Wind Turbine Model Research Articles

Wind Turbine Operation Status Monitoring and Fault Prediction Methods Based on Sensing Data and Big Bang–Big Crunch Algorithm

As wind power generation technology rapidly advances, the threat of wind turbine failures to the secure and stable operation of the power grid is gaining increasing attention. Real-time monitoring of operation status and predicting potential failures in wind turbines are indispensable requirements for the safe integration of wind power. In this paper, a model based on the least squares support vector machine (LSSVM), whose parameters are optimized by the Big Bang–Big Crunch algorithm, is constructed to improve the monitoring of wind turbine operation status and fault prediction accuracy. The research methodology consists of several key stages. Firstly, the initial wind turbine sensing data are preprocessed, utilizing factor analysis to reduce dimensionality and obtain the main influencing factors of wind turbine operation. Then, an improved failure prediction model for wind turbines, based on the least squares support vector machine, is developed using the preprocessed data. The model parameters are optimized by utilizing the Big Bang–Big Crunch optimization algorithm to enhance the prediction accuracy of wind turbine failures. Finally, the feasibility and accuracy of the proposed method are validated through a case study conducted on a regional power grid with wind farm integration.

Experimental Study on Identification of Aerodynamic Damping Matrix for the Tower of an Operating Wind Turbine by Artificial Excitation

ABSTRACTQuantitation of damping is of great significance for the design and condition assessment of wind turbines. The authors' previous theoretical and numerical studies showed that compared with damping ratios, a modal aerodynamic damping matrix can better describe the damping coupling in the fore‐aft (FA) and side‐side (SS) tower motions. In the present study, an improved damping identification method was first proposed to identify this damping matrix with artificial exciters and then verified by using OpenFAST simulations under different excitation frequencies, excitation force amplitudes, and turbulent wind fields. Following the numerical study, a scaled wind turbine model with a geometric scale ratio of 1/75 was carefully designed based on the NREL 5‐MW wind turbine prototype, in which the scaled blade design follows the rule in thrust coefficient similarity. An identification study was performed with this scaled model by a series of wind tunnel tests. The modal aerodynamic damping matrix was identified under steady‐state harmonic excitation in the operating state and compared with the identified results by a free decay method and the theoretical values. The results experimentally confirm the correctness of the aerodynamic damping matrix theory under uniform wind and the feasibility of the improved identification method in practice.

Deep analysis of power regulation on fatigue loads and platform motion in floating wind turbines

The fatigue loads experienced by components and the motion of the platform in floating wind turbines are likely influenced by their operation within a limited power state; however, the corresponding effects remain inadequately understood. This study investigates a 5 MW semi-submersible floating wind turbine, aiming to provide a comprehensive analysis of fatigue loads on critical components and platform motion under various power regulation modes. To achieve this objective, we employed the NREL 5 MW semi-submersible wind turbine model, generated three-dimensional wind fields using TurbSim, and conducted dynamic simulations with OpenFAST, utilizing MLife software for post-processing of fatigue load analysis. We assessed the fatigue loads of four key components—the blade root, tower base, drivetrain, and mooring cables—alongside the platform motion under varying generator speeds, yaw angles, and active power levels. Additionally, correlation analysis was performed to explore the relationship between component fatigue loads and platform motion under different yaw conditions. The results demonstrate that both generator speed and yaw angle significantly influence fatigue loads and platform motion, while the effect of active power appears to be relatively minor. Specifically, an increase in generator speed markedly elevates fatigue loads on the blade root and drivetrain, while concurrently reducing loads on the tower base and certain mooring lines. The analysis further reveals that, under various yaw angles, the fatigue loads on symmetrically positioned mooring cables exhibit a symmetric response, impacting fatigue damage in the blade root and tower base moments. Moreover, the relationship between maximum horizontal platform displacement and fatigue loads varies. The findings of this study provide critical insights into the operational optimization of floating wind turbines.

