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Journal articles on the topic 'Wind farm performance modeling'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Chen, Debin, Binbin Cheng, Dexin Li, Zhouyu Li, Tianye Zhao, and Jiasheng Zhang. "Design and Development of Wind Turbine Performance Analysis System Modeling." Journal of Physics: Conference Series 2179, no. 1 (2022): 012025. http://dx.doi.org/10.1088/1742-6596/2179/1/012025.

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Abstract Wind turbines have been subjected to alternating loads for a long time, and the working environment is harsh, and their performance gradually deteriorates over time. Therefore, it is very necessary to understand the operating status of each component of the wind turbine unit through detailed evaluation of its performance through the monitoring data of the wind turbine unit.This paper designs and develops a wind turbine performance analysis software system based on the related business processes and functional requirements of wind farms. The system can calculate all the indicators rela

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2

Distribution, of weibull, Elyaqouti Mustapha, Bouhouch Lahoussine, and Ihlal Ahmed. "Evaluation of the Energy Performance of the Amougdoul Wind Farm, Morocco." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 2 (2017): 692–705. https://doi.org/10.11591/ijece.v7i2.pp692-705.

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This paper is concerned with the assessment of the the performance of the Amougdoul wind farm. We have determined the Weibull parameters; namely the scale parameter, c (m/s) and shape parameter, k. After that, we have estimated energy output by a wind turbine using two techniques: the useful power calculation method and the method based on the modeling of the power curve, which is respectively 134.5 kW and 194.19 KW corresponding to 27% and 39% of the available wind energy, which confirm that the conversion efficiency does not exceed 40%.

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Smits, Coen-Jan, Jean Gonzalez Silva, Valentin Chabaud, and Riccardo Ferrari. "A FAST.Farm and MATLAB/Simulink interface for wind farm control design." Journal of Physics: Conference Series 2626, no. 1 (2023): 012069. http://dx.doi.org/10.1088/1742-6596/2626/1/012069.

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Abstract Increasing the efficiency of wind farms is important for speeding up the transition from fossil fuels to renewable energy sources. Current wind farm control relies on maximization of power generation of individual turbines. However, research has demonstrated that plant-wide wind farm control could optimize the performance of a wind farm. Wind farm simulation tools are crucial in designing, testing, and validating wind farm controls. FAST.Farm is a recently developed multi-physics engineering tool for modeling wind farm performance by solving the aero-hydro-servoelastic dynamics of eac

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Bhatt, Aditya H., Mireille Rodrigues, Federico Bernardoni, Stefano Leonardi, and Armin Zare. "Stochastic Dynamical Modeling of Wind Farm Turbulence." Energies 16, no. 19 (2023): 6908. http://dx.doi.org/10.3390/en16196908.

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Low-fidelity engineering wake models are often combined with linear superposition laws to predict wake velocities across wind farms under steady atmospheric conditions. While convenient for wind farm planning and long-term performance evaluation, such models are unable to capture the time-varying nature of the waked velocity field, as they are agnostic to the complex aerodynamic interactions among wind turbines and the effects of atmospheric boundary layer turbulence. To account for such effects while remaining amenable to conventional system-theoretic tools for flow estimation and control, we

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Milan, Patrick, Matthias Wächter, and Joachim Peinke. "Stochastic modeling and performance monitoring of wind farm power production." Journal of Renewable and Sustainable Energy 6, no. 3 (2014): 033119. http://dx.doi.org/10.1063/1.4880235.

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Farrell, Alayna, Jennifer King, Caroline Draxl, et al. "Design and analysis of a wake model for spatially heterogeneous flow." Wind Energy Science 6, no. 3 (2021): 737–58. http://dx.doi.org/10.5194/wes-6-737-2021.

