Baseline Control Strategy for Maximum Power Tracking for a 5MW Offshore Wind Turbine

2019 ◽  
Author(s):  
Sina Ameli ◽  
Mohammad Javad Morshed ◽  
Afef Fekih
Keyword(s):  
Wind Turbine ◽  
Control Strategy ◽  
Maximum Power ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Maximum Power Tracking

scholarly journals Application of adaptive fuzzy control to suppression vibration response of floating offshore wind turbine

2021 ◽  
Vol 39 (2) ◽  
pp. 241-248
Author(s):  
Jiajia Yang ◽  
Erming He ◽  
Juncheng Shu
Keyword(s):  
Wind Turbine ◽  
Control Strategy ◽  
Passive Control ◽  
Wind Wave ◽  
Fuzzy Controller ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Structural Parameter ◽  
Floating Offshore Wind Turbine ◽  
Adaptive Fuzzy

Floating offshore wind turbine is a complex rigid-flexible coupling nonlinear system, and the accurate dynamic model is difficultly established. Therefore, the wind-wave interference cannot be improved by adopting the conventional control strategy. In order to solve this problem, an adaptive fuzzy controller (AFC) is used to suppress the dynamic response of floating wind turbine. Two correction factors are introduced to optimize the fuzzy rule, and the traditional fuzzy controller (FC) is firstly obtained. Since the balance positions change and structural parameter perturbation of the wind turbine, an AFC is designed and validated. Finally, the suppression vibration responses ability of floating offshore wind turbine by using the different control strategies is studied under the random wind-wave disturbance and blade pitch control system coupling effect. The simulation results show that the tracking ability of the AFC to the target value is obviously higher than that of the FC; Comparing with the passive control strategy, the suppression vibration effect on the power spectral density (PSD) of the platform pitch (PFPI) motion peak can increase by 39.06% by adopting the AFC.

scholarly journals Reducing Tower Fatigue through Blade Back Twist and Active Pitch-to-Stall Control Strategy for a Semi-Submersible Floating Offshore Wind Turbine

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1897 ◽  
Author(s):  
Dawn Ward ◽  
Maurizio Collu ◽  
Joy Sumner
Keyword(s):  
Fatigue Life ◽  
Wind Turbine ◽  
Control Strategy ◽  
Bending Moment ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Floating Offshore Wind Turbine ◽  
Negative Damping ◽  
Stall Control ◽  
Axial Fatigue

The necessity of producing more electricity from renewable sources has been driven predominantly by the need to prevent irreversible climate chance. Currently, industry is looking towards floating offshore wind turbine solutions to form part of their future renewable portfolio. However, wind turbine loads are often increased when mounted on a floating rather than fixed platform. Negative damping must also be avoided to prevent tower oscillations. By presenting a turbine actively pitching-to-stall, the impact on the tower fore–aft bending moment of a blade with back twist towards feather as it approaches the tip was explored, utilizing the time domain FAST v8 simulation tool. The turbine was coupled to a floating semisubmersible platform, as this type of floater suffers from increased fore–aft oscillations of the tower, and therefore could benefit from this alternative control approach. Correlation between the responses of the blade’s flapwise bending moment and the tower base’s fore–aft moment was observed with this back-twisted pitch-to-stall blade. Negative damping was also avoided by utilizing a pitch-to-stall control strategy. At 13 and 18 m/s mean turbulent winds, a 20% and 5.8% increase in the tower axial fatigue life was achieved, respectively. Overall, it was shown that the proposed approach seems to be effective in diminishing detrimental oscillations of the power output and in enhancing the tower axial fatigue life.

Experimental Study for SPAR Type Floating Offshore Wind Turbine With Blade-Pitch Control

2013 ◽  
Author(s):  
Toshiki Chujo ◽  
Yoshimasa Minami ◽  
Tadashi Nimura ◽  
Shigesuke Ishida
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Offshore Wind ◽  
Scale Model ◽  
Offshore Wind Turbine ◽  
Experimental Proof ◽  
Pitch Control ◽  
Model Experiments ◽  
North Eastern ◽  
North Eastern Part

The experimental proof of the floating wind turbine has been started off Goto Islands in Japan. Furthermore, the project of floating wind farm is afoot off Fukushima Prof. in north eastern part of Japan. It is essential for realization of the floating wind farm to comprehend its safety, electric generating property and motion in waves and wind. The scale model experiments are effective to catch the characteristic of floating wind turbines. Authors have mainly carried out scale model experiments with wind turbine models on SPAR buoy type floaters. The wind turbine models have blade-pitch control mechanism and authors focused attention on the effect of blade-pitch control on both the motion of floater and fluctuation of rotor speed. In this paper, the results of scale model experiments are discussed from the aspect of motion of floater and the effect of blade-pitch control.

scholarly journals Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3333
Author(s):  
Maria del Cisne Feijóo ◽  
Yovana Zambrano ◽  
Yolanda Vidal ◽  
Christian Tutivén
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Environmental Conditions ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Small Subset ◽  
Scaled Model ◽  
Vibration Data ◽  
Large Water

Structural health monitoring for offshore wind turbine foundations is paramount to the further development of offshore fixed wind farms. At present time there are a limited number of foundation designs, the jacket type being the preferred one in large water depths. In this work, a jacket-type foundation damage diagnosis strategy is stated. Normally, most or all the available data are of regular operation, thus methods that focus on the data leading to failures end up using only a small subset of the available data. Furthermore, when there is no historical precedent of a type of fault, those methods cannot be used. In addition, offshore wind turbines work under a wide variety of environmental conditions and regions of operation involving unknown input excitation given by the wind and waves. Taking into account the aforementioned difficulties, the stated strategy in this work is based on an autoencoder neural network model and its contribution is two-fold: (i) the proposed strategy is based only on healthy data, and (ii) it works under different operating and environmental conditions based only on the output vibration data gathered by accelerometer sensors. The proposed strategy has been tested through experimental laboratory tests on a scaled model.

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