A smart multiphysics approach for wind turbines design in industry 5.0

This paper aims to develop a smart multiphysics approach for wind turbine design utilizing Industry 5.0. A new blade profile is developed and optimized by non-dominated sorting genetic algorithm II (NSGA-II) for shape design, and a 3D modeling of wind turbines is proposed. The aerodynamic modeling of a horizontal axis wind turbine (HAWT) is an important step in the design of wind turbines. The blade geometry design plays an important role in a wind turbine to maximize the aerodynamic performance and extract as much kinetic energy as possible from the wind resource. This paper addresses a high-level design and optimization for the parameters of a new blade. Moreover, a 3D modeling of large wind turbines (>7 MW) is proposed that can be used in wind farms. This approach can be used in real-time design in Industry 5.0 using different data from sensors. Finally, the optimized blade increases the produced power by 10% (from 7.5 MW to 8.2 MW). The proposed approach allows people to work alongside machinery to improve processes and provide personalization for companies manufacturing wind turbines.

Dynamic Formulation of the Double Multiple Stream Tube Model of Offshore Vertical Axis Wind Turbines

Abstract The paper proposes a novel algorithm to simulate Vertical Axis Wind Turbines in floating motion based on a low-fidelity approach. The conventional Double Multiple Stream Tube Model is formulated as a steady state model to deal with the inherent unsteady aerodynamics of VAWTs. The Double Multiple Stream Tube Model makes use of an average contribution of the blade passages over a revolution to solve the Blade Element Momentum equation. The model reduces the dependence of the solution (the induction field) to a space variable. Floating Vertical Axis Wind Turbines undergoes a variation of aerodynamic quantities according to wave periods, also different from the rotor period. This paper provides a new formulation of the Double Multiple Stream Tube Model to solve the turbine rotation in time and space, introducing the variation of the platform velocity and a revised approach to the downstream actuator disc. The most impactful parameter is found to be the wave frequency: wave periods at full-scale are lower than the ones of the turbine rotor, featuring optimal operating points at low tip speed ratios. The increase of the wave frequency highlights the differences of the average torque prediction for a single blade up to 10% between the unsteady and the standard formulation. In this context, the development of fast low-fidelity computational tools play a key role in the assessment of the preliminary designs of Floating Offshore Vertical Axis Wind Turbines and it will be used to optimise the aerodynamic design of the laboratory scaled model under surge motion.

Технико-экономические показатели энергокомплекса при использовании ветроэнергетических установок для покрытия собственных нужд ТЭЦ

In accordance with the Russia Energy Strategy until 2035, within the framework of the implementation of energy-saving technologies in the power-generating sector, it is planned to use hybrid energy, which is one of the ways of transition from conventional generation to generation with a high share of renewable energy sources. However, to implement such complexes, it is necessary to conduct research that considers the natural and climatic characteristics of the region, its resource capabilities, as well as the mutual influence of conventional and renewable energy sources in the structure of generating equipment capacities. The wind potential at the construction site has been analyzed to assess the feasibility of incorporating wind turbines. Based on the technical characteristics of wind turbines and meteorological conditions, the model of wind turbines to be installed has been selected. Technical and economic indicators of the energy complex operation, providing for the use of wind power plants to partially cover the house load of CHPs have been obtained. The indicators are determined on an annual, monthly basis using the physical method of attributing the total fuel costs for heat and electricity output in case of their combined production. Operation of wind power plants as a part of the energy complex with CHP in winter has a positive effect on the process of thermal efficiency of combined heat and power generation, and in summer, on the contrary, leads to deterioration of the efficiency indicators. Relatively small fuel savings per year when using wind power plants as a part of the energy complex with CHP does not allow to consider the project cost-effective. The obtained results should be considered when making system-wide decisions on the development of renewable energy sources in parallel operation with fossil fuel power plants.

Wave basin investigation of floating wind turbine model using underwater particle image velocimetry: investigating hydrodynamic wake structures

This paper conducts a fundamental study on the hydrodynamic performance of a floating offshore wind turbine (FOWT). This includes investigating the impacts of platform motion, turbine operation, and environmental conditions on hydrodynamic wake development behind the FOWT. Using underwater particle image velocimetry in a wave basin, the fluid flow behaviour behind the floating wind substructure model is compared for different test configurations. The FOWT is examined in three configurations: I. Zero degrees of freedom (fixed), II. Six degrees of freedom (floating), and III. Floating with turbine in operation (full system). The results reveal significant hydrodynamic wake turbulence differences between fixed and floating systems, with 95% and 89% increases in turbulent kinetic energy (TKE) and turbulence intensity (TI), respectively. Turbine loading in configuration III results in additional damping and reduces eddy shedding, resulting in a 26% and 31% decrease in TKE and TI, compared to the floating configuration II. It is found that neglecting turbine operation overestimates substructure drag terms by at least 30%, while fixing the model underestimates drag terms by 54%. This study highlights a trade-off between model accuracy and dynamic complexity when estimating viscous effects on FOWT. It also provides a valuable set of high-fidelity validation data for the development of numerical tools for FOWT analysis.

