Integrated analysis and design optimization of floating wind turbines (FWT) is vital for cost reduction and efficiency enhancement.
# 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.
This study evaluates an efficient time-domain fatigue analysis approach for welded joints in semi-submersible floating wind turbine (FWT)
Performance is about monitoring operation, knowing when something changes, understanding why it changed, and acting with confidence. Wind farm performance covers how assets produce energy over time, how losses evolve and how turbines respond to changing conditions. * • SCADA is noisy, unstable and not designed for performance analysis. * • Rotor effects bias the measured wind speed. * • Identical turbines can report different winds under the same conditions. Monitoring tools are useful for visibility: they show that something changed through trends and KPIs. Performance analysis explains what changed, why it changed and whether it matters, using signals that are comparable and interpretable rather than raw, mixed operation data. * • Comparable conditions across time and turbines. * • Impact quantified in performance and AEP. * • Wind-speed prediction baselines using turbine and neighbours context. * • Issue-specific signatures across turbine operation. * • Quantified impact in performance and AEP. * • Turn results into a clear performance plan.
12TH WORKSHOP ON INDUSTRIAL SYSTEMS AND ENERGY TECHNOLOGIES (JOSITE2017), MADRID, SPAIN 1 Performance Analysis and Anomaly Detection in Wind Turbines based on Neural Networks and Principal Component Analysis Peyman Mazidi, Student Member, IEEE, Lina Bertling Tjernberg, Senior Member, IEEE, and Miguel A. Sanz-Bobi, Senior Member, IEEE Abstract—This paper proposes an approach for maintenance management of wind turbines based on their life. The proposed approach uses performance analysis and anomaly detection (PAAD) which can detect anomalies and point out the origin of the detected anomalies. This PAAD algorithm utilizes neural network (NN) technique in order to detect anomalies in the performance of the wind turbine (system layer), and then applies principal component analysis (PCA) technique to uncover the root of the detected anomalies (component layer). To validate the accuracy of the proposed algorithm, SCADA data obtained from online condition monitoring of a wind turbine are utilized. Reducing time and cost of maintenance and increasing availability and in return profits in form of savings are some of the benefits of the proposed PAAD algorithm.
A **.gov** website belongs to an official government organization in the United States. # 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. 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. The proposed integrated framework addresses several key research gaps by:capturing multi-scale temporal–frequency patterns that are often overlooked by time-domain models;modeling both global and local dependencies in a computationally efficient manner;introducing an interpretable frequency-domain attention mechanism;achieving enhanced nonlinear representation with reduced model complexity; and.integrating an LSSVM-based residual correction mechanism that improves robustness in multi-step forecasting.
This research paper presents a novel approach to wind power prediction, focusing on seasonal analysis and machine learning models. The study addresses short-term wind power forecasting, specifically targeting the prediction of wind power generation at a given location over periods ranging from a few minutes to several days in advance. This study evaluates the performance of two machine learning models, kNN Regression and AdaBoost, across these seasons, providing valuable insights into their effectiveness in wind power prediction. This research contributes to advancing wind power forecasting methodologies by offering a comprehensive analysis of seasonal variations and leveraging machine learning techniques for accurate and reliable predictions. Wind power prediction is the process of forecasting the amount of electricity that can be generated from wind turbines at a given location over a specific period of time, typically ranging from a few minutes to several days in advance. This prediction is crucial for the efficient wind energy integration into the power grid and for ensuring a reliable and stable electricity supply.
Master’s Thesis Active Power Prediction for Wind Turbine Upgrade Evaluation: A Temporal Fusion Transformer Approach Author: Gaspar Lloret Guti´ errez-Col´ on Director: Yolanda Vidal January 2026 Abstract Accurate wind power forecasting is a challenging task due to the stochastic and non-stationary nature of wind and the complex interactions between turbines operating within the same wind farm. This work addresses the problem using a temporal, multivariate modeling approach based on the Temporal Fusion Transformer (TFT), a deep learning architecture designed for heterogeneous time-series forecasting. Based on these insights, a feature engineering pipeline is developed that incorporates operational state indicators, turbulence and volatility metrics, trigonometric representations of angu-lar variables, and cyclical time features. Overall, this study shows that temporal deep learning models, when combined with domain-informed feature engineering and probabilistic forecasting, offer a robust and in-terpretable framework for wind power prediction in stochastic and industrial settings. 4 2 State of the Art 6 2.1 Wind Power Modeling .