Response Of Turbines Research Articles

Modal Analysis of Offshore Monopile Wind Turbine: An Analytical Solution

Abstract An analytical solution of the dynamic response of offshore wind turbines under wave load with nonlinear Stokes’s wave theory and wave–structure and soil–foundation interactions is developed. Natural frequencies and the corresponding modes are obtained. The effect of the wave–structure interaction, the added mass, the foundation stiffness, and the nacelle translational and rotational inertia on the motion of the structure is investigated. The nonlinear loading provided by the drag term of Morison’s equation is successfully handled. A parametric study to examine the effect of the structural parameters on the dynamic response is conducted, and the results of the proposed analytical solution are compared to numerical ones. The proposed method has the following advantages: (a) it is accurate and straightforward because of its analytical nature, (b) it does not ignore the drag term in the wave loading by keeping its nonlinearity nature, (c) the structure of the wind turbine is modeled as a continuous system, (d) it takes into account the effect of the rotational and translational inertia of the nacelle on the dynamic response, and (e) it provides an interpretation of the effect of the sea level variation in changing the natural frequencies.

Numerical analysis and comparison study of the 1:60 scaled DTU 10 MW TLP floating wind turbine

This paper presents numerical investigations and comparison to experiments for a scaled DTU 10MW Tension Leg Platform (TLP) floating wind turbine. Two state-of-the-art aero-servo-hydro-elastic models, HAWC2 and FAST, are used and compared with experimental results. This study applies the same controller, turbulent wind, and hydrodynamic input as in the experiments to the numerical simulations. First, detailed numerical model calibration and validation have been performed. Free decay experimental test results are compared. Next, the damping ratio is calibrated. The rotor thrust is matched to the measurements by adjustment of the blade pitch angle in both numerical models. The same controller used in the experiments is applied to the numerical model via a Dynamic Link Library interface. Detailed comparisons for regular waves, irregular waves, and a focused wave group impact are presented. Results for surge motion, nacelle acceleration, front mooring line tension, blade pitch and rotor speed are included. In general, both numerical models predict the floating wind turbine responses very well compared to the experimental results both in time and frequency domains. Owing to the use of a linear wave forcing model and Morison based viscous loads, however, the low-frequency surge motion is found to be under-predicted in both models.

Determination of the Response Amplitude Operator of a tidal turbine as a spectral transfer function

A transfer function determination method is proposed in this study to predict the unsteady fluctuations of the performance of a tidal turbine model. This method is derived from the Response Amplitude Operator (RAO) applied in the offshore industry to predict linear wave-induced loads on large structures. It is based on a spectral approach and requires the acquisition of a turbine parameter (e.g. torque, thrust, power or root-blade force) in synchronization with an upstream flow velocity measurement. On the frequency range where the causality between these two signals is proven, the transfer function is established using the ratio between the cross-spectral density and the spectral density of the incoming velocity.The linearity is verified using the coherence function, which shows validity for the turbine power in the lowest frequencies only. This transfer function is then used to reconstruct the power fluctuations which is compared to the recorded one for a particular flow condition with bathymetry generated turbulence. The result shows the dependence on the accurate location of the velocity measurement point used for the reconstruction. This point must exactly correspond to the expected turbine location, i.e. where the turbine response needs to be processed. Bearing in mind its limits, the method can be used to predict the loadings of extreme events on the turbine structure and the performance variations corresponding to the unsteady characteristics of a turbulent flow, for a better grid integration.

Dynamic response of monopile-supported wind turbines considering non-Gaussian characteristics of wind inflow

Available wind turbine design standards usually assume that the inflow wind field is a smooth Gaussian process, but the in-situ measured data show that the inflow wind presents a certain degree of non-Gaussian characteristics. This study investigates the dynamic response of the supporting structure of the wind turbine under both Gaussian and non-Gaussian wind fields. The Mises stress at the tower base and the mudline of the monopile are considered. Through the global maxima method and the average conditional exceedance rate method, the extreme value response of the turbine under a given mean recurrence interval is obtained. The results indicate that by taking the non-Gaussian characteristics of wind fields into account, the short-term extreme values are generally larger than those under Gaussian wind fields. For the long-term extreme values of the stress at the two critical points, the influence of kurtosis is significantly greater than the skewness. Meanwhile, the long-term stress extreme value also increases with the increase of kurtosis. The non-Gaussian characteristic of the wind field has a particular influence on the safe operation of the wind turbine and should be considered when designing wind turbines.