Dynamic strain field estimation of fixed offshore support structures using limited acceleration measurements

The strain history affecting the fatigue life of fixed offshore support structures is one of the most important topics in structural monitoring. However, some critical fatigue locations are located below the seabed, and the installation of strain gauges at these locations entails significant maintenance costs. In contrast, acceleration which reflects the vibration characteristics of the structure is an easily accessible and more stable physical parameter. Therefore, this study presents a strain estimation scheme that uses limited acceleration measurements to estimate the dynamic strain field of the fixed offshore support structures. An adaptive displacement reconstruction method is used to obtain the displacements at the master degrees of freedom, then the global displacements are obtained by the evolutionary orthogonal response expansion method, and finally, these displacements are implemented into the numerical software by redefining the boundary conditions to obtain the dynamic strain field. The advantage of the proposed method over the traditional strain-solving forward problem is that it relies solely on measured acceleration and structural geometry information, without requiring displacement, strain, or modal information. The correctness of the proposed method is verified by a numerical jacket platform model with multi-harmonic and non-stationary random excitations. Furthermore, physical experiments under eccentric rotation and hammering excitations are performed to assess the applicability of the proposed method on a wind turbine model equipped with laser displacement and fibre bragg grating sensors as reference measurements. The results show that the proposed method can accurately predict the dynamic strains of jacket platform and wind turbine support structures, while the appropriate agreement between the estimated strains and the numerical results, and the optical measured response is observed, with maximum estimation errors of 1.80% and 4.63%, respectively. Summarily, the application of the approach enables a new type of strain monitoring applicable to fixed offshore support structures.

Similarities in the meandering of yawed rotor wakes

This study investigated the meandering of yawed wind turbine rotor wakes, focusing on the similarities across different yaw angle scenarios. Spectrum analysis of velocity fluctuations reveals that the meandering of the yawed rotor wake is symmetrical about the wake center, despite its skewness. The non-zero lateral force of the yawed rotor enhances meandering in the lateral direction compared to the vertical direction. However, the lateral profiles of meandering strength exhibit similarities across different yaw angle scenarios, indicating a consistent wake meandering mode. The wake meandering frequency increases with the yaw angle. A relationship involving wake meandering frequency, drag coefficient, and yaw angle is formulated for wind turbine rotor wakes under different yaw angles. This relationship is also applicable to thin plate wakes within a certain range of inclination angles/yaw angles. The present study reveals the similarity in wake meandering characteristics across different yaw angle scenarios, which is instrumental in improving our understanding of wake meandering and in developing analytical wake models for wind turbines.

Strategic analysis of wind energy potential and optimal turbine selection in Al-Jouf, Saudi Arabia

Wind power is considered one of the most environmentally friendly and rapidly growing form of renewable energy. This study aims at assessment of wind power potential for Al-Jouf region in Saudi Arabia. A long term historical wind speed data of 21 years (2000–2021) was analytically modeled using Weibull distribution function to determine the wind characteristics. The Weibull parameters and average wind speed for monthly and yearly were evaluated using MATLAB program. An online wind power calculator developed by Meteotest was used to evaluate the wind energy by selecting various turbines. Six types of commercial wind turbines of 3 MW capacity were selected for wind power assessment to optimize the turbine selection. Our analysis showed that average wind speed varies between 3.88 m/s and 4.99 m/s and an overall average wind speed is 4.38 m/s. The most frequent wind speed observed was 3.9 m/s with a probability of 20 % approximately. The Weibull distribution parameters, shape parameter “k” values ranged between 1.89 and 2.21 with an average of 1.98 and scale factor “c” values varied between 4.41 and 5.66 m/s with a mean value of 4.86 m/s. Performance evaluations of selected wind turbine models reveal that the Vestas V126 turbine outperforms others at the Al-Jouf site, generating an annual energy yield of 3779400 kWh at a capacity factor of 14.4 %. These results suggest that by constructing a wind farm consisting of 100 V126 turbines may compensate for the energy needs of 46000 individuals. These findings establish Al-Jouf as a viable location for utility-scale wind power projects, offering valuable insights for turbine manufacturers, developers, operators, and policymakers interested in deploying large-capacity turbines in the region. The method used in this study for wind power analysis and turbine selection is simple and helpful to provide an initial assessment about the site suitability and turbine selection without undergoing exhaustive efforts.

