Importance of Shear in Site Assessment of Wind Turbine Fatigue Loads

Autor: Morten Lybech Th⊘gersen, René M. M. Slot, Lasse Svenningsen, John Dalsgaard S⊘rensen
Rok vydání: 2018
Předmět:
Zdroj: Slot, R M M, Svenningsen, L, Sørensen, J D & Thøgersen, M L 2018, ' Importance of Shear in Site Assessment of Wind Turbine Fatigue Loads ', Journal of Solar Energy Engineering, vol. 140, no. 4, 041012 . https://doi.org/10.1115/1.4039748
ISSN: 1528-8986
0199-6231
DOI: 10.1115/1.4039748
Popis: Wind turbines are subjected to fatigue loading during their entire lifetime due to the fluctuating excitation from the wind. To predict the fatigue damage, the design standard IEC 61400-1 describes how to parametrize an on-site specific wind climate using the wind speed, turbulence, wind shear, air density, and flow inclination. In this framework, shear is currently modeled by its mean value, accounting for neither its natural variance nor its wind speed dependence. This very simple model may lead to inaccurate fatigue assessment of wind turbine components, whose structural response is nonlinear with shear. Here we show how this is the case for flapwise bending of blades, where the current shear model leads to inaccurate and in worst case nonconservative fatigue assessments. Based on an optimization study, we suggest modeling shear as a wind speed dependent 60% quantile. Using measurements from almost one hundred sites, we document that the suggested model leads to accurate and consistent fatigue assessments of wind turbine blades, without compromising other main components such as the tower and the shaft. The proposed shear model is intended as a replacement to the mean shear, and should be used alongside the current IEC models for the remaining climate parameters. Given the large number of investigated sites, a basis for evaluating the uncertainty related to using a simplified statistical wind climate is provided. This can be used in further research when assessing the structural reliability of wind turbines by a probabilistic or semiprobabilistic approach.
Databáze: OpenAIRE