Sea states influence on the behaviour of a bottom mounted full-scale twin vertical axis tidal turbine

As only a few tidal turbine developers have reached full-scale installation, the community lacks clear feedback on the validity of development tools to predict in-situ behaviour. From 2019 to 2021, HydroQuest tested its first 1 MW capacity bottom mounted twin vertical axis tidal turbine (VATT) OceanQuest at the Paimpol-Bréhat site, off the coast of Brittany, France. Thus, two years of operational data are available to analyse the behaviour of the full-scale VATT at sea. After describing the environmental conditions and the average performance of the machine, the wave effect on the turbine response is studied. The presence of strong waves multiplies almost by a factor 3 the fluctuation intensities of the torque, the rotational speed of the rotors and the turbine drag, without affecting significantly the mean quantities. The strong coherence between the loads and velocity spectra in the wave frequencies range indicates that the increase of fluctuation intensities is directly due to the periodic loads of surface waves.

Optimal gust wind energy capture using critical tracking frequency for wind turbines

AbstractGusty wind in urban areas contains significant energy potential that can be harnessed, but the existing wind turbine rotor speed control systems based on continuous wind speed tracking have a noticeable response delay as compared to the duration of the common short gusts, inducing a significant deficit in harnessed excess energy content (EEC) from gusty wind. This work scrutinizes the energy content distribution among the gusty wind components and their differential impacts on the control response to identify factors to improve response timeliness. Cross‐correlation spectrum analysis between the wind turbine response and the local wind spectrum was used to identify the critical tracking frequency that can significantly reduce the average response delay of wind turbines in gusty wind conditions while maximizing the gust energy harvesting ability was developed and validated by various time‐domain simulation results. Combining the critical tracking frequency with the theoretical estimation of the EEC of a wind turbine, the expected EEC harnessing ability of a wind turbine can be estimated before installation. For the sample Savonius wind turbine in this work, the response delay of the wind turbine was shortened by 65%, leading to 25% more harnessed EEC from gusty wind.

Multisensory collaborative damage diagnosis of a 10 MW floating offshore wind turbine tendons using multi-scale convolutional neural network with attention mechanism

An effective damage diagnosis and prognostic management method can considerably reduce operation and maintenance costs of floating wind turbines. In this research, an intelligent damage diagnosis framework, named “MS-ACNN”, has been developed using a multi-scale deep convolution neural network model fused with an attention mechanism. The framework is used to detect, localize, and quantify existing and potential damages on multibody floating wind turbine tendons. The MS-ACNN framework is fitted with two multi-scale extractors, designed to capture multi-scale information from raw wind turbine response signals measured using multi-sensor. The attention mechanism uses weight ratios of extracted damage feature to enhance the MS-ACNN's capability in offering a better generalization in damage diagnosis. The framework's performance is examined under normal and noisy environments and with a diagnosis accuracy of 80%, which is higher than those obtained using most generic industrial grade diagnostic tools (MS–CNN–I, MSCNN-II, CNN, CNN-LSTM and CNN-BiLSTM) by at least 10%. The framework is also fitted with a Majority Weighted Voting rule to reduce false alarms and ensure optimum performance of the multi-sensor during collaborative diagnosis. Further examination shows that the inclusion of a voting rule increases the diagnostic performance's F1 index from 90% for single sensor and 84% for multi-sensor results to 94%.

Common Faults and Response of Auxiliary Steam Turbines in Thermal Power Plant

Common Faults and Response of Auxiliary Steam Turbines in Thermal Power Plant

A Computational Model to Simulate Thunderstorm Downbursts for Wind Turbine Loads Analysis

Abstract The generation of wind fields is of interest in the study of the structural performance of wind turbines in critical events, such as thunderstorm downbursts. Various methods ranging from the use of empirical data to employing computational simulations are typically adopted to study the response of wind turbines in downburst flow fields. While the former approach is limited in the ability to account for accurate and spatially resolved details of the flow field, the latter is expensive and, therefore, has limitations in its use. As an alternative, in this work, we propose a paused downburst model in which a snapshot of a time-dependent computational fluid dynamics (CFD) simulation is used to generate “mean” wind fields during thunderstorm downbursts. The developed model for the mean wind field is validated against recorded downburst data in the literature. The turbulent component of the wind field is generated using computationally inexpensive techniques based on Fourier-based power spectral density functions and coherence functions. In an illustrative example, the combined mean and turbulence wind fields are generated and applied on a utility-scale wind turbine to study structural load characteristics during a downburst event.

