17.6.4.1 Wind Turbine Wakes, Wind Turbines

Chapter Contents (Back)
Wind Turbines. Turbine Wake. Includes papers not related to visual analysis, but all the wind turbine related work.

Christidis, T.[Tanya], Law, J.[Jane],
Mapping Ontario's Wind Turbines: Challenges and Limitations,
IJGI(2), No. 4, 2013, pp. 1092-1105.
DOI Link 1402
BibRef

Wu, S.H.[Song-Hua], Yin, J.P.[Jia-Ping], Liu, B.Y.[Bing-Yi], Liu, J.T.[Jin-Tao], Li, R.Z.[Rong-Zhong], Wang, X.T.[Xi-Tao], Feng, C.Z.[Chang-Zhong], Zhang, K.L.[Kai-Lin],
Coherent Doppler lidar to investigate wind turbulence,
SPIE(Newsroom), December 24, 2014
DOI Link 1501
Characterizing the turbulent wake of wind turbines enables their optimal arrangement in a wind farm, potentially increasing power output. BibRef

Doubrawa, P.[Paula], Barthelmie, R.J.[Rebecca J.], Wang, H.[Hui], Pryor, S.C., Churchfield, M.J.[Matthew J.],
Wind Turbine Wake Characterization from Temporally Disjunct 3-D Measurements,
RS(8), No. 11, 2016, pp. 939.
DOI Link 1612
BibRef

Kim, H.G.[Hyun-Goo], Jeon, W.H.[Wan-Ho], Kim, D.H.[Dong-Hyeok],
Wind Resource Assessment for High-Rise BIWT Using RS-NWP-CFD,
RS(8), No. 12, 2016, pp. 1019.
DOI Link 1612
Wind flow analysis for integrating wind turbine on a high-rise building. BibRef

Uysal, F., Selesnick, I., Isom, B.M.,
Mitigation of Wind Turbine Clutter for Weather Radar by Signal Separation,
GeoRS(54), No. 5, May 2016, pp. 2925-2934.
IEEE DOI 1604
Fourier transforms BibRef

van Dooren, M.F.[Marijn F.], Trabucchi, D.[Davide], Kühn, M.[Martin],
A Methodology for the Reconstruction of 2D Horizontal Wind Fields of Wind Turbine Wakes Based on Dual-Doppler Lidar Measurements,
RS(8), No. 10, 2016, pp. 809.
DOI Link 1609
BibRef

Kumer, V.M.[Valerie-Marie], Reuder, J.[Joachim], Eikill, R.O.[Rannveig Oftedal],
Characterization of Turbulence in Wind Turbine Wakes under Different Stability Conditions from Static Doppler LiDAR Measurements,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Beauchamp, R.M., Chandrasekar, V.,
Suppressing Wind Turbine Signatures in Weather Radar Observations,
GeoRS(55), No. 5, May 2017, pp. 2546-2562.
IEEE DOI 1705
Doppler radar, geophysical signal processing, mission effectiveness, precipitation, radar clutter, radar echo, radar system, turbine radar signature, vegetation, weather radar observation, wind turbine signature, Clutter, Doppler radar, Meteorological radar, Meteorology, Radar cross-sections BibRef

Beauchamp, R.M., Chandrasekar, V.,
Characterization and Modeling of the Wind Turbine Radar Signature Using Turbine State Telemetry,
GeoRS(55), No. 9, September 2017, pp. 5134-5147.
IEEE DOI 1709
electromagnetic wave scattering, radar cross-sections, telemetry, wind turbines, X-band radar observation, scattering theory, turbine radar signature suppression, turbine state telemetry, utility-scale wind turbine, Meteorological radar, Radar cross-sections, Telemetry, radar signal processing, spectral, analysis
See also Dual-Polarization Radar Characteristics of Wind Turbines With Ground Clutter and Precipitation. BibRef

Fuertes, F.C.[Fernando Carbajo], Markfort, C.D.[Corey D.], Porté-Agel, F.[Fernando],
Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Beck, H.[Hauke], Kühn, M.[Martin],
Reconstruction of Three-Dimensional Dynamic Wind-Turbine Wake Wind Fields with Volumetric Long-Range Wind Doppler LiDAR Measurements,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Zhou, K.[Kai], Cherukuru, N.[Nihanth], Sun, X.Y.[Xiao-Yu], Calhoun, R.[Ronald],
Wind Gust Detection and Impact Prediction for Wind Turbines,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805

See also Comments on Wind Gust Detection and Impact Prediction for Wind Turbines. BibRef

Mayor, S.D.[Shane D.], Dérian, P.[Pierre],
Comments on 'Wind Gust Detection and Impact Prediction for Wind Turbines',
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811

