRACT Due to the widespread penetration of wind energy, large-capacity wind turbines (WTs) are becoming more conspicuous. In this paper, a linear control-oriented large-capacity wind turbine (WT) model is redesigned based on the 15 MW WT in openFAST. Compared with the original model, the redesigned model is more efficient and conducive to the design of linear controllers. To verify the validity of the redesigned WT model, we design a distributed control scheme with deloading operation of the wind farm (WF) to fairly share the load. Simulations are performed to verify the effectiveness of the application in the WF.

Effect of combined surge and pitch motion on the aerodynamic performance of floating offshore wind turbine

Floating offshore wind turbines (FOWTs) can encounter variations in the phase difference and amplitude of combined surge-pitch platform motion. The phase difference between surge and pitch movement can result in the superposition or cancellation of wind turbine motion, potentially leading to significant changes in its aerodynamic characteristics. Herein, a wind turbine model was developed using the computational fluid dynamics (CFD) method to investigate the aerodynamic performance of the wind turbine. The findings revealed significant variations in the aerodynamics characteristics of the wind turbine when subjected to surge and pitch motions. When the rotor moves forward with a large relative velocity, dynamic stall can be triggered and induce the violent fluctuation of aerodynamic load. Moreover, vortex ring state (VRS) may occur when the wind turbine moves backward, leading to the recirculation of tip and root vortices and possibly causing negative thrust and torque. The trend of the drag force acting on the tower exhibits an almost complete opposition to that of the rotor thrust with the change of phase difference in the combined surge-pitch motion. It is also noted that the tower lift fluctuates asymmetrically, and as the platform motion amplitude increases, the tower lift gradually increases towards one side.

Experimental study of turbulent inflow on the aerodynamic performance of a wind turbine with Gurney flaps

Nowadays, wind turbines operate within complex inflow environments. Meanwhile, installing Gurney flaps on existing wind turbines could enhance wind energy efficiency. However, limited research has been conducted on the variation of aerodynamic characteristics of a wind turbine equipped with Gurney flaps under turbulent inflow conditions. Hence, wind tunnel test comparisons were made between the output power, wind load, and wake characteristics of a model wind turbine with and without Gurney flaps. The results demonstrated a correlation between the additional power increase in the wind turbine equipped with Gurney flaps and the aerodynamic variation of the corresponding airfoil. Gurney flaps could be effective at higher tip speed ratios, and the power enhancement efficiency initially increased but then decreased as turbulence intensity increased from a low value to 19.0%. Installing Gurney flaps resulted in significant pulsation peaks within the original inertial sub-range. The time-averaged thrust coefficient shifts upward, but the difference decreases slightly under turbulent conditions. Wake analysis revealed that the presence of additional wake velocity deficits primarily concentrated within the near-wake region, which extends along the spanwise direction. These findings could enhance a better understanding of the aerodynamic performances of wind turbines installing Gurney flaps under varying turbulent flow conditions.

An efficient active learning Kriging approach for expected fatigue damage assessment applied to wind turbine structures

Estimating fatigue damage in wind turbine structures over their lifetime is a time-demanding task, necessitating simulations encompassing all the wind and wave conditions. Typically, this involves expending several thousand hours in simulation efforts. Given formidable computational demand, this study proposes an efficient active learning Kriging approach named ALK-EFDA for estimating expected fatigue damage in wind turbines. The ALK-EFDA framework leverages a Kriging surrogate model to predict fatigue damage levels across various wind-wave scenarios, and a learning function is designed to reduce Kriging prediction error in the high probability of occurrences of wind-wave cases. An active learning approach is proposed to estimate the expected fatigue damage efficiently. To validate the effectiveness of this approach, we applied it to a 15 MW Semi-submersible floating wind turbine model. The proposed approach estimates the expected short-term fatigue damage of the wind turbine tower and mooring lines. Our findings demonstrate that the ALK-EFDA method efficiently and accurately estimates fatigue damage in wind turbine structures. Compared to simulation methods, the proposed ALK-EFDA approach significantly reduces computational expenses by over 30 times. Furthermore, the absolute error, when compared to simulated results, remains at approximately 1%. This method will be a useful tool for offshore wind turbine designers.

Optimization of energy efficiency for offshore wind farms via wake modeling-free NMPC

This paper presents an energy efficiency improvement scheme for offshore wind farms. This scheme uses learning-based nonlinear model predictive control (NMPC) and does not require wake modeling. The main feature is that each wind turbine can independently complete all optimal control, without the need for wind farm wake model calculation and iterative optimization. Specifically, this paper studies the critical factors for increasing the active power of wind farms and reducing the structural load of wind turbines, and reconstructs the optimal control objectives of wind turbines. Furthermore, combined with the nonlinear model of wind turbines containing uncertainty, a nonlinear model predictive control algorithm based on deep neural networks was designed, which can achieve multi-objective optimal control at the wind turbine level. The simulation results show that the uncertainty estimation method can effectively improve the nonlinear control performance, achieve an increase in the active power of the wind farm without wake iterative optimization, and at the same time suppress the structural loads of all wind turbines.