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Abstract. Methods of turbine wake modeling are being developed to more accurately account for spatially variant atmospheric conditions within wind farms. Most current wake modeling utilities are designed to apply a uniform flow field to the entire domain of a wind farm. When this method is used, the accuracy of power prediction and wind farm controls can be compromised depending on the flow-field characteristics of a particular area. In an effort to improve strategies of wind farm wake modeling and power prediction, FLOw Redirection and Induction in Steady State (FLORIS) was developed to imple

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Teng, Jian, and Corey D. Markfort. "A Calibration Procedure for an Analytical Wake Model Using Wind Farm Operational Data." Energies 13, no. 14 (2020): 3537. http://dx.doi.org/10.3390/en13143537.

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Wind energy is one of the fastest growing renewable energy sources in the U.S. Wind turbine wakes change the flow field within wind farms and reduce power generation. Prior research has used experimental and computational methods to investigate and model wind farm wake effects. However, these methods are costly and time-consuming to use commercially. In contrast, a simple analytical approach can provide reasonably accurate estimates of wake effects on flow and power. To reducing errors in wake modeling, one must calibrate the model based on a specific wind farm setting. The purpose of this res

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HUSSEIN, ASHRAF S., and HISHAM E. EL-SHISHINY. "MODELING AND SIMULATION OF MICRO-SCALE WIND FARMS USING HIGH PERFORMANCE COMPUTING." International Journal of Computational Methods 09, no. 02 (2012): 1240025. http://dx.doi.org/10.1142/s0219876212400257.

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Onshore wind farms usually consist of numerous horizontal axis wind turbines closely placed in clusters, and they are often cited on complex terrain. This paper proposes a computational framework for modeling and simulation of wind flow over micro-scale (and early meso-scale) wind farms using distributed memory, massively parallel high performance computing platforms. The present framework uses the Reynolds Averaged Navier–Stokes (RANS) to model the wind flow over the wind farms, as the flow is considered to be fully turbulent, isothermal and incompressible. The wind turbines installed in the

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Visbech, Jens, Tuhfe Göçmen, Özge Sinem Özçakmak, Alexander Meyer Forsting, Ásta Hannesdóttir, and Pierre-Elouan Réthoré. "Aerodynamic effects of leading-edge erosion in wind farm flow modeling." Wind Energy Science 9, no. 9 (2024): 1811–26. http://dx.doi.org/10.5194/wes-9-1811-2024.

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Abstract. Leading-edge erosion (LEE) can significantly impact the aerodynamic performance of wind turbines and thereby the overall efficiency of a wind farm. Typically, erosion is modeled for individual turbines where aerodynamic effects only impact the energy production through degraded power curves. For wind farms, aerodynamic deficiency has the potential to also alter wake dynamics, which will affect the overall energy production. The objective of this study is to demonstrate this combined effect by coupling LEE damage prediction and aerodynamic loss modeling with steady-state wind farm flo

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Lefebvre, Pierre, Isabelle Roy, Marc Tremblay, and Sophie Gagnon. "Multi-Scale Numerical Simulation and Optimization Strategies for Wind Farm Layouts in High-Altitude Regions." Energy & System 4, no. 1 (2024): 21–33. https://doi.org/10.71070/es.v4i1.19.

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This study explores the optimization of wind farm layouts in high-altitude regions using a multi-scale numerical simulation approach integrated with advanced optimization strategies. Data were collected from various wind farms in the Tibetan Plateau and the Himalayan region, including wind speed, direction, air density, temperature, and terrain elevation over a five-year period. The research methodology comprised data preprocessing, wind flow modeling via Computational Fluid Dynamics (CFD) and the turbulence model, wind turbine performance modeling based on the Betz limit and Jensen wake model

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Ezzaidi, Asma, Mustapha Elyaqouti, Lahoussine Bouhouch, and Ahmed Ihlal. "Evaluation of the Energy Performance of the Amougdoul Wind Farm, Morocco." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 2 (2017): 692. http://dx.doi.org/10.11591/ijece.v7i2.pp692-705.