Wind turbine foundation ring fatigue reliability analysis under wind-load using SCADA system

Under the action of wind load, the foundation ring of the fan will generate stress concentration and alternating stress, leading to fatigue failure. Based on the average wind speed obtained from the supervisory control and data acquisition (SCADA) system, the orthogonal expansion method of random pulsating wind field and the number theory point selection method are used to calculate and simulate the corresponding pulsating wind speed time series, and then calculate the wind speed time series and wind load time series. Based on the ABAQUS finite element software, a model of a 5 MW wind turbine is established. The modal analysis is used to obtain the modal vibration pattern and the intrinsic frequency to verify the reasonableness of the structural modeling. Subsequently, the wind load time series is applied to the wind turbine model to obtain the stress time series using instantaneous modal dynamic analysis (Modal dynamics). The stress time series at the location of maximum stress concentration in the foundation ring is extracted, and the stress time series with the same stress amplitude is obtained through conversion, which has Wiener characteristics. After obtaining the stress amplitude using the rainflow counting method, the fatigue reliability is calculated based on the structural fatigue reliability analysis method considering the threshold crossing duration of the random process. This research holds reference significance for the calculation of fatigue reliability under approximate working conditions.

Temperature rise of high-speed bearing in gearbox of 5 MW wind turbine based on Bayesian-LightGBM and improved PSO-SVM troubleshooting

5MW wind turbine gearbox high-speed bearing temperature rise failure is one of the important factors affecting the stable operation of the wind turbine, accurate prediction and timely diagnosis can effectively improve the efficiency of the wind turbine. In this paper, a combined modelling wind turbine gearbox high-speed bearing temperature rise prediction method based on Bayesian-LightGBM and improved PSO-SVM is proposed with a 5 MW wind turbine as the research object. Firstly, the initial dimensionality reduction of SCADA data is performed by sparse random projection matrix, which reduces the redundant data. Secondly, feature selection is performed on the remaining data using Bayesian-LightGBM to identify 13 key input feature parameters. Then, the hyperparameters of the PSO algorithm are optimised using Bayesian algorithm and further, the optimised PSO algorithm is applied to identify the SVM parameters. Finally, a simulation experiment platform is established based on MATLAB to verify the temperature rise of high-speed bearings in gearboxes of wind turbines by example calculation and comparative analysis. The results show that the model established in this paper is effective, the prediction results are accurate and the performance is stable, and then compared with the algorithms such as PSO-SVM, SVM, BP, etc, the coefficient of determination of the algorithm is greater than 0.994 in both the training set and the test set, and the average decision percentage error is around 1.88% in both.

Predictive Maintenance with Machine Learning: A Comparative Analysis of Wind Turbines and PV Power Plants

The transition to renewable energy requires innovations in new renewable energy sources, such as wind turbines and photovoltaic (PV) systems. Challenges arise in ensuring efficient and reliable performance in their operation and maintenance. Predictive maintenance using machine learning (PdM-ML) is relevant for addressing these challenges by enhancing failure predictions and reducing downtime. This study examines the effectiveness of PdM-ML in wind turbine and PV systems by analyzing operational data, performing data preprocessing, and developing machine learning models for each system. The results indicate that the model for wind turbines can predict failures in critical components such as gearboxes and blades with high accuracy. In contrast, the model for PV systems is effective in predicting efficiency declines in inverters and solar panels. Regarding operational complexity, each model has advantages and disadvantages of its own, but when compared to conventional maintenance techniques, both provide lower costs with greater operational efficiency. In conclusion, machine learning-based predictive maintenance is a promising solution for enhancing the reliability and efficiency of renewable energy systems.

