Wind Turbine Model Research Articles

Dynamic reliability estimation of onshore wind turbine tower based on frequency-domain method

With the upscaling of wind turbine, the issue of dynamic reliability of wind turbine towers has become increasingly evident. However, the complexity and nonlinearity of wind turbine dynamics present significant challenges in evaluating their dynamic reliability under stochastic loads, requiring substantial computational resources. In this paper, a computationally cost-effective frequency-domain method is proposed as a means of estimating the dynamic reliability of an onshore wind turbine tower. An analytical approach is employed to obtain the power spectral density (PSD) of the rated wind speed and load on the rotating blades. Secondly, a dynamic model of onshore horizontal axis onshore wind turbine is established using flexible multi-body dynamics (MBD). The linear time-varying dynamic equation is transformed into a linear time-invariant form through the application of statistical linearization method. Subsequently, complex modal analysis is used to employ the displacements and internal forces of the tower. Moreover, the first-passage reliability and fatigue reliability of the wind turbine tower are determined through power spectral analysis. A numerical example of a 5-MW wind turbine is presented to demonstrate that the proposed method yields results in good agreement with Monte Carlo simulations (MCS), thereby demonstrating its effectiveness. The reliability analysis of the 5-MW wind turbine reveals that over a 20-year design life, the first-passage reliability is 0.9997 when adopting fore-after displacement at the tower top as the failure criterion, and the fatigue reliability of the tower base is 0.9445.

Analytical modeling of wind-turbine wakes behind an abrupt rough-to-smooth surface roughness transition

Numerous wind farms are planned and built in the coastal or forest-to-grassland transition areas with abrupt rough-to-smooth surface roughness change. Behind the abrupt change, the atmospheric boundary layer (ABL) undergoes a complex transition process which brings big challenges to the canonical wake models of wind turbines. To this end, we employ large eddy simulation (LES) to investigate the development of the ABL and the evolution of wind-turbine wakes at different positions under roughness abruption from rough to smooth, and propose a novel analytical wake model. Due to the abrupt change of surface roughness, pressure gradient forms around the abruption and the internal boundary layer (IBL) develops downstream. The wind turbine near the abruption point is influenced by the pressure gradient, resulting in smaller wake width, while those situated within the IBL are significantly affected by the flow transition, resulting in systematic differences in wake recovery. To explicitly account for the flow transition in the wake model, we introduce an equivalent additional thrust to represent the momentum contribution caused by both background velocity and Reynolds stress. A detailed budget analysis is then conducted around the wind turbine and shows that the equivalent additional thrust is highly correlated with the streamwise turbulence intensity. Finally, a new wake model under roughness abruption is developed and compared with the LES data. Results show that the proposed model demonstrates superior performance over the existing models.

A double-Gaussian wake model considering yaw misalignment

A wake steering has been known to effectively increase wind farm production by deflecting the upstream turbines’ wakes via yaw misalignment, thus minimizing their negative impacts on the downstream turbines' performances. This study presents analytical modeling of horizontal-axis wind turbine (HAWT) wake using low-cost analytical modeling as an alternative to expensive numerical and experimental trials. The existing double-Gaussian (DG) analytical wake model was modified to include the yaw misalignment effect, allowing its usability for the yawed HAWT wake modeling. The benchmark dataset produced by high-fidelity large eddy simulation (LES) of wake flowfields behind the turbine with yaw angles of 0º, 10º, 20º, and 30º were used to validate the accuracy of the DG yaw wake model. Overall, the DG yaw wake model predictions showed good agreement with the benchmark dataset under varying HAWT rotor yaw configurations. The analytical results verified by the LES dataset confirm the effectiveness of yaw misalignment in deflecting the wake trajectory, expediting the wake recovery downstream of the HAWT. In addition, a higher rotor yaw angle improves the wake recovery rate in the prevailing wind direction. Notable deviations against the benchmark dataset were found mainly within the near-wake region owing to flow acceleration arising from turbine-induced turbulence. As a result, the model’s predictions were slightly lower than the benchmark dataset, most likely due to neglecting the acceleration term in the analytical model derivation. Otherwise, the analytical model could accurately predict the mean wake velocity within the far-wake region for all evaluated cases, demonstrating its reliability in estimating wind speed potential within a practical distance for micrositing. These results were also proved quantitatively by statistical evaluations utilizing root mean square error (RMSE) and Pearson correlation coefficient R. The present study points out the importance of the upstream HAWTs’ rotor yaw controls to properly deflect their wakes away from their mainstream trajectories, thus effectively maximizing the wind speed potentials extracted by the downstream HAWTs and improving the overall wind farm production.

