Mechanics‐based model updating for identification and virtual sensing of an offshore wind turbine using sparse measurements

2020 ◽  
Author(s):  
Mansureh‐Sadat Nabiyan ◽  
Faramarz Khoshnoudian ◽  
Babak Moaveni ◽  
Hamed Ebrahimian
Keyword(s):  
Wind Turbine ◽  
Model Updating ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Virtual Sensing ◽  
Sparse Measurements

scholarly journals Support condition monitoring of offshore wind turbines using model updating techniques

2019 ◽  
Vol 19 (4) ◽  
pp. 1017-1031 ◽  
Author(s):  
Ying Xu ◽  
George Nikitas ◽  
Tong Zhang ◽  
Qinghua Han ◽  
Marios Chryssanthopoulos ◽  
...  
Keyword(s):  
Finite Element ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Model Updating ◽  
Element Model ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Support Condition ◽  
Offshore Wind Turbines ◽  
Wind Turbine System

The offshore wind turbines are dynamically sensitive, whose fundamental frequency can be very close to the forcing frequencies activated by the environmental and turbine loads. Minor changes of support conditions may lead to the shift of natural frequencies, and this could be disastrous if resonance happens. To monitor the support conditions and thus to enhance the safety of offshore wind turbines, a model updating method is developed in this study. A hybrid sensing system was fabricated and set up in the laboratory to investigate the long-term dynamic behaviour of the offshore wind turbine system with monopile foundation in sandy deposits. A finite element model was constructed to simulate structural behaviours of the offshore wind turbine system. Distributed nonlinear springs and a roller boundary condition are used to model the soil–structure interaction properties. The finite element model and the test results were used to analyse the variation of the support condition of the monopile, through an finite element model updating process using estimation of distribution algorithms. The results show that the fundamental frequency of the test model increases after a period under cyclic loading, which is attributed to the compaction of the surrounding sand instead of local damage of the structure. The hybrid sensing system is reliable to detect both the acceleration and strain responses of the offshore wind turbine model and can be potentially applied to the remote monitoring of real offshore wind turbines. The estimation of distribution algorithm–based model updating technique is demonstrated to be successful for the support condition monitoring of the offshore wind turbine system, which is potentially useful for other model updating and condition monitoring applications.

scholarly journals VIRTUAL SENSING AND STRAIN ESTIMATION ON AN OFFSHORE WIND TURBINE USING SUPERVISED LEARNING

2021 ◽  
Author(s):  
Marius Tarpø ◽  
Sandro Amador ◽  
Evangelos Katsanos ◽  
Mattias Skog ◽  
Johan Gjødvad ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Supervised Learning ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Strain Estimation ◽  
Virtual Sensing

Model Updating of Soil-Structure Interface of Monopiled Offshore Wind Turbines

2020 ◽  
Author(s):  
Shuai Cong ◽  
Sau-Lon James Hu ◽  
Hua-Jun Li
Keyword(s):  
Wind Turbine ◽  
Soil Structure ◽  
Model Updating ◽  
Element Model ◽  
Offshore Wind ◽  
Soil Structure Interaction ◽  
Offshore Wind Turbine ◽  
Finite Element Model Updating ◽  
Offshore Wind Turbines ◽  
Stiffness And Damping

Abstract As the vibration analysis of an offshore wind turbine (OWT) system should consider its soil-structure interaction, a recent article proposed a simple soil-structure interface model. It includes a horizontal spring, a rotational spring and a rotational dash-pot at the interface. Developing a finite element model updating method on correcting the soil-structure interface coefficients based on the true response measurements is highly desired. This paper develops an accurate and efficient model updating method for simultaneously updating the stiffness and damping parameters of the soil-structure interface of a monopiled offshore wind turbine, when only a few measured modal frequencies and damping ratios are available. The performance of the proposed method is numerically demonstrated through a simulated National Renewable Energy Laboratory 5-MW (NREL 5-MW) reference turbine.

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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