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academia.edu
research
https://www.academia.edu/19071105/Fundamental_time_domain_wind_turbine_models…
Image 6: First page of “Fundamental time–domain wind turbine models for wind power studies”Image 7: PDF Icon. # Fundamental time–domain wind turbine models for wind power studies. “Fundamental Time–Domain Wind Turbine Models for Wind Power Studies.” Renewable Energy, 2007. This paper provides the most basic yet comprehensive timedomain wind turbine model upon which more sophisticated models along with their power and speed control ... 3. A 1.5 MW fixed-speed wind turbine model demonstrates accurate power curve behavior up to rated wind speed of 14 m/s. To study the impact of wind farms on the dynamics of the power system, an important issue is to develop appropriate wind farm models to represent the dynamics of many individual WTGs. This paper presents various dynamic models, including a detailed model and three reduced-order equivalent models, of wind farms with fixed-speed WTGs. These models are developed and compared by simulation studies in the PSCAD/EMTDC environment under different wind velocity and fluctuation conditions as well as gird fault conditions.
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sciencedirect.com
article
https://www.sciencedirect.com/science/article/abs/pii/S0960148106003466
by S Santoso · 2007 · Cited by 165 — This paper provides the most basic yet comprehensive time–domain wind turbine model upon which more sophisticated models along with their power and speed
X
xray.greyb.com
article
https://xray.greyb.com/wind-turbines/modelling-techniques-for-wind-farm-perfo…
The fundamental challenge lies in balancing individual turbine performance against overall farm optimization while accounting for the dynamic nature of wind resources and equipment constraints. Wind Farm Yaw Control System with Wavelet-Based Wind Prediction and Coordinated Turbine Adjustment. Real-time collaborative yaw control of wind farms that maximizes power generation by coordinating wind turbine yaw angles in response to changing wind conditions. This allows rapid and accurate prediction of wind direction and speed variations that can be used for real-time coordinated yaw control to mitigate wake effects between turbines. Intelligent control method and system for wind turbines that optimizes power generation efficiency by using digital twin modeling, data monitoring, and strategy matching to mitigate the effects of variable wind conditions. The method involves: monitoring wind speed with synchronized sensors, generating predicted wind speed, fitting turbine efficiency using digital twin models, matching optimization results with calibrated wind speeds, and stable optimization using averaged wind speeds. Wind Turbine Blade and Yaw Adjustment System with Reinforcement Learning-Based Predictive Control.
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upcommons.upc.edu
research
https://upcommons.upc.edu/bitstreams/f508f692-8aa7-47cf-8070-9a57c110da62/dow…
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 .
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pmc.ncbi.nlm.nih.gov
official
https://pmc.ncbi.nlm.nih.gov/articles/PMC12722771
by J Gao · 2025 · Cited by 2 — This study proposed a multi-module integrated model for wind power forecasting based on time–frequency domain analysis, aiming to enhance prediction accuracy
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asmedigitalcollection.asme.org
article
https://asmedigitalcollection.asme.org/offshoremechanics/article/137/1/011901…
The current paper deals with a study of a semisubmersible wind turbine (WT), where short-term tower base bending moments and tower fatigue damage were.
B
biz.uiowa.edu
research
https://www.biz.uiowa.edu/faculty/nstreet/Renewable_Energy_1.pdf
In this paper, a variety of different approaches, including data mining, evolutionary computation, principal component analysis (PCA), residual approach, and control charts, have been used to build prediction models and characterize power curves of a wind farm by a nonlinear parametric model. Learning parametric model from training data Based on the analysis of the historical data (data set 1), the wind farm power curve is approximated by logistic function [25] (1) y ¼ f ðx; qÞ ¼ a1 þ mex=s 1 þ nex=s ; q ¼ ða; m; n; sÞ (1) where x is the principal component of 89 wind speeds (the pre-dictor in k-NN-P1 model), y is the power of the wind farm, and q ¼ (a, m, n, s) is a 4-dimension vector parameter of logistic function that determines the shape of the power curve. The new training data for the parametric model includes the principal component derived from 89 wind speeds used as x(i) in function (2) and the power predicted by the k-NN-P1 (k ¼ 250) model used as y(i) in function (2).
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mdpi.com
article
https://www.mdpi.com/1996-1073/5/11/4350
by W Dong · 2012 · Cited by 52 — In this study, three problems in time domain based gear contact fatigue analysis under dynamic conditions are discussed: It uses a statistical model analysis