Fractional order control of Wind Energy Conversion System based on DFIG

The paper deals with the elaboration of a robust and efficient decoupled active and reactive powers control algorithm of double-fed induction generator (DFIG) using fractional order control based on proportional and integral controllers. First, the models of wind turbine and electrical generator are established, as well as a decoupled active and reactive powers control of DFIG. Second, the used fractional order controllers are designed using an analytical design method based on frequency domain specifications, namely phase margin, robustness to gain variations and gain limitation at the crossover frequency. Third, to evaluate the fractional order controller control performances, simulation results are presented and discussed for all the operating region of the WECS. Then, simulation results are presented and interpreted considering a variable wind speed in order to reach all the operating regions of the WECS. Finally, a conclusion is given in the light of the obtained results.

Optimizing microgrid integration of renewable energy for sustainable solutions in off/on-grid communities

Rising energy costs, climate change impacts, and transmission losses have increased demand for renewable energy sources and decentralized solutions. As more people seek smart living and working environments, integrated smart microgrids powered by hybrid renewable systems have become attractive solutions for off-grid and on-grid communities. This study proposes designing a solar-wind-battery hybrid microgrid supplying a medical load et al.-Ain Al-Sokhna, Egypt. The optimization objectives aim to minimize the loss of power supply probability (LPSP %) and the levelized cost of energy (LCOE, $/kWh). A key consideration when designing and optimizing hybrid microgrids is the energy management strategy, which coordinates different generation sources and fluctuating load demand. Therefore, optimization algorithms were applied to balance energy flows while meeting loads, mitigating weather impacts, and preventing overcharging/deep discharge of battery storage. Models of wind turbines, photovoltaic panels, and battery storage were developed to simulate and analyze proposed microgrid operations. A multi-objective optimization approach evaluated LPSP and LCOE metrics using transit search, grey wolf, and particle swarm algorithms to find optimal system configurations. The optimization algorithms demonstrated varying performances in minimizing the multi-objective functions for the on-grid and off-grid microgrids. The particle-swarm optimization technique is the best solution for the off-grid system, which contains PV, wind, and battery storage, with a minimum LCOE of 0.3435 $/kWh and an LPSP of 4.5334%. Meanwhile, the transit-search optimization algorithm found the optimal solution for the on-grid configuration according to the objective function, yielding an LCOE of 0.116 $/kWh and an LPSP value of 3.0639 × 10−16. Statistical analysis confirmed that the algorithms generally exhibited stable and robust optimization capabilities. Of the methods, transit search was the most effective overall optimization approach.

Modeling of a horizontal axis wind turbine with the QBlade simulator and NACA airfoils

This paper proposes the modeling of a horizontal axis wind turbine (HAWT), with the QBlade simulator, using the NACA airfoils, for this purpose we use the QBlade simulator, and the NACA airfoils, scaling the geometry, in order to use the wind tunnel of the school of mines of the UNSA, with a diameter of 0.45 m, once designed with these tools, we proceed to the 3D printing of the blades in the school of Architecture of the UNSA, and the model is built, to submit it to the input variables: wind speed, air density, wind angle of attack, and as output variables angular velocity, and torque on the shaft; with these data, the information is analyzed, in order to establish the conclusions, and recommendations: The modeling with the QBlade simulator and the NACA airfoils was satisfactory, so it is recommended to use it in the design of the (HAWT).

Design process for scaled model wind turbines using field measurements

Abstract This paper presents a method for designing wind turbine-scaled models based on field data. The scaling process requires careful consideration of the system's physical laws and similarity criteria. Scaling methods depend on the prototype's operating and experimental conditions (scaled model dimensions and wind tunnel capabilities). The data from a field turbine is the input for creating a scaled model and testing it in a wind tunnel. There are few field data available. The task is not straightforward since most operating conditions must be satisfied, and the similarity criteria could result in different scaled models and wind tunnel conditions. The drawback of the scaling models is their inability to reproduce all the operating conditions. The method presented here considers most of the recommended similarities, including the dynamic modeling of the system and two additional similarities, one related to the blade's natural frequencies and the other to the rotor's moment of inertia. The additional similarities allow the designers to define the blade's mass and stiffness. The final scaled-model design was obtained by modifying the blade profile to reproduce similar power, pitch momentum, and trust coefficients, keeping the same twist angle as the prototype. Since the possible profiles are too large, the best approximation was obtained by comparing the coefficient curves of different blade profiles with the prototype's curves. The curves were calculated using the BEM method. It was found that the scaled model has an entirely different design.