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This paper is concerned with the assessment of the the performance of the Amougdoul wind farm. We have determined the Weibull parameters; namely the scale parameter, <em>c</em> (m/s) and shape parameter, <em>k</em>. After that, we have estimated energy output by a wind turbine using two techniques: the useful power calculation method and the method based on the modeling of the power curve, which is respectively 134.5 kW and 194.19 KW corresponding to 27% and 39% of the available wind energy, which confirm that the conversion efficiency does not exceed 40%.

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Andersson, Leif Erik, and Lars Imsland. "Real-time optimization of wind farms using modifier adaptation and machine learning." Wind Energy Science 5, no. 3 (2020): 885–96. http://dx.doi.org/10.5194/wes-5-885-2020.

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Abstract. Coordinated wind farm control takes the interaction between turbines into account and improves the performance of the overall wind farm. Accurate surrogate models are the key to model-based wind farm control. In this article a modifier adaptation approach is proposed to improve surrogate models. The approach exploits plant measurements to estimate and correct the mismatch between the surrogate model and the actual plant. Gaussian process regression, which is a probabilistic nonparametric modeling technique, is used in the identification of the plant–model mismatch. The efficacy of th

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Cañadillas, Beatriz, Maximilian Beckenbauer, Juan J. Trujillo, et al. "Offshore wind farm cluster wakes as observed by long-range-scanning wind lidar measurements and mesoscale modeling." Wind Energy Science 7, no. 3 (2022): 1241–62. http://dx.doi.org/10.5194/wes-7-1241-2022.

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Abstract. As part of the ongoing X-Wakes research project, a 5-month wake-measurement campaign was conducted using a scanning lidar installed amongst a cluster of offshore wind farms in the German Bight. The main objectives of this study are (1) to demonstrate the performance of such a system and thus quantify cluster wake effects reliably and (2) to obtain experimental data to validate the cluster wake effect simulated by the flow models involved in the project. Due to the lack of free wind flow for the wake flow directions, wind speeds obtained from a mesoscale model (without any wind farm p

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José, F. Herbert-Acero, Probst Oliver, Réthoré Pierre-Elouan, Chr. Larsen Gunner, and K. Castillo-Villar Krystel. "A Review of Methodological Approaches for the Design and Optimization of Wind Farms." Energies 7, no. 11 (2014): 6930–7016. https://doi.org/10.3390/en7116930.

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This article presents a review of the state of the art of the Wind Farm Design and Optimization (WFDO) problem. The WFDO problem refers to a set of advanced planning actions needed to extremize the performance of wind farms, which may be composed of a few individual Wind Turbines (WTs) up to thousands of WTs. The WFDO problem has been investigated in different scenarios, with substantial differences in main objectives, modelling assumptions, constraints, and numerical solution methods. The aim of this paper is: (1) to present an exhaustive survey of the literature covering the full span of the

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Mangara, Rajabu Juma, and Laban Lameck Kebacho. "Assessment of Onshore Wind Farm Performance to Geometric Layout Choices by Utilizing Mesoscale Modelling Techniques." Tanzania Journal of Science 50, no. 2 (2024): 334–46. http://dx.doi.org/10.4314/tjs.v50i2.13.

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Previous studies have shown that the average losses of wind power production due to the wind turbine wake effect within operating wind farms is between 10% to 20% of the overall power output. Among other factors, it is reviled that, the wind farm array layout can contribute significantly to both wake effect and power loss at the wind farm site. This study employs mesoscale modelling techniques to assess the effect of geometric layout on the onshore wind farms performance. Geometric layout can be defined by the spacing and alignment (e.g. staggered or aligned) of the wind turbines with respect

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Tian, De, and Hai Hui Song. "Modeling and Simulation on Transformer Computer Protection in Wind Farm." Applied Mechanics and Materials 437 (October 2013): 173–76. http://dx.doi.org/10.4028/www.scientific.net/amm.437.173.

