Wind energy harnessing of the NREL 5 MW reference wind turbine in icing conditions under different operational strategies

Zanon, Alessandro; Gennaro, Michele De; Kühnelt, Helmut

doi:10.1016/j.renene.2017.08.076

Cited by 69 publications

(57 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The iced cases were also analyzed numerically at angles of attack below stall [12]. In 2018, the ice accretion transient phenomenon and its effect on turbine performance was studied by coupling 2D steady state CFD, with a blade momentum and an ice accretion code [13].…”

Section: Introductionmentioning

confidence: 99%

Time-Dependent Effects of Glaze Ice on the Aerodynamic Characteristics of an Airfoil

Tabatabaei

Cervantes

Trivedi

2018

International Journal of Rotating Machinery

View full text Add to dashboard Cite

The main objective of this study is to estimate the dynamic loads acting over a glaze-iced airfoil. This work studies the performance of unsteady Reynolds-averaged Navier-Stokes (URANS) simulations in predicting the oscillations over an iced airfoil. The structure and size of time-averaged vortices are compared to measurements. Furthermore, the accuracy of a two-equation eddy viscosity turbulence model, the shear stress transport (SST) model, is investigated in the case of the dynamic load analysis over a glaze-iced airfoil. The computational fluid dynamic analysis was conducted to investigate the effect of critical ice accretions on a 0.610 m chord NACA 0011 airfoil. Leading edge glaze ice accretion was simulated with flat plates (spoiler-ice) extending along the span of the blade. Aerodynamic performance coefficients and pressure profiles were calculated and validated for the Reynolds number of 1.83 × 106. Furthermore, turbulent separation bubbles were studied. The numerical results confirm both time-dependent phenomena observed in previous similar measurements: (1) low-frequency mode, with a Strouhal number Sth≈0,013–0.02, and (2) higher frequency mode with a Strouhal number StL≈0,059–0.69. The higher frequency motion has the same characteristics as the shedding mode and the lower frequency motion has the flapping mode characteristics.

show abstract

Section: Introductionmentioning

confidence: 99%

Time-Dependent Effects of Glaze Ice on the Aerodynamic Characteristics of an Airfoil

Tabatabaei

Cervantes

Trivedi

2018

International Journal of Rotating Machinery

View full text Add to dashboard Cite

show abstract

“…Fatigue, Aerodynamics, Structures, and Turbulence (FAST) [25,26] is the major computational engineering tool for the U.S. National Renewable Energy Laboratory (NREL), it includes the hydrodynamic models for offshore forms, aerodynamic models, structural dynamics models, and control and electrical system dynamics models, to enable coupling nonlinear simulation to be implemented in the time domain.…”

Section: Fault Descriptionsmentioning

confidence: 99%

“…In this study, the FAST benchmark model of a 5 MW wind turbine with three blade horizontal variable speed proposed by [26] is used to obtain the historical operation data. The parameters of the WT are shown in Table 1 [27].…”

Section: Fault Descriptionsmentioning

confidence: 99%

“…The roughness factor is 0.01 m; the mean speed at hub height is 14 m/s; the wind type is logarithmic profile; and the intensity in the kaimal turbulence model is 10%. The FAST benchmark [26] is a SimuLink-based model with sealed FAST code, in which 15 sensors are used for the measurement of the monitoring variables, and white noise is added to all sensors. The detailed descriptions of each sensor and noise level are shown in Table 2, and the sensors' locations are shown in Figure 4.…”

Section: Fault Descriptionsmentioning

confidence: 99%

See 1 more Smart Citation

State Rules Mining and Probabilistic Fault Analysis for 5 MW Offshore Wind Turbines

