The purpose of this paper is to derive and propose site-specific joint environmental contours for two eligible Offshore Wind Farm Organized Development Areas (OWFODAs) in the Aegean Sea, Greece. The contours are tailored primarily for the design, structural reliability assessment and definition of site-specific environmental load combinations of offshore wind turbines (OWTs); they are quantified based on publicly available 28-year data sets related to offshore wind and wave conditions, namely, wave height, Hs, wave peak period, Tp and mean wind speed at the hub height of the wind turbine, u¯hub. A new methodology, using the modified Inverse First Order Reliability Method (IFORM), is proposed to accurately reflect the regional climate peculiarities, combined with fifth-order polynomials and a sigmoid function to fit the data of the Weibull parameters and correctly capture the low- and mid-range values of Hs, which are statistically far more frequent. Several results, in terms of 2D and 3D contour surfaces for two locations in each OWFODA, for 50-year and 100-year return periods are presented. Finally, two tables are cited: one gathering Hs and Tp values corresponding to the maximum u¯hub conditions, and another gathering u¯hub and Tp values corresponding to the maximum Hs conditions. The presented joint probability distributions and the environmental contour surfaces bridge metocean statistical modelling with renewable energy systems design. By providing site-specific joint metocean conditions, the proposed methodology supports offshore wind farm design and structural assessment, thereby contributing to sustainable wind energy development in the Aegean Sea.
T. Tsaousis, C. Michailides, I. Chatjigeorgiou· Journal of Marine Science an...· 0 citations
The blades directly affect the safety and power generation efficiency of the wind turbines. With the blade size increases, the reliable modal identification becomes important for vibration-based health monitoring. Although operational modal analysis (OMA) technique has been used in condition monitoring for the wind turbine blades, most existing studies focus on investigating a specific single method or under ideal excitation conditions. To overcome this limitation, this study takes the IEA-15MW large wind turbine blade as the research object and compares three OMA methods through numerical simulations, namely covariance-driven stochastic subspace identification (SSI-COV), frequency domain decomposition (FDD), and poly-reference least squares complex frequency domain (PolyMAX). The performance of the modal parameter identification methods is evaluated with respect to different sensor layouts, blade–tower coupling conditions, and environmental excitations. The results indicate that sparse sensor deployment cannot reliably identify the damage-sensitive high-order and complex modes. A nine-channel layout concentrated near second-order deformation regions significantly improves the identification of second-order flapwise frequencies and controls the average error of the first six modes within 3%. PolyMAX shows the best identification stability under different numbers and layouts of the sensors. Blade–tower coupling changes the blade modal characteristics and increases identification difficulty. Under this condition, FDD can still identify both low-order and high-order modes with good stability. Under different real wind conditions, the increasing wind speed causes the aerodynamic load to deviate from the white noise assumption, generally leading to fluctuations in the identification errors, with relatively large local errors occurring at certain medium and high wind speeds. Overall, the three OMA methods show different advantages under different identification conditions. PolyMAX shows the best stability under different sensor layouts and performs best when wind speed increases in the coupled wind turbine model, indicating that it is the most suitable for the actual complex coupling effects and environmental conditions. This research hopefully provides a basis for the subsequent engineering application of vibration-based modal identification of large offshore blades.
Qiang Liu, Meng Zhang, Xu Han et al.· Energies· 0 citations
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