Probabilistic sensitivity analysis of offshore wind turbines using a transformed Kullback-Leibler divergence

Teixeira, Rui; O’Connor, Alan; Nogal, María

doi:10.1016/j.strusafe.2019.03.007

Cited by 23 publications

(21 citation statements)

References 38 publications

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“…MC in WRA is usually used for windfarm layout optimization 23,24 or profitability UA 10,[25][26][27][28][29] often with no SA accompanying. In wind turbine specific studies, MC was also used for stochastic blade flutter analysis, 30 turbine reliability, 31 and fatigue load 32,33 assessments. To the best of the authors' knowledge, SA in WRA commonly uses OAT methods [10][11][12][13][14][15] that disregard model nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

Quasi-Monte Carlo technique in global sensitivity analysis of wind resource assessment with a study on UAE

Tsvetkova¹,

Ouarda²

2019

Journal of Renewable and Sustainable Energy

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The present paper bridges mathematical modeling and wind resource assessment (WRA). Sensitivity analysis (SA) links portions of output variance to the variance in each input variable. Global SA (GSA) explores inputs globally. One-at-a-time SA is dominating in WRA, while GSA is often overlooked. Compared to traditional methods, GSA offers potential improvement by the means of the quasi-Monte Carlo (QMC) technique with its elaborate sampling designs enabling faster convergence. The main novelty of this work is twofold: the use of QMC in WRA and Sobol method as a variance-based GSA method in WRA. This paper is among a few using GSA in WRA. Two case studies were conducted. One shows that QMC with sampling based on Sobol sampling outperforms Latin hypercube sampling and pseudorandom sampling. It also shows that in terms of accuracy of results, the brute-force calculation of Sobol sensitivity indices (SI) should be used whenever the model allows it; otherwise, SI can be estimated. Another case study demonstrates a valid GSA study for WRA at Masdar City, United Arab Emirates. The results suggest that the influence of the variance in Weibull parameters on annual energy production (AEP) might be overestimated, while found to be responsible for 2% of AEP uncertainty, and the influence of the variance in air density might be overlooked, while found to account for 94%. WRA studies would benefit greatly from using the QMC and Sobol method. The Sobol method is a universal GSA method, providing valid results for nonlinear models typical for WRA, and QMC provides global scalability, model independence, and flexibility in uncertainty quantification.

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Section: Introductionmentioning

confidence: 99%

Quasi-Monte Carlo technique in global sensitivity analysis of wind resource assessment with a study on UAE

Tsvetkova¹,

Ouarda²

2019

Journal of Renewable and Sustainable Energy

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“…The observed coincidence deserves further theoretical and numerical studies, especially in the context of material and geometrical structural nonlinearities [ 38 , 39 ] with some stochastic imperfections. Further comparison of Bhattacharyya relative entropy with other probabilistic divergency models such as Kullback-Leibler [ 40 ], for instance, may provide an interesting alternative to the existing FORM and SORM reliability algorithms.…”

Section: Discussionmentioning

confidence: 99%

Structural Safety of the Steel Hall under Dynamic Excitation Using the Relative Probabilistic Entropy Concept

Bredow

Kamiński

2022

Materials

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This work aimed to analyze the reliability of a steel hall that was recently erected in central Poland subjected to dynamic wind excitation using the stochastic finite element method. Reliability analysis was completed using the relative entropy concept delivered by Bhattacharyya and contrasted with the first-order reliability method recommended by the engineering design codes. Bhattacharyya probabilistic relative entropy was additionally rescaled in this study to fit the demands and recommended admissibility intervals given in Eurocode 0. The finite element method study was carried out thanks to a discrete model created in the system ABAQUS 2019, while all further statistical and probabilistic computations were programmed and completed in the symbolic environment of MAPLE 2019. Contrary to most engineering analyses in steel structure areas, this study included the important warping effect while designing the hall ridges and the purlins. Dynamic structural responses were determined via the Hilber-Hughes-Taylor algorithm and their series were numerically obtained for a series of input uncertainty parameters representing several mechanical and environmental quantities. The generalized 10th order iterative stochastic perturbation technique was contrasted in this context with statistical estimators from the Monte Carlo simulations and numerical integration resulting from the semi-analytical approach. The key research finding of this study was an extremely good coincidence between the FORM indices and the rescaled relative probabilistic entropies for the given stochastic excitations, which additionally did not depend on a choice of one of the three proposed numerical approaches.

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“…Hübler et al 5 Robertson et al 9 Toft et al 11 Teixeira et al 8 Peeringa &Bedon 4 Velarde et al 6 Turbine size (MW) studies regarding either the parameters considered, or the parameters identified as important. This may be caused both by different sensitivities for different turbines and foundations, as well as by the level of uncertainty assumed in the studies.…”

Section: Referencementioning

confidence: 99%

“…3 These parameters should be given particular focus during the design process, and they can be identified through a sensitivity analysis (SA). Numerous SAs and reliability studies have been performed on individual OWTs, [4][5][6][7][8][9][10][11][12][13][14] with size varying from 4 to 10 MW. The results from selected studies considering several uncertain parameters are summarised in Table 1.…”

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confidence: 99%

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Fatigue design sensitivities of large monopile offshore wind turbines

et al. 2022

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The input for fatigue analyses of offshore wind turbines is typically chosen based on design values provided by design standards. While this provides a straightforward design methodology, the contribution of different input parameters to the uncertainty in the fatigue damage estimates is usually unknown. This knowledge is important to have when improving current designs and methodologies, and the parameters governing the uncertainty is typically found through a sensitivity analysis. Several sensitivity studies have been performed for monopile‐based offshore wind turbines, typically focusing on specific turbines and engineering disciplines. This paper performs a sensitivity study for three monopile‐based offshore wind turbines (5 MW, 10 MW, 15 MW) using parameters from several engineering disciplines. The results show that the fatigue utilization is primarily governed by the uncertainty in the SN curves and fatigue capacity. Following this, the uncertainty in the environmental conditions is the dominating uncertainty, with wind loads becoming increasingly important as turbine size increases. Additionally, the effect of modelling uncertainties is investigated. The wind‐related model uncertainties dominate in the tower top, while uncertainties in the wave and soil models dominate in the tower base and monopile. Designers wanting to reduce the uncertainty in a design are recommended to focus on the environmental conditions, and using as accurate models as possible. All modelling uncertainties are significant, but research should particularly be focused on wave directionality and soil models.

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Probabilistic sensitivity analysis of offshore wind turbines using a transformed Kullback-Leibler divergence

Cited by 23 publications

References 38 publications

Quasi-Monte Carlo technique in global sensitivity analysis of wind resource assessment with a study on UAE

Quasi-Monte Carlo technique in global sensitivity analysis of wind resource assessment with a study on UAE

Structural Safety of the Steel Hall under Dynamic Excitation Using the Relative Probabilistic Entropy Concept

Fatigue design sensitivities of large monopile offshore wind turbines

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