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nature.com
article
https://www.nature.com/articles/s41598-025-27896-9
# Improving wind power prediction with advanced temporal and frequency domain processing combined with error correction. Accurate prediction of wind power is crucial for grid scheduling and the integration of renewable energy, given its significant temporal variability and nonlinear characteristics. This study proposed a multi-module integrated model for wind power forecasting based on time–frequency domain analysis, aiming to enhance prediction accuracy and reliability. The mode9l combined several advanced techniques, including Wavelet Convolutions (WTC), Long Short-Term Memory Networks (LSTM), Time Series Lightweight Adaptive Network (TSLANet), Frequency Enhanced Channel Attention Mechanism (FECAM), and Fast Kolmogorov-Arnold Networks (FastKAN). Each module was designed to capture distinct characteristics in wind power data, such as local frequency features, temporal dependencies, global contextual information, frequency-domain features, and complex nonlinear relationships. Through the integration of these modules, the model achieved high-precision predictions in multi-scale and dynamic environments. Experimental results showed that the model delivered exceptional performance across various test scenarios, significantly improving the handling of multi-scale, complex nonlinear, and global dependency issues in wind power forecasting, demonstrating considerable application potential.
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sciopen.com
article
https://www.sciopen.com/article/10.26599/OCEAN.2026.9470016
Fully coupled time-domain simulation is essential for the analysis and design of floating wind turbines (FWTs); and optimization studies. A
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wes.copernicus.org
article
https://wes.copernicus.org/preprints/wes-2025-115/wes-2025-115.pdf
Modeling approach comparison for FWTs. Modeling Approach Advantages Disadvantages Frequency-Domain Models – Computationally efficient (fast calculations) (Borg and Collu, 2015) – Suitable for preliminary and conceptual design phases – Enables rapid exploration of large design spaces – Facilitates multi-objective optimization with reduced computational demand – Assumes linear system behavior, limiting accuracy (Borg and Collu, 2015; Journée and Massie, 2001) – Underpredicts dynamic and nonlinear structural responses – Unable to reliably capture transient (Borg and Collu, 2015) and non-linear interactions (e.g., memory effects) (Journée and Massie, 2001) – Necessitates application of safety factors due to inherent uncertain-ties (Pillai et al., 2018) Time-Domain Models – High accuracy in capturing nonlinear and transient structural re-sponses – Comprehensive representation of aerodynamic, hydrodynamic, structural, and control interactions (GL, 2018) – Reliable fatigue (GL, 2018) and ultimate load estimations – Appropriate for detailed design stages and verification purposes – Significant computational expense (Borg and Collu, 2015) – Require input data from frequency domain simulations (Borg and Collu, 2015) – Not directly practical for iterative design optimization processes (Pegalajar-Jurado et al., 2018) – Computationally prohibitive for extensive parametric analyses or re-peated optimization cycles Surrogate-Based Hybrid Approach (Proposed) – Effectively balances computational effort and modeling accuracy – Enables high-fidelity time-domain analyses at significantly reduced computational cost via surrogate modeling and design space reduc-tion with analytical constraints – Facilitates detailed structural optimization across multiple limit states (ULS, FLS, SLS) – Provides a bridge between conceptual and detailed design phases – Enhances accuracy significantly compared to purely frequency-domain approaches – Requires cautious training and validation of surrogate models – Performance is heavily dependent on the quality and representative-ness of surrogate training data – Initial computational burden associated with generating surrogate model training datasets 3 https://doi.org/10.5194/wes-2025-115 Preprint.
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dnv.com
article
https://www.dnv.com/article/time-domain-analysis-for-floating-offshore-wind-s…
# Time domain analysis for floating offshore wind substructure design. The expansion of the offshore wind industry to deeper water depths requires the usage of floating wind support structures, bringing new challenges to the industry. * Time Domain Direct Load Generation method: This is the most general method where hydrodynamic pressure and Morison loads are generated directly in the time domain before they are mapped to a structural Finite Element model. * Time Domain Load Reconstruction method: This method is an evolution of the Direct Load Generation method and can be used to drastically reduce the computational cost associated with hydrodynamic load generation. This method is the fastest and may reduce the simulation time from hours for direct simulation to just a few minutes. Frequency domain analysis for floating offshore wind substructure design. Webinar: New fast time domain simulation methods for floating wind substructure design.
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dnv.com
news
https://www.dnv.com/news/2025/dnv-launches-advanced-time-saving-tools-for-ana…
# DNV launches advanced time-saving tools for analysis of floating offshore wind structures. DNV, the independent energy expert and assurance provider, has developed three advanced time-domain methods for analysing the structural performance of floating offshore wind turbines. Now available in DNV’s Sesam software, the methods simulate how turbines respond to wind and wave forces in harsh offshore environments. These new methods represent a fundamental advance in the analysis of floating wind structures, delivering faster performance, greater efficiency, and adherence to the latest standards.". Since its origin in the 1960s, DNV’s Sesam software has been trusted for the design and analysis of ships and offshore structures. Access DNV’s newly published whitepaper to explore advanced time-domain methods for floating wind analysis, supporting the safe and efficient design of tomorrow’s FOWTs. Time-domain methods for floating offshore wind turbine substructures. ### Discuss software for the design of offshore wind turbine structures.
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docs.nlr.gov
official
https://docs.nlr.gov/docs/fy22osti/82011.pdf
Published under licence by IOP Publishing Ltd The Science of Making Torque from Wind (TORQUE 2022) Journal of Physics: Conference Series 2265 (2022) 042020 IOP Publishing doi:10.1088/1742-6596/2265/4/042020 1 An Open-Source Frequency-Domain Model for Floating Wind Turbine Design Optimization Matthew Hall, Stein Housner, Daniel Zalkind, Pietro Bortolotti, David Ogden, Garrett Barter National Renewable Energy Laboratory 15013 Denver West Parkway, Golden, CO, USA E-mail: matthew.hall@nrel.gov Abstract. The model, called RAFT (Response Amplitudes of Floating Turbines), incorporates quasi-static mooring reactions, strip-theory and potential-flow hydrodynamics, blade-element-momentum aerodynamics, and linear turbine control. The model is applied to three reference floating wind turbine designs and its predictions are compared with results from time-domain OpenFAST simulations. 2. Modeling Approach As a frequency-domain model, RAFT is based on a linear, frequency-dependent equation for the floating system’s steady-state response.
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sciencedirect.com
article
https://www.sciencedirect.com/science/article/abs/pii/S0029801824010436
by T Tao · 2024 · Cited by 41 — This study examined the effect of yaw optimization control on the fatigue life of offshore wind turbines using tower bolts.
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pmc.ncbi.nlm.nih.gov
official
https://pmc.ncbi.nlm.nih.gov/articles/PMC11950329
A **.gov** website belongs to an official government organization in the United States. Image 9: Close Search Image 10: Search. # Optimizing power generation in a hybrid solar wind energy system using a DFIG-based control approach. The Hybrid Solar Wind Energy System (HSWES) integrates wind turbines with solar energy systems. The goal is to optimize power tracking efficiency in an electrically linked solar photovoltaic system combined with a wind-powered Doubly Fed Induction Generator (DFIG). This study aims to optimize power extraction efficiency and hybrid system integration with electrical grids by applying the Maximum Power Point Tracking (MPPT) technique to solar and wind systems. An integrated energy system is produced when solar photovoltaic panels are incorporated into a wind power system based on DFIG’s DC connection of the converter. Using these MPPT methods in a DFIG hybrid system connected to the grid, a solar photovoltaic system connected to the direct current link, and a converter presents several technical challenges and opportunities12,13.