Effective wind speed estimation based on a data-driven model of wind turbine tower deflection

Nasrabad, Vahid Saberi; Hajnayeb, Ali; Sun, Qiao

doi:10.1177/0954406221993845

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…This consolidates the need to consider the effect of wind velocity on the stresses generated in the OWT support structure. Previous research works have mainly focussed on researching the soil-pile interaction of wind turbine support structures, [7][8][9][10][11][12][13][14] fatigue assessments, design and performance, 3,[14][15][16][17][18][19][20] induced stresses in welds and connections and their performance [21][22][23][24][25] and recently, corrosion 26,27 while applying finite element analysis. 28,29 Although key findings involved optimised design parameters, stress concentrations and corrosion mechanisms, missing gaps include integrating these factors, mainly how corrosion and stresses interact in the support structure.…”

Section: Introductionmentioning

confidence: 99%

Stress analyses of high-rated capacity large diameter offshore wind turbines: Analytical and numerical analyses of uniform corrosion effects

Okenyi,

Bodaghi,

Siegkas

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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As the wind energy sector grows, offshore wind turbines (OWTs) have been pushed to have higher power outputs. This has led to large-diameter OWT support structures that are capable of withstanding aerodynamic and hydrodynamic loads as well as corrosion. This research analyses a 15-megawatt (MW) OWT support structure using analytical and numerical models. The analytical model was applied based on the Euler-Bernoulli beam theory. Static and modal finite element models were also applied. The structural stability implications of uniform corrosion on the stress evolution of the monopile and tower at different corrosion zones were discussed. Analytical and numerical (finite element analysis) predictions of stress evolution for different wind velocities and uniform corrosion material loss showed an agreement. The analyses showed that material loss due to corrosion increased the stress levels in the support structure. The location of the maximum tensile stress changed from the submerged to the splash zone, indicating that the splash zone may accumulate more damage over time due to the reduction of the monopile thickness and generation of local pits. Predicting the stress evolution due to uniform corrosion could be instrumental in the future designs of OWTs. It can be integral to fatigue assessments for enabling more detailed and accurate life predictions.

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Section: Introductionmentioning

confidence: 99%

Stress analyses of high-rated capacity large diameter offshore wind turbines: Analytical and numerical analyses of uniform corrosion effects

Okenyi,

Bodaghi,

Siegkas

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…In a case study of the 1310 m long Hardanger Bridge, Petersen et al [18,19] studied wind loads in time domain and frequency domain and introduced the Potential Gaussian Process model (GP-LFMS) to characterize the evolution of wind loads. Nasrabad et al [20] presented a new wind speed estimation method by observing the deflection response of the tower and used the neural network to help estimate. Charisi et al [21] determined the variation of wind pressure coefficient at different locations along the wind direction by on-site measurement of a double medium-sized building complex, which is of great significance to the subsequent calculation of wind pressure and wind load.…”

Section: Introductionmentioning

confidence: 99%

Identification of Wind Loads on Structures Based on Modal Kalman Filter with Unknown Inputs

2022

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Wind loads on structures are difficult to directly measure, so it is practical to identify structural wind loads based on the measurements of structural responses. However, this inversed problem is challenging compared with conventional load identification as wind loads are time-space coupled and spatially distributed dynamic loads on structures. An improved method is proposed for identifying wind loads on structures using only partial measurements of structural acceleration responses in this paper. First, the wind loads on a structure are decomposed by proper orthogonal decomposition as a series of time-space decoupled sub-distributed dynamic loads with independent basic spatial distribution functions and time history functions. Herein, structural modes are adopted as the basic spatial distribution functions and structural modes of discretized and continuous structural systems are investigated. Then, a history function of the decomposed wind load is identified in the modal domain based on modal Kalman filter with unknown inputs, which is proposed by the authors. Finally, the distributed wind loads are reconstructed for discrete or continuous structural systems. The feasibility of the proposed algorithm is verified by two numerical examples of identification of wind loads on a discrete shear frame and a wind turbine tower, respectively.

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Identification of full-field wind loads on buildings using a mechanism-inspired recursive convolutional neural network with partial structural responses

Zhang,

Lei,

Liu

et al. 2024

Physics of Fluids

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Indirect identification approaches through structural responses have proven effective for wind load estimation in real-world engineering. Currently, methods for identifying wind loads mainly rely on theoretical inverse identification, with rare research based on the mapping relationship between structural responses and wind loads through machine learning. In this paper, a scheme for identifying full-field wind loads using a recursive convolutional neural network (CNN) inspired by physical mechanisms is proposed. The recursive form of the network, as well as the inspiration for its inputs and outputs, is inspired by the spatial correlation and the mapping relationship between wind loads and structural responses. Thus, the network inputs comprise a fusion of structural acceleration and inter-story displacement responses, while the network outputs represent the independent wind loads on structures. Notably, mismatch test is employed by the network, wherein the training and testing datasets originate from entirely different sources. Specifically, during training, Gaussian white noises that simulate wind loads are utilized, while real wind load data are used for testing. The generalization of the proposed scheme is demonstrated through the identification of full-field wind loads generated by different stationary or non-stationary wind spectra of the 76-story wind-excited benchmark building. Furthermore, the proposed scheme is validated by identifying the full-field wind loads of a 67-story shear wall structure with wind tunnel test data.

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Effective wind speed estimation based on a data-driven model of wind turbine tower deflection

Cited by 4 publications

References 40 publications

Stress analyses of high-rated capacity large diameter offshore wind turbines: Analytical and numerical analyses of uniform corrosion effects

Stress analyses of high-rated capacity large diameter offshore wind turbines: Analytical and numerical analyses of uniform corrosion effects

Identification of Wind Loads on Structures Based on Modal Kalman Filter with Unknown Inputs

Identification of full-field wind loads on buildings using a mechanism-inspired recursive convolutional neural network with partial structural responses

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