Pitch control for floating offshore wind turbines via model-dependent extended state observer-based active disturbance rejection control

Various types of disturbances bring significant challenges to the stable operation of floating offshore wind turbines. This paper formulates the pitch control of floating offshore wind turbines using a novel model-dependent extended state observer-based active disturbance rejection control, which can effectively estimate and compensate for total disturbances. Firstly, a Markov model is proposed to describe wind and wave combined external disturbances and the aerodynamic characteristics of floating offshore wind turbines. Then, an active disturbance rejection control is developed based on the constructed model-dependent extended state observer. The approach provided in this paper can adjust the pitch angle to ensure that the generator speed output tracks the desired reference value. Based on this, the multi verse optimization algorithm is introduced to optimize the parameters of the controller. Finally, the superiorities of the results presented in this paper are illustrated by a 5 MW floating offshore wind turbine model. Simulation results demonstrate that the model-dependent extended state observer-based active disturbance rejection control strategy can significantly adjust the pitch angle to guarantee the stable operation of floating offshore wind turbines under wind and wave combined external disturbances.

Advancing Offshore Wind Turbine modeling: developing an integrated tool for structural control

This paper presents the modeling of a 22 MW OWT (IEA Wind 22-Megawatt Offshore Reference Wind Turbine), intended for integration into a new MATLAB-based user-friendly interface. Users can select from turbines ranging from 5 to 22 MW, inputting relevant data on wind and maritime conditions, and other relevant data. Significantly advancing prior research utilizing a 5 MW NREL OWT, this study encompasses hydrodynamic and aerodynamic modeling of a flexible tower to assess response peaks at the OWT hub, evaluated via Power Spectral Density analysis. The routine models the tower, incorporating a rotational spectrum to account for blade rotation effects. The developed tool may include the incorporation of vibration absorbers for structural control, such as pendulum, inertial, or liquid column absorbers, among others.

Power regulation of a wind farm through flexible operation of turbines using predictive control

Wind farms are becoming larger, presenting opportunities to improve their efficiency and effectiveness. Although wind farm control can be used to adjust a wind farm’s power output to meet grid requirements, its development is restricted by the limited flexibility of wind turbine operation. Wind turbines are typically designed to operate at the power output determined by the wind speed, making it difficult to adjust their power output quickly and easily in response to changes in grid demand. A new approach to wind farm control that provides full flexibility for both wind farms and turbines is proposed. This method adjusts the wind farm’s power production using predictive control of each turbine to meet the grid demands. The power generated by each turbine is adjusted to achieve this, keeping fluctuation in the wind farm power output low. A wind farm simulation is conducted under varying wind speeds using the discretized C MEX Matlab/SIMULINKR○ model of the 5 MW Supergen turbine and the wind turbine model from NREL. The results demonstrate that the wind farm power output tracks a reference value that can be set by the grid. The results are also analyzed on the torque/speed plane, and they indicate that the turbines operate within their specified safe operating zones under both normal and gusty wind operating conditions at the same time.

Development of Savonius Type Wind Turbine Model as A Source of Renewable Electrical Energy

Abstract Wind turbines can be used as a renewable energy resource, especially in remote areas and outer islands where there is no electricity. Based on the calculations that have been carried out, it is found that the dimensions of the wind turbine model used in this research are a turbine height of 1 meter with a diameter of 0.5 meters. Meanwhile, the dimensions of the shaft used are 20mm. Model testing has been carried out in Juanda Surabaya, it is known that the generator power produced increases along with increasing wind speed which is also influenced by the rotation of the wind turbine. In the results of this research, the largest generator power was produced at a wind speed of 4.8 ms−1, which produced a generator power of 12.6 watts. Meanwhile, the lowest power is 0 watts because a wind speed of 0.4 m/s is not able to rotate the wind turbine. From the data collection results, it was also found that the SCC could only charge the battery if the minimum voltage reached 13 volts. So the new SCC can charge the battery at a wind speed of 3.7 m/s with a voltage output of 13.1 volts and a current strength of 0.5 amperes. This research will use a Savonius wind turbine with 2 rotor blades with dimensions of 1 meter high and 0.5 meter diameter. The research results show that power is obtained at a generator voltage starting from 13.1 volts which produces a current strength of 0.7 amperes. It is at this voltage that the new Solar/Wind Charge Control can charge the battery.

