Safety‐factor calibration for wind turbine extreme loads

Moriarty, Patrick

doi:10.1002/we.306

Cited by 16 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moriarty et al (2004) have developed the peak extraction methods for estimation of extremes for wind turbine. Moriarty (2008) proposed two safety factors as an alternative to extrapolation. He concluded that the safety factors calculated from the median of extreme wind loads were less uncertain than those obtained from mean and standard deviation.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear short term extreme response of spar type floating offshore wind turbines

2017

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Nonlinear short term extreme response of spar type floating offshore wind turbines

2017

View full text Add to dashboard Cite

“…The largest eigenvalue can be solved using (14): max = 3. The eigenvector is then obtained according to (14): w = [1,2,3] . To determine the consistency of the matrix, CR is calculated according to (16): CR = 0 < 0.1.…”

Section: Case Study and Discussionmentioning

confidence: 99%

“…Peeringa [1] estimated the extreme load of a wind turbine using parametric extrapolation, in which two different distribution functions are selected to fit the test load. The safety factor [2], a new extrapolation method, is applied in a wind turbine, in which the subjectivity and empiricism of parametric extrapolation are avoided. Rodzewicz [3] predicted the long-term loads of a glider accurately by reintegrating tested data.…”

Section: Introductionmentioning

confidence: 99%

Selection Method for Kernel Function in Nonparametric Extrapolation Based on Multicriteria Decision-Making Technology

Wang

Liu

Zeng

et al. 2013

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Selecting the most appropriate kernel function to extrapolate a load set is the paramount step in compiling load spectrum, as it affects the results of nonparametric extrapolation largely. Aiming at this issue, this paper provides a new approach in selecting kernel function for the nonparametric extrapolation. To solve the complexity and uncertainty of nonparametric extrapolation, characteristics of four kernel functions and their effects on the results are explained in the “from-to” diagram obtained by rainflow counting. Multicriteria decision-making (MCDM) is then applied to solve the selection problem of kernel function. To evaluate the dispersion degrees of the mean and amplitude of a load set accurately, their range, standard deviation, and interquartile range are selected as the evaluation criteria. The weight of each criterion, which represents the impact degree on the selection of the kernel function, is calculated separately using the eigenvector and entropy method. The comprehensive weights are obtained by applying the optimization theory and Jaynes’ maximum entropy principle. Finally, the importance of each criterion is discussed according to their calculated comprehensive weights, and the selection method for kernel functions is obtained, which is illustrated by extrapolating the output torque of the power split device of hybrid electrical vehicles.

show abstract

“…Based on this method, the finite sample data was extrapolated to 50 years. In addition, an extrapolation method based on the mean and standard deviation of extreme values was proposed by Moriarty [63], where subjectivity of the parametric extrapolation was avoided.…”

Section: A Review Of the Extrapolation Methods In Load Spectrum Compilingmentioning

confidence: 99%

A Review of the Extrapolation Method in Load Spectrum Compiling

Wang¹,

Hong-bin²,

Li³

et al. 2016

SV-JME

View full text Add to dashboard Cite

60 INTRODUCTIONIn engineering, many mechanical structures and components are subjected to complex and random loads, which determine the fatigue reliability and life of the machinery [1] and [2]. Thus, it is indispensable to conduct fatigue analysis and life prediction of the structures and components based on a load spectrum [3] and [4]. Currently, a load spectrum is widely used in the fields of aerospace [5] . In practice, a long-term load spectrum contains the complete load information, but it is difficult to be directly measured due to the restrictions of testing technology, as well as time and cost. Therefore, it is necessary to obtain a long-term load spectrum based on a short-term one.The traditional load spectrum compiling method multiplies a short-term load spectrum with a constant proportionality coefficient [15] to [17]. Since only the data measured in a finite time is repeated, the extreme loads that cannot be measured and have a greater impact on damage are ignored. Load extrapolation methods can overcome the above limitation of the traditional method. With the development of statistics and computer software, new methods have been applied to load extrapolation. In load spectrum compiling, results may vary from each other with different extrapolation methods. Therefore, selecting an appropriate load extrapolation method is very important, but that is difficult in practice. For a better understanding of the methods and to provide selection guidance, several commonly used extrapolation methods are reviewed and summarized based on the literature and illustrations in this paper.The extrapolation methods are classified as the parametric extrapolation method (PE), nonparametric extrapolation method (NPE) and quantile extrapolation method (QE). In PE, sample data is supposed to obey a known distribution, and the parameters in the function are estimated according to the load sample. In NPE, an extrapolated result is obtained because the density distribution with an arbitrary shape can be received based on a nonparametric density estimation. When the sample data has different load characteristics due to different working conditions and different operating behaviors in the testing process, QE can break the data into a series of clusters and computes the damage of each rainflow matrix. The literature and illustrations are presented to evaluate the extrapolation methods and the characteristics of various extrapolation methods, such as the critical factors, the advantages and disadvantages, and the application ranges, are summarized. Some potential research prospects are

show abstract

Safety‐factor calibration for wind turbine extreme loads

Cited by 16 publications

References 9 publications

Nonlinear short term extreme response of spar type floating offshore wind turbines

Nonlinear short term extreme response of spar type floating offshore wind turbines

Selection Method for Kernel Function in Nonparametric Extrapolation Based on Multicriteria Decision-Making Technology

A Review of the Extrapolation Method in Load Spectrum Compiling

Contact Info

Product

Resources

About