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This paper studies a single machine infinite bus system with two-winding transformer and access 110KV power system after improveing voltage. We established a system model of transformer based on MATLAB/Simulink, and established the computer protection data acquisition system model in wind farm, studied second harmonic ratio brake differential protection. Setted calculation combined with actual differential protection for a given parameter transformer, and use the simulink to simulate test of short-circuit. By the simulation study, we investigated the performance of transformer differential pro

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Zhang, Dichang, Christian Santoni, Zexia Zhang, Dimitris Samaras, and Ali Khosronejad. "Adaptive Multitask Neural Network for High-Fidelity Wake Flow Modeling of Wind Farms." Energies 18, no. 11 (2025): 2897. https://doi.org/10.3390/en18112897.

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Wind turbine wake modeling is critical for the design and optimization of wind farms. Traditional methods often struggle with the trade-off between accuracy and computational cost. Recently, data-driven neural networks have emerged as a promising solution, offering both high fidelity and fast inference speeds. To advance this field, a novel machine learning model has been developed to predict wind farm mean flow fields through an adaptive multi-fidelity framework. This model extends transfer-learning-based high-dimensional multi-fidelity modeling to scenarios where varying fidelity levels corr

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Guan, Qixue, Jiarui Han, Keying Geng, and Yueqiu Jiang. "Wind Turbine Performance Evaluation Method Based on Dual Optimization of Power Curves and Health Regions." Applied Sciences 14, no. 13 (2024): 5699. http://dx.doi.org/10.3390/app14135699.

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The wind power curve serves as a critical metric for assessing wind turbine performance. Developing a model based on this curve and evaluating turbine efficiency within a defined health region, derived from the statically optimized power curve, holds significant value for wind farm operations. This paper proposes an optimized wind power curve segmentation modeling method based on an improved PCF algorithm to address the inconsistency between the function curve and the wind power curve, as well as the issues of prolonged curve modeling training time and susceptibility to local optima. A health

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Tabas, Daniel, Jiannong Fang, and Fernando Porté-Agel. "Wind Energy Prediction in Highly Complex Terrain by Computational Fluid Dynamics." Energies 12, no. 7 (2019): 1311. http://dx.doi.org/10.3390/en12071311.

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With rising levels of wind power penetration in global electricity production, the relevance of wind power prediction is growing. More accurate forecasts reduce the required total amount of energy reserve capacity needed to ensure grid reliability and the risk of penalty for wind farm operators. This study analyzes the Computational Fluid Dynamics (CFD) software WindSim regarding its ability to perform accurate wind power predictions in complex terrain. Simulations of the wind field and wind farm power output in the Swiss Jura Mountains at the location of the Juvent Wind Farm during winter wer

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Barthelmie, R. J., S. C. Pryor, S. T. Frandsen, et al. "Quantifying the Impact of Wind Turbine Wakes on Power Output at Offshore Wind Farms." Journal of Atmospheric and Oceanic Technology 27, no. 8 (2010): 1302–17. http://dx.doi.org/10.1175/2010jtecha1398.1.

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Abstract There is an urgent need to develop and optimize tools for designing large wind farm arrays for deployment offshore. This research is focused on improving the understanding of, and modeling of, wind turbine wakes in order to make more accurate power output predictions for large offshore wind farms. Detailed data ensembles of power losses due to wakes at the large wind farms at Nysted and Horns Rev are presented and analyzed. Differences in turbine spacing (10.5 versus 7 rotor diameters) are not differentiable in wake-related power losses from the two wind farms. This is partly due to t

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Arthur, Robert S., Jeffrey D. Mirocha, Nikola Marjanovic, et al. "Multi-Scale Simulation of Wind Farm Performance during a Frontal Passage." Atmosphere 11, no. 3 (2020): 245. http://dx.doi.org/10.3390/atmos11030245.