Small-Signal Modeling of Grid-Forming Wind Turbines in Active Power and DC Voltage Control Timescale

Small-Signal Modeling of Grid-Forming Wind Turbines in Active Power and DC Voltage Control Timescale

Analysis of Bifurcation Characteristics of Fractional‐Order Direct Drive Permanent Magnet Synchronous Generator

ABSTRACTIn this study, we establish a fractional‐order direct drive permanent magnet synchronous wind turbines (DPMSG) model defined by Caputo based on the fractional calculus theory to overcome the singularity and limitations of integer‐order DPMSG models. The path and characteristics of the DPMSG system entering the bifurcation and chaos caused by the internal parameter changes and external disturbances were analyzed. First, we established a nonlinear fractional‐order mathematical model of a DPMSG system. Second, a bifurcation diagram was drawn using the maximum algorithm, and the path to chaos of the system at different orders was analyzed by combining its chaotic phase portrait and temporal sequence diagram. Subsequently, the impact of variations in the system order on the chaotic features of the original system was analyzed. The internal parameter adjustments of the system and changes in the system stability under external disturbances and other external excitations were analyzed. The influence of the system on its bifurcation phenomenon and chaotic behavior under multidimensional orders was determined, and it was observed that its path into chaos was opened by period‐doubling bifurcation. Lastly, the dual‐parameter stability domain of the system order corresponding to the internal parameters of the system was obtained by determining the parameter conditions for the critical stability of the system.

Real-time fatigue assessment of Floating Offshore Wind Turbine Mooring employing sequence-to-sequence-based deep learning on indirect fatigue response

Mooring lines in Floating Offshore Wind Turbines (FOWT) are subjected to significant environmental loading, leading to fatigue degradation over prolonged usage which necessitates real-time monitoring to ensure operational safety. Traditional fatigue assessment measures stress fluctuations under operational loads with expensive underwater sensors, which are costly and complex to install, especially for monitoring mooring at higher depths. This eventually increases the levelized cost of energy and maintenance expenses. This study proposes a cost-efficient fatigue monitoring method for catenary mooring using a novel sequence-to-sequence deep-learning (DL) approach that leverages platform motions as indirect fatigue response to infer fatigue assessment. By correlating FOWT platform motions with their impact on the fatigue life of mooring lines, the proposed method offers an alternative to costly underwater sensor measurements. In the absence of real data, the DL model is trained using simulated datasets for an OC4 semi-submersible wind turbine model in the OpenFAST open-source simulation package, capturing a wide range of tension fluctuations under diverse metocean conditions. Accordingly, this synthetic data has been treated as “real data” throughout this study. The trained network effectively captures the nonlinear relationship between platform motion and mooring fatigue response in real-time, enabling efficient assessment of fatigue-induced damage on mooring lines.

Wind energy potential assessment using the Weibull distribution method for future energy self-sufficiency

This study develops a methodological approach to achieve energy self-sufficiency in various geographical contexts, using the province of Taza, located in northeastern Morocco, as a representative case study. The province has significant energy potential with a strategic location and abundant renewable resources, particularly wind energy. This research aims to address the shortcomings in energy self-sufficiency in the Taza province by evaluating the wind energy potential of five distinct areas. A rigorous statistical analysis was conducted using the Weibull distribution and several parameter estimation algorithms: Maximum Likelihood Method, Jestus Method, Lysen Method, Moroccan Method, and Graphical Method. These techniques allowed the determination of key parameters, including the shape and scale factors, essential for accurately characterising wind potential. The main indicators used for the evaluation included the annual energy production and the capacity factor of the sites. The results reveal that zone 3, named Taza, exhibits the highest wind energy potential, with a capacity factor of 30.84 % and an annual energy production of 9.18 GWh at a height of 100 m. The GoldWind GW 140/3.4 wind turbine model was found to be optimal for all the areas studied. Furthermore, the study explores potential impacts on electricity consumption and provides projections for the necessary deployment of GW 140/3.4 wind turbines until 2030 to enhance the energy self-sufficiency of the province. This research contributes significantly to provincial energy planning by demonstrating the effectiveness of statistical methods in assessing wind potential while offering strategic recommendations for integrating optimised wind turbines to strengthen local energy self-sufficiency.

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.

Hierarchical dynamic wake modeling of wind turbine based on physics-informed generative deep learning

With the increasing demand for electric power, the size of wind farms is becoming much larger than ever before. Power and load prediction are two of the most essential topics in wind farm layout optimization. Traditional wake modeling methods, such as analytic models and CFD simulations, struggle to handle such large-scale problems accurately and efficiently. In this study, a novel hierarchical dynamic wake modeling approach for wind turbines using generative deep learning, PHOENIX (PHysics-infOrmed gEnerative deep learniNg for hIerarchical dynamic wake modeling eXploration), is proposed to capture the spatial–temporal features of the unsteady wake field in wind turbine clusters. The dynamic wake meandering (DWM) model is employed to generate the corresponding datasets for training, testing, and validating the deep learning-based wake prediction framework. This research is expected to accelerate the prediction process and improve accuracy, and it can be further applied to wind turbine design and wind farm layout optimization.

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.

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.