A Novel Probabilistic Method for Under Frequency Load Shedding Setting Considering Wind Turbine Response

The Under Frequency Load Shedding (UFLS) is the last automated action to restore power system frequency stability when a severe contingency causes a significant drop in the frequency of interconnection or islanded areas. In this paper, a novel probabilistic algorithm that considers the wind turbine frequency response is proposed to design and optimize the UFLS scheme. The uncertainties of power systems such as wind speed, hourly load, inertia time constant, and generation losses are taken into account. The Monte-Carlo Simulation (MCS) method is used to model the power system uncertainties. Additionally, a novel method is developed to calculate the power system frequency response and accelerate the multistage UFLS scheme optimization processes. In this method, a time-independent formulation for power system frequency response is utilized to reduce the computational complexity. More than Twenty different UFLS plans are designed using the proposed probabilistic algorithm. Besides, some time-domain simulations are carried on the modified IEEE 39-bus test system to investigate the performance of the designed UFLS schemes. Results indicate that designed UFLS schemes provide stable frequency response and improve the power system frequency stability.

Seismic vulnerability of jacket supported large offshore wind turbine considering multidirectional ground motions

Large numbers of offshore wind turbines in earthquake prone areas increase the scientific interest in analysing their seismic vulnerability. This study investigates the deformation and collapses susceptibility of a 10 MW jacket supported offshore wind turbine (OWT) considering different geometry of pile and jacket. A three-dimensional finite element model of the soil-pile-jacket-tower is developed in ABAQUS. A performance-based analysis is conducted to quantify the engineering demand parameters by choosing first mode spectral acceleration as the intensity measure for different pile length-to-diameter ratios and the base width of the jacket. The significance of considering higher modes and the vertical component of the earthquake motion on the dynamic response of offshore wind turbines is also discussed. Considerable amplification of acceleration is observed at the nacelle due to vertical excitation. In low to moderate earthquake shaking, jacket base width shows a marginal influence on the responses, while a significant impact is observed for strong earthquake shaking. Mudline rotation and acceleration response at the nacelle are the most critical parameters that govern the design under the serviceability limit state. Moreover, it is observed that initiation of the rocking mode of vibration for jacket-supported OWTs resting on a pile foundation is significantly less until its embedment length reduces considerably (3 to 5 m).

Virtual sensors for wind turbines with machine learning‐based time series models

AbstractModern wind turbines have multiple sensors installed and provide constant data stream outputs; however, there are some important quantities where installing physical sensors is either impractical or the sensor technology is not sufficiently advanced. An example of such a problem is, for example, sensing the shape and location of wake‐induced wind deficits caused by upwind turbines—a feature which would have relevant application in wind farm control; however, it is hard to detect physically due to the need of scanning the airflow in front of the turbine in multiple locations. Another control‐related example is monitoring and predicting the blade tip‐tower clearance. A “virtual sensor” can be created instead, by establishing a mathematical relationship between the quantity of interest and other, measurable quantities such as readings from already available sensors (e.g., SCADA, lidars, and met‐masts). Machine Learning (ML) approaches are suitable for this task as ML algorithms are capable of learning and representing complex relationships. This study details the concept of ML‐based virtual sensors and showcases three specific examples: blade root bending moment prediction, detection of wind turbine wake center location, and forecasting of blade tip‐tower clearance. All examples utilize sequence models (Long Short‐Term Memory, LSTM) and use aeroelastic load simulations to generate wind turbine response time series and test model performance. The data types used in the examples correspond to channels that would be available from high‐frequency SCADA data combined with a blade and tower load measurement system. The resulting model performance demonstrates the feasibility of the ML‐based virtual sensor approach.

Seismic response of large offshore wind turbines on monopile foundations including dynamic soil–structure interaction

This paper presents the results of a study on the dynamic and seismic response of the support structures of three reference Offshore Wind Turbines (OWT) of increasing rated power, founded to the seabed through monopile foundations. Thus, the structural behaviour of the NREL 5 MW, IEA Wind 10 MW and IEA Wind 15 MW reference OWTs under seismic input is analysed. To do so, a model based on the aero-hydro-servo-elastic OpenFAST open-software code, modified to include dynamic Soil–Structure Interaction (SSI) and input ground motion, is employed. Dynamic SSI phenomena are incorporated through lumped parameter models fitted to the impedances computed using an advanced boundary elements–finite elements model of the soil–foundation system in which the monopile is discretized as a steel pipe buried in the unbounded seabed. The fore–aft and side-to-side responses of the systems are computed under power production, parked and emergency shutdown operating conditions considering different earthquakes and arrival times. It is found that even low and moderate intensity earthquakes can produce significant increases in the structural demands of large OWTs. There exists a clear tendency for SSI to be beneficial when the size of the OWT increases.