2019

View full text Add to dashboard Cite

Research on fault identification for wind turbines (WTs) is a widespread concern. However, the identification accuracy in existing research is vulnerable to uncertainty in the operation data, and the identification results lack interpretability. In this paper, a data-driven method for fault identification of offshore WTs is presented. The main idea is to improve fault identification accuracy and facilitate the probabilistic sorting of possible faults with critical variables so as to provide abundant and reliable reference information for maintenance personnel. In the stage of state rule mining, representative initial rules are generated via the combination of a clustering algorithm and heuristic learning. Then, a multi-population quantum evolutionary algorithm is utilized to optimize the rule base. In the stage of fault identification, abnormal states are identified via a fuzzy rule-based classification system, and probabilistic fault sorting with critical variables is realized according to the fuzzy reasoning of state rules. Ten common sensor and actuator faults in 5 MW offshore WTs are taken to verify the feasibility and superiority of the proposed scheme. Experimental results demonstrate that the proposed method has higher identification accuracy than other identification methods and thus prove the feasibility of the proposed probabilistic fault analysis scheme. data mining technologies have made considerable progress [11]. Consequently, data-based methods have received increasing attention in the area of fault detection and location of WTs [12].In data-based research, substantial improvements have been proposed to improve the accuracy of fault identification. Chen et al. [13] proposed a fault prediction method for WTs using an adaptive neuro-fuzzy inference system with a priori knowledge. This method provides a fault instruction and allows the operator to determine the repair plan before the failure worsens. Hu et al. [14] proposed a fault diagnosis method that combines SVM and domain knowledge to improve the identification accuracy for faults in WTs. However, the aforementioned methods depend excessively on expert knowledge, and their identification results are simplistic. Wang et al. [15] proposed an FDI scheme based on a variable selection method using principal component analysis; in this scheme, the result of fault identification is determined by the signal contribution, but the method can only identify an abnormal signal rather than a fault type. Ruiz et al. [16] proposed a fault classification method for WTs that transforms the domain signals of WTs into two-dimensional matrices. Certain similar faults are merged for identification in the experiment, and the separation of similar faults is unwarrantable. Pashazadeh et al. [17] obtained the fault data of WTs from the Fatigue, Aerodynamics, Structures, and Turbulence (FAST) simulator and Simulink (in a MATLAB environment) and developed an FDI scheme on the basis of classifier fusion, in which four classifiers are implemented in parallel. However, this m...

show abstract

“…Such better modeling is seen in Reference [26] where a stochastic approach was applied to model energy conversion and power production from a wind farm under various wind conditions including turbulent wind fluctuations. In another dimension, Reference [27] combined a computational fluid dynamic (CFD) approach with blade element momentum (BEM) theory to propose operating conditions for wind turbines during icing conditions.…”

Section: Introductionmentioning

confidence: 99%

Assessment and Performance Evaluation of a Wind Turbine Power Output

Abolude

Zhou

2018

Energies

View full text Add to dashboard Cite

Estimation errors have constantly been a technology bother for wind power management, often time with deviations of actual power curve (APC) from the turbine power curve (TPC). Power output dispersion for an operational 800 kW turbine was analyzed using three averaging tine steps of 1-min, 5-min, and 15-min. The error between the APC and TPC in kWh was about 25% on average, irrespective of the time of the day, although higher magnitudes of error were observed during low wind speeds and poor wind conditions. The 15-min averaged time series proved more suitable for grid management and energy load scheduling, but the error margin was still a major concern. An effective power curve (EPC) based on the polynomial parametric wind turbine power curve modeling technique was calibrated using turbine and site-specific performance data. The EPC reduced estimation error to about 3% in the aforementioned time series during very good wind conditions. By integrating statistical wind speed forecasting methods and site-specific EPCs, wind power forecasting and management can be significantly improved without compromising grid stability.

show abstract

Wind energy harnessing of the NREL 5 MW reference wind turbine in icing conditions under different operational strategies

Cited by 69 publications

References 16 publications

Time-Dependent Effects of Glaze Ice on the Aerodynamic Characteristics of an Airfoil

Time-Dependent Effects of Glaze Ice on the Aerodynamic Characteristics of an Airfoil

State Rules Mining and Probabilistic Fault Analysis for 5 MW Offshore Wind Turbines

Assessment and Performance Evaluation of a Wind Turbine Power Output

Contact Info

Product

Resources

About