The influence of the rotor spacing distance and rotating speed ratio on the power production of dual rotor wind turbines using a modified two-way interaction blade element momentum method

The present study aims to develop an accurate and fast improved blade element method (BEM) with two-way rotor-rotor interaction model which can account for two-way rotor-rotor interaction and the influence of rotor spacing while most previous BEM models for co-axis dual rotor wind turbines (DRWT) ignore the rear to front rotor interaction effect. A modified BEM model distinct from computationally intensive Computation Fluids Dynamics (CFD) model, is proposed for DRWTs. Validation of the present model is conducted by comparing it with experimental data from referenced literature for both co- and counter-rotating configuration. In our case study, for both co- and counter-rotating configurations, the power coefficient (CP) maps exhibit a double-hump shape instead of a single peak on the power output -λ curve observed in a single rotor wind turbine system. This characteristic may lead the DRWT to a local CPmax condition rather than the global CPmax while pursing the optimum operational condition. Although the counter-rotating DRWT can achieve a higher CPmax for most rotor spacing ratio (S/R) cases, it exhibits inferior power output than the single rotor for the S/R = 0.1 case. The present model can serve as a rapid and accurate model for configuring and control strategy evaluating DRWTs.

Highly Stable Lattice Boltzmann Method with a 2-D Actuator Line Model for Vertical Axis Wind Turbines

Highly Stable Lattice Boltzmann Method with a 2-D Actuator Line Model for Vertical Axis Wind Turbines

Development and verification of real-time hybrid model test delay compensation method for monopile-type offshore wind turbines

The real-time hybrid model (RTHM) test is adept at addressing the scale contradiction, the lack of fidelity in wind modelling in hydrodynamic testing facilities and spatial constraints inherent in conventional monopile-type offshore wind turbine (OWT) model testing methods, thus emerging as an effective avenue for conducting physical model tests of Monopile-type OWTs. This method entails the reproduction of aerodynamic loads or platform motions using loading device or vibration tables. Time delays in the physical attributes of the loading device and signal transmission processes within the system can result in error accumulation, with the potential to impact overall system stability. Moreover, time delay compensation algorithms for hybrid model test systems with force control loading can easily generate excessive noise, leading to system divergence. As a result, time delay has emerged as a technical challenge in the RTHM test. To address this issue, this paper has developed second-order and third-order polynomial extrapolation algorithms, alongside an adaptive compensation algorithm. The adaptive compensation algorithm employs the least squares method to identify parameters of the loading system, enabling it to address variations in the time delay of the experimental system caused by the nonlinearity of the loading system and changes in the physical properties of the model. The feasibility and effects of time delay compensation for various algorithms are validated through numerical simulation. Results indicate that the adaptive compensation algorithm surpasses second and third-order polynomial extrapolation compensation algorithms in terms of accuracy and compensation effectiveness. To validate the applicability of the adaptive compensation algorithm, a RTHM test was conducted. Across rotor thrust force (RotThrust) and tower top displacement, there was an average reduction of approximately 5 % and 9 % in the maximum and minimum synchronization errors, respectively. This highlights the efficacy of the delay compensation algorithm in practical applications, notably diminishing time delay errors within the experimental system. The adaptive compensation algorithm continuously adjusts and updates parameters, enhancing the adaptability of the compensation process to time-varying systems.

Independent Pitch Adaptive Control of Large Wind Turbines Using State Feedback and Disturbance Accommodating Control

Wind turbines experience significant unbalanced loads during operation, exacerbated by external disturbances that challenge the stability of the pitch control system and affect output power. This paper proposes an independent pitch adaptive control strategy integrating state feedback and disturbance accommodating control (DAC). Initially, nonlinear wind turbine dynamics are globally linearized, and DAC is applied to mitigate the impact of wind disturbances dynamically. Subsequently, the entire range of wind speeds is segmented, and controllers are individually designed to optimize gain settings according to specific control objectives at each wind speed interval. Scheduling parameters such as collective pitch angle and tower fore-aft displacement are identified and trained using Radial Basis Function Neural Networks (RBFNN). Finally, based on the output gain values determined by RBFNN, the full-state feedback controller group is adaptively adjusted, and the optimal controller is selected for the final output. Simulations conducted on the NREL 5MW reference wind turbine model using FAST and Simulink demonstrate that compared to the ROSCO controller, the proposed strategy ensures smoother output power and effectively reduces blade and tower loads, thereby extending the turbine’s operational lifespan.