Seismic response of offshore wind turbines supported on Monopiles and Suction Buckets: Numerical modelling and soft computing study

In this study, a coupled numerical modeling and soft computing was carried out to investigate the seismic response and liquefaction resistance of two common types of foundations used for offshore wind turbines (OWT), namely the Monopile and Suction Bucket Foundations under combined loading of wave, wind and earthquake. A series of fully coupled non-linear dynamic analysis was performed using FLAC-3D to produce the database required in the soft computing procedure by evaluating the effect of different factors such as foundation's geometrical properties (foundation's type and its aspect ratio), soil properties (relative density and depth of liquefiable layer) and the input ground motion parameters (frequency and amplitude). Parameters of SANISAND constitutive model were calibrated using centrifuge studies performed on OWTs. Afterwards, genetic algorithm (GA) was used to develop prediction models for prediction the seismic response parameters, especially the maximum value of tower's rotation, which was verified against the results of centrifuge experiments.

Fragility of offshore wind turbines variation with pulse-period and amplitude: Directivity and Fling step

This work evaluates the offshore wind turbine structure (OWT) subjected to the forward directivity and fling step types of pulse ground motion. The influence of the pulse period and amplitude of the forward directivity and fling step pulses are ascertained for the ground motion generated from closed-form analysis. The probabilistic seismic demand model (PSDM) approach is used to compute the offshore wind turbine response. The OpenSees tool is used to perform nonlinear time history analysis. Finally, fragility curves are derived for serviceability limits state of wind turbines. The influence of forward directivity and fling step pulse is evaluated and compared for the tower top and pile top rotation. Results shows that the OWT is more sensitive to the directivity pulses than fling step pulse for increased pulse period, while inversed trend observed for the pulse amplitude.

Tribo-Erosion Performance of GFRP Composite Panels in Both Offshore and Onshore Environmental Conditions

Abstract The erosion wear due to raindrop plays a vital role in the service life of wind turbine components, especially blades. Furthermore, the rain impact angle and the environmental conditions have a major impact on erosion wear. In the present work, the rain erosion performance of glass fiber reinforced polyester (GFRP) blade material in offshore and onshore environmental conditions has been studied. A whirling arm test rig was created and used to conduct the erosion tests on both offshore and onshore conditions. To simulate the offshore and onshore environmental conditions, experiments were carried out using both fresh and saltwater. The trail runs were carried out for time duration (30–90 min) at different impact angles (0—90 deg) by running the whirling arm tester at different impact velocities (30–70 m/s). During the experimentation, the impact velocity varied from 30 to 70 m/s. The impact angle was taken as 0 deg, 30 deg, 45 deg, 60 deg, and 90 deg, respectively. The results showed that erosion wear in the offshore conditions was more in all the cases as compared to onshore conditions. Furthermore, the maximum mass loss was at 45-deg impact angles for both conditions, which was further endorsed by Scanning Electron Microscope (SEM) analysis. Very little work has been reported on the optimization of erosion wear response of Offshore and Onshore wind turbines by using different design of experiment techniques. The erosion testing reveals that the GFRP blades exhibit a ductile erosion mechanism, which was further explained by using three-dimensional surface plots with the help of Image J software.

Wind turbine response in waked inflow: A modelling benchmark against full-scale measurements

Predicting the power and loads of wind turbines in waked inflow conditions still presents a major modelling challenge. It requires the accurate modelling of the atmospheric flow conditions, wakes of upstream turbines and the response of the turbine of interest. Rigorous validations of model frameworks against measurements of utility-scale wind turbines in such scenarios remain limited to date. In this study, six models of different fidelity are compared against measurements from the DanAero experiment. The two benchmark cases feature a full-wake and partial-wake scenario, respectively. The simulations are compared against local pressure forces and inflow velocities measured on several blade sections of the downstream turbine, as well as met mast measurements and standard SCADA data. Regardless of the model fidelity, reasonable agreements are found in terms of the wake characteristics and turbine response. For instance, the azimuth variation of the mean aerodynamic forces acting on the blade was captured with a mean relative error of 15–20%. While various model-specific deficiencies could be identified, the study highlights the need for further full-scale measurement campaigns with even more extensive instrumentation. Furthermore, it is concluded that validations should not be limited to integrated and/or time-averaged quantities that conceal characteristic spatial or temporal variations.