Root cause localization for wind turbines using physics guided multivariate graphical modeling and fault propagation analysis

Under the background of widespread utilization of wind energy, the improvement of power generation efficiency and the reduction of operation and maintenance costs have increased the demand for anomaly detection and localization of wind turbines through fault propagation analysis, while the large-scale data collected by SCADA systems provide sufficient data support and promising assistance for this purpose. It is an effective and cost-saving solution to this demand to construct a high-precision wind turbine digital twin model under normal operating conditions based on multivariate monitoring data, and to realize root cause localization based on prediction deviation and backtracking analysis. This paper proposes a root cause localization framework for wind turbines based on explicit-implicit knowledge fusion and multivariate time-series graph neural networks, fully utilizing the inter-variable dependencies mining capability of graph structure learning and the temporal prediction advantage of time-series graph neural networks. Five types of explicit knowledge based on explicit rules are constructed to describe the explicit relationships between monitoring variables. The fused knowledge (i.e., graph adjacency matrix) is obtained by fusing the explicit knowledge with the implicit knowledge hidden in the data through a quasi-hard-attention mechanism based on the idea of graph structure learning. A high-precision digital twin model for wind turbines based on fused knowledge is constructed using the multivariate time-series graph neural network. The anomaly degree of monitoring variables’ state is defined based on the prediction deviation, with which the propagation of fault's state over time can be accurately described, and the root cause localization through backtracking analysis can be achieved. Case studies are conducted using field fault data from a wind farm, a sugar factory and a thermal power plant. The results show that the proposed framework can provide a solution for studying the fault propagation process and root cause localization, also demonstrating certain robustness and generalizability.

Design and Research of Computer Model of Wind Turbine Using LabVIEW

Design and Research of Computer Model of Wind Turbine Using LabVIEW

Multiple-node model of wind turbine generating system for unbalanced distribution system load flow analysis

This paper discusses a method to integrate a wind turbine generating system (WTGS) into a three-phase unbalanced distribution system load flow (DSLF) analysis. The proposed method is based on the single-phase multiple-node model. In the present work, the single-phase multiple-node model is extended to a three-phase multiple-node model to facilitate the load flow analysis of a three-phase unbalanced power system network. The multiple-node model (i.e., three-node model) will only modify the load flow analysis by introducing two lines and two load buses to the distribution system network where the WTGS is installed. Thus, a standard three-phase load flow program can be employed to compute the unknown quantities in the DSLF problem formulation. The proposed method is verified by incorporating the model into the load flow analysis of three-phase distribution networks. The investigation uses two representative distribution networks (i.e., 19-bus and 25-bus networks). The results of the study confirm the validity of the proposed method.

Multi-segment droop control and optimal parameter setting strategy of wind turbine for frequency regulation

Traditionally, virtual inertia is used as the control strategy for wind turbines when participating in frequency regulation. However, it has inherent defects in measurement error amplification due to the frequency differential. Also, when control delay of wind turbines is taken into consideration, virtual inertia is essentially a fast power response, just the same as droop control. Hence, considering the adaptability of droop control, it is valuable to explore a novel droop control method without virtual inertia, as the frequency response model of wind turbines. For this reason, a multi-segment droop control strategy is proposed to provide a reliable frequency regulation model for wind turbines that can be broadly applied to other inverter-based power sources. Firstly, based on the extended system frequency response model, a differential equation including piecewise time-varying coefficients is established, and the analytical expression of the frequency response is obtained using the impulse function balancing principle and the integration by parts algorithm. Subsequently, based on analytical expression, the performance of the presented strategy is theoretically analyzed via comparison with virtual inertia control, and the conclusion that the presented multi-segment droop control has better frequency regulation performance can be got. Furthermore, to achieve optimal frequency regulation performance, a Lagrangian function is established to determine the optimal parameters of the proposed control strategy. Finally, the performance of the proposed strategy was verified in a two-area system model constructed on MATLAB/Simulink. Results show that the proposed multi-segment droop control has better frequency regulation performance.

Wind power forecasting method of large-scale wind turbine clusters based on DBSCAN clustering and an enhanced hunter-prey optimization algorithm

As the large-scale grid connection of wind turbines poses challenges to the safe and stable operation of the power grid, it is necessary to forecast the power of wind turbine clusters. However, with the rapid increase in the installed capacity of wind turbines, simultaneously improving the accuracy and efficiency of large-scale wind-turbine cluster power prediction models has become a challenge. In this research, a novel hybrid power-forecasting model for wind-turbine clusters has been designed using density-based spatial clustering of applications with noise (DBSCAN) and an enhanced hunter-prey optimization algorithm (ENHPO). First, the Pearson correlation coefficient was used to select multiple variables that significantly affected the wind power. Second, multiple wind turbines are clustered into different groups using the DBSCAN clustering algorithm, and ENHPO is employed to optimize the DBSCAN parameters to strengthen the clustering performance. Finally, one wind turbine with a high correlation was selected as the representative wind turbine in each group, and power prediction was achieved using the multivariate long short-term memory. Simulation results of four datasets in different seasons show that, compared with other clustering methods, such as fuzzy C-means, balanced iterative reduction, and clustering using hierarchies, K-means, and density peak clustering, the prediction accuracy and prediction efficiency of the proposed hybrid prediction model are improved by 21.85% and 18.07%, respectively, on average. The experimental results effectively demonstrate that the designed model can enhance the accuracy and efficiency of power forecasting simultaneously.