Impact of wind turbine nominal power limitation over wind turbine blade remaining useful life and its economic consequences

The present paper investigates the effects of wind turbine nominal power limitation on the remaining useful life of turbine blades. It also looks at the economic impact of this limitation. In this context, the paper provides wind turbine owners and operators with an overview of how to potentially extend the remaining useful life of wind turbine blades and lays out the economic benefits that can be achieved via the modulation of nominal power. In investigating wind turbine blade damage, prior studies focused mainly on predictive models based on the 10 minutes SCADA data wind speed history, without however, trying to protract the remaining useful life of the blades. Only a handful of papers have explored the possibility of increasing the remaining useful life by adjusting the start-up and shutdown procedures with poor results. It would appear that wind turbine blade fatigue damage mainly increases when the wind turbine is in a power production regime, and the mechanical stresses associated with this regime are a function of the nominal power of the wind turbine. The present work therefore investigates the impacts of nominal power changes on both the remaining useful life of wind turbine blades and the economic value of the wind turbine in a bid to identify an optimal control mode. The wind turbine blade damage evaluation is based on 10 minutes SCADA data and the FAST simulation tool with the ultimate goal of providing wind turbine operators with an easy application. The damage evaluation is then applied considering different nominal power levels for the same wind turbine model in order to see the resulting impact on the remaining useful life. This project therefore takes a pioneering approach by proposing a remaining useful life optimization tool to wind turbine operators, in effect, a decision-making tool regarding which exploitation strategy to adopt.

Time-varying cost modeling and maintenance strategy optimization of plateau wind turbines considering degradation states

Plateau wind power has great potential in reducing carbon emissions; however, compared with other renewable energy, its economics still need to be improved. As an effective approach to enhance its economic feasibility, maintenance strategy optimization aims to reduce maintenance costs per kilowatt-hour and extend equipment lifespan. This paper proposes a multi-objective optimization model for the maintenance decision-making of plateau wind turbines that considers the degradation state. It incorporates: i) modeling the maintenance process of plateau wind turbines by combining time-based and state-based methods; ii) considering the time-varying maintenance costs in complex environments; and iii) employing a multi-objective optimization method to find the optimal strategy that meets maintenance requirements. The complexity considered in the model mainly includes the randomness of the operating duration for each equipment state, the temporal variability of equipment distribution and installation costs, and the uncertainty in maintenance effectiveness. The proposed optimization method is applied to a wind farm in the Yunnan-Guizhou Plateau, China.The results indicate that traditional maintenance strategies underestimate maintenance costs and equipment lifespan losses. Compared with conventional maintenance strategies, this method can reduce equipment maintenance costs by 24.07 % and extend its operating life by 11.58 %. Additionally, this paper has conducted a series of parametric analyses to enhance the generalization performance of the model. The proposed method effectively addresses the economic issues of plateau wind turbine maintenance and provides a valuable decision-making tool for guiding the long-term maintenance of wind turbines in complex environments.

Numerical Investigation of Wake Characteristics for Scaled 20 kW Wind Turbine Models with Various Size Factors

Wind energy is essential for sustainable energy development, providing a clean and reliable energy source through the wind turbine. However, the vortices and turbulence generated as wind passes through turbines reduce wind speed and increase turbulence, leading to significant power losses for downstream turbines in wind farms. This study investigates wake characteristics in wind turbines by examining the effects of different scale ratios on wake dynamics, using both experimental and numerical approaches, utilizing scaled-down models of the Aeolos H-20 kW turbine at scales of 1:33, 1:50, and 1:67. The experimental component involved wind tunnel tests in an open-circuit tunnel with adjustable wind speeds and controlled turbulence intensity. Additionally, Computational Fluid Dynamics (CFD) simulations were conducted using STAR-CCM+ (Version 15.06.02) to numerically analyze the wake characteristics. Prior to the simulation, a convergence test was performed by varying grid density and y+ values to establish optimized simulation settings essential for accurately capturing wake dynamics. The results were validated against experimental data, reinforcing the reliability of the simulations. Despite minor inconsistencies in areas affected by tower and nacelle interference, the overall results strongly support the methodology’s effectiveness. The discrepancies between the experimental results and CFD simulations underscore the limitations of the rigid body assumption, which does not fully account for the deformation observed in the experiment.