Aeroelastic Response of Wind Turbine Rotors under Rapid Actuation of Flap-Based Flow Control Devices

The largest commercial wind turbines today are rated at powers between 12 MW to 16 MW, with rotor diameters between 220 m to 242 m, which are expected to grow beyond 250 m in the near future. Economies-of-scale factors suggest the advantages of upscaling in rotor size to effectively harvest the wind potential. An increased emphasis on studies related to improvements and innovations in aerodynamic load-control methodologies has led researchers to focus on overcoming the bottlenecks in size upscaling. Though conventional pitch control is an effective approach for long-term load variations, their application to mitigate short-term fluctuations has limitations. This is directly associated with the cubical dependence on the weight of the rotor with increasing diameter. Alternatively, active flow-control devices (FCDs) have the potential to alleviate load fluctuations through rapid aerodynamic trimming. Fractional light-weight attachments such as trailing-edge flaps promise the swift response of such rapid fluctuations and require low power of actuation. The current study investigates the performance of active in dynamic load control for utility-scale wind turbines through an aeroelastic evaluation of the turbine response to control actions in short time-scales relevant to rapid load fluctuations. The numerical platform used in the analysis is designed to consider the complex multi-physics dynamics of the wind turbine through a self-adaptive Ordinary Differential Equation (ODE) algorithm that integrates the dynamics presented by control system in to the coupled response of aerodynamics and structural deformations of the rotor. The benchmark case in consideration is the use of fractional trailing-edge flaps used on blades designed for the NREL-5MW Reference Wind Turbine, originally designed by the National Renewable Energy Laboratory.

Deep Spatial-Temporal 2-D CNN-BLSTM Model for Ultrashort-Term LiDAR-Assisted Wind Turbine's Power and Fatigue Load Forecasting

Optimizing wind turbine performance is still a challenge due to the dynamic interactions between the spatially temporally stochastic wind fields and the wind turbine as a complex mechanical system. Recent cost reduction of remote sensing wind measurement technologies, such as light detection and ranging (LiDAR), has opened a new research area on the use of deep learning models for predicting wind turbine's responses. In this article, a LiDAR-aided deep learning model is presented to learn the powerful spatial-temporal characteristics from the input wind fields. In the proposed method, the combination of 2-D convolutional neural networks (CNNs) and bidirectional long short-term memory (BLSTM) units is used to capture high levels of abstractions in wind fields concurrently, and thus, forecasting wind output power and fatigue load as two representatives of wind turbine responses. The LiDAR wind preview information is used as the 2-D-images of wind fields for the CNN. Moreover, the BLSTM is incorporated with the proposed CNN to improve the forecasting accuracy further and learn deep temporal features. The aero-elastic 5-MW reference wind turbine of National Renewable Energy Laboratory (NREL) is used to evaluate the performance of proposed model compared to the state-of-the-art deep-learning-based architectures in the recent literature.

Response of 5 MW Floating Wind Turbines to Combined Action of Wind and Rain

For 5 MW floating wind turbines, the load response is significantly affected by wind and rain conditions. In order to reveal the relevant regularity of windblown rain and analyze the load response after being affected by the wind and rain, the rain phase is regarded as a continuous phase to be simulated. The self-compiled solver WARFoam (Wind and Rain Foam) is used to simulate the 5 MW wind turbines under wind and rain conditions. It is based on the Euler multiphase-model theory and the algorithm of unidirectional coupling of wind and rain. In this paper, the results of aerodynamic loads under WAR conditions are compared with the results of using the Lagrange particle-tracking model in order to prove that the Euler multiphase model can accurately calculate rain loads. On the basis of comparative verification, the convergence of the self-compiled solver is verified, which proves that the load-response analysis of the wind turbines under wind and rain conditions is accurate and efficient. The results show that rain has a significant impact on the load response of the wind turbines. Finally, the simulation results obtain the envelope diagram of the influence coefficient of rain-induced loads, which provides a quantitative reference standard for the calculation of the loads under wind and rain conditions.