See also Wind Gust Detection and Impact Prediction for Wind Turbines. BibRef

Brugger, P.[Peter], Fuertes, F.C.[Fernando Carbajo], Vahidzadeh, M.[Mohsen], Markfort, C.D.[Corey D.], Porté-Agel, F.[Fernando],
Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Fuertes, F.C.[Fernando Carbajo], Porté-Agel, F.[Fernando],
Using a Virtual Lidar Approach to Assess the Accuracy of the Volumetric Reconstruction of a Wind Turbine Wake,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Aird, J.A.[Jeanie A.], Quon, E.W.[Eliot W.], Barthelmie, R.J.[Rebecca J.], Debnath, M.[Mithu], Doubrawa, P.[Paula], Pryor, S.C.[Sara C.],
Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Deng, Y.X.[Yu-Xin], Zhang, M.[Min], Jiang, W.Q.[Wang-Qiang], Wang, L.T.[Le-Tian],
Electromagnetic Scattering of Near-Field Turbulent Wake Generated by Accelerated Propeller,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
BibRef


Bakri, A.E., Sefriti, S., Boumhidi, I.,
A sensor fault detection scheme of DFIG-based wind turbine using deep auto-encoder approach,
ISCV20(1-6)
IEEE DOI 2011
asynchronous generators, electric current measurement, fault diagnosis, measurement errors, neural nets, Deep learning BibRef

Nietiedt, S., Goering, M., Willemsen, T., Luhmann, T.,
Measurement of Fluid-structure Interaction of Wind Turbines in Wind Tunnel Experiments: Concept And First Results,
Optical3D19(143-149).
DOI Link 1912
BibRef

Uti, M.N., Din, A.H.M., Omar, A.H.,
Assessment of Seasonal Variability for Wind Speed And Significant Wave Height Using Satellite Altimeter Over Malaysian Seas,
GeoDisast18(153-158).
DOI Link 1901
BibRef
Earlier:
Reliability of Wind Speed Data From Satellite Altimeter to Support Wind Turbine Energy,
GeoDisast17(215-224).
DOI Link 1805
BibRef

Lahmadi, K., Boumhidi, I.,
Stabilization analysis of observer-based controller for uncertain and disturbed T-S fuzzy model: Application to wind turbine,
ISCV18(1-8)
IEEE DOI 1807
Lyapunov methods, control system synthesis, fuzzy control, nonlinear control systems, observers, pendulums, stability, uncertainties parameters BibRef

Elkhadiri, S., Elmenzhi, P.L., Lyhyaoui, P.A.,
Fuzzy logic control of DFIG-based wind turbine,
ISCV18(1-5)
IEEE DOI 1807
PI control, PWM power convertors, asynchronous generators, electric current control, fuzzy control, machine vector control, variable speed wind turbine BibRef

Rajae, L., Ismail, B.,
Optimal nonlinear control for a variable speed wind turbine based on support vector machine algorithm,
ISCV17(1-6)
IEEE DOI 1710
Aerodynamics, Generators, Mathematical model, Rotors, Support vector machines, Torque, Wind turbines, MPPT, Nonlinear state feedback control, Sliding mode control, Support vector machines, Variable, speed, wind, turbine BibRef

Aboulem, S., Boufounas, E.M., Boumhidi, I.,
Optimal tracking and robust intelligent based PI power controller of the wind turbine systems,
ISCV17(1-7)
IEEE DOI 1710
Particle swarm optimization, Robustness, Rotors, Sliding mode control, Torque, Wind turbines, Integral sliding mode control, Particle swarm optimization, BibRef

Boufounas, E.M., Berrada, Y., Koumir, M., Boumhidi, I.,
A robust intelligent control for a variable speed wind turbine based on general regression neural network,
ISCV15(1-6)
IEEE DOI 1506
backpropagation BibRef

Zhi-Feigao, Xie, Y.[Yuan], Xu, Y.B.[Yong-Bin], Wang, Y.H.[Yong-Hai],
Research of wind turbine gearbox vibration signal based on the wavelet analysis,
ICWAPR15(58-63)
IEEE DOI 1511
gears BibRef

Rodríguez, G.[Germán], Fuciños, M.[Maria], Pardo, X.M.[Xosé M.], Fdez-Vidal, X.R.[Xosé R.],
Videogrammetry System for Wind Turbine Vibration Monitoring,
IbPRIA15(505-513).
Springer DOI 1506
BibRef

Lv, Y.G.[Yue-Gang], Guan, N.[Ning], Liu, J.C.[Jun-Cheng], Cai, T.Q.[Teng-Qian],
The fault diagnosis of rolling bearing in gearbox of wind turbines based on second generation wavelet,
ICWAPR14(43-49)
IEEE DOI 1402
Fault diagnosis BibRef

Zhao, R.[Rui], Iqbal, M.R.A., Bennett, K.P., Ji, Q.A.[Qi-Ang],
Wind turbine fault prediction using soft label SVM,
ICPR16(3192-3197)
IEEE DOI 1705
Economic indicators, Sensors, Support vector machines, Time series analysis, Training, Wind energy, Wind, turbines BibRef

Chapter on Optical Flow Field Computations and Use continues in
Surface Reconstruction from Optical Flow .


Last update:May 15, 2022 at 14:39:14