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Predicting the response of wind farms to changing flow conditions is necessary for optimal design and operation. In this work, simulation and analysis of a frontal passage through a utility scale wind farm is achieved for the first time using a seamless multi-scale modeling approach. A generalized actuator disk (GAD) wind turbine model is used to represent turbine–flow interaction, and results are compared to novel radar observations during the frontal passage. The Weather Research and Forecasting (WRF) model is employed with a nested grid setup that allows for coupling between multi-scale atm

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El-Tawab*, Sally, Ibrahim Nassar, and Mohammed Mehanna. "Hybrid DFIG Driven Wind Turbine – Grid Systems Modeling and Control for Reliable Source." International Journal of Recent Technology and Engineering (IJRTE) 8, no. 4 (2019): 400–409. http://dx.doi.org/10.35940/ijrte.d69602.118419.

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This paper proposes an approach for modeling the wind turbines and designing a control methodology. Look up table of the optimal power is achieved based on maximum power point tracking (MPPT) profile for maximizing system effectiveness. A 5*2 MW and 690 V wind farm connected with IEEE 14 bus network is the suggested system. The wind energy conversion systems (WECS) are associated with the network through Voltage Source Converters (VSC). The doubly-fed induction generator (DFIG) is used, due to its ability to work at multiple operational speeds. DFIG vector control model is used for the system

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Lingad, M. V., M. Rodrigues, S. Leonardi, and A. Zare. "Three-dimensional stochastic dynamical modeling for wind farm flow estimation." Journal of Physics: Conference Series 2767, no. 5 (2024): 052065. http://dx.doi.org/10.1088/1742-6596/2767/5/052065.

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Abstract Modifying turbine blade pitch, generator torque, and nacelle direction (yaw) are conventional approaches for enhancing energy output and alleviating structural loads. However, the efficacy of such methods is challenged by the lag in adjusting such settings after atmospheric variations are detected. Without reliable short-term wind forecasting tools, current practice, which mostly relies on data collected at or just behind turbines, can result in sub-optimal performance. Data-assimilation strategies can achieve real-time wind forecasting capabilities by correcting model-based predictio

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Sebastiani, Alessandro, James Bleeg, and Alfredo Peña. "A method to correct for the effect of blockage and wakes on power performance measurements." Wind Energy Science 8, no. 12 (2023): 1795–808. http://dx.doi.org/10.5194/wes-8-1795-2023.

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Abstract. Wind turbine power performance measurements often occur at the perimeter of a wind farm, where the wind flow is subject to blockage effects, which might impact the measured power performance. We perform Reynolds-averaged Navier–Stokes simulations of a wind farm with five rows of 20 turbines each, operating in a conventionally neutral boundary layer, to evaluate whether the power performances measured for turbines in the upstream row would differ from that of a turbine operating in isolation under the same inflow conditions. We simulate the power performance measurements with both met

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Scheurich, Frank, Emil Hedevang, Miguel López-Caballero, et al. "Time-Series Based Surrogate Model For Wind Farm Performance Prediction." Journal of Physics: Conference Series 2767, no. 9 (2024): 092001. http://dx.doi.org/10.1088/1742-6596/2767/9/092001.

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Abstract Aeroelastic codes are state-of-the-art simulation tools in both industry and academia for the modelling of wind turbine loads and power output. Although these codes are widely used for the analysis of individual turbines, they are in general computationally too expensive for the calculation of all turbines within a wind farm. Engineering models that are computationally cheaper but also provide a lower fidelity are therefore typically used for wind farm power performance predictions. In this paper, an alternative approach to simulate wind farm performance is presented: the use of a dat

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Kethavath, Naveen N., and Niranjan S. Ghaisas. "Evaluation of wind farm performance over heterogeneously rough terrain using large eddy simulation." Journal of Physics: Conference Series 2767, no. 9 (2024): 092016. http://dx.doi.org/10.1088/1742-6596/2767/9/092016.

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Abstract We evaluate the effect of an abrupt change in the surface aerodynamic roughness height on a wind farm sited on it using the large eddy simulation (LES). Compared to a wind farm sited on a uniformly rough surface, the alteration in aerodynamic surface roughness from a rough to smooth value leads to substantial changes in the first-order and second-order turbulent statistics. Specifically, the rough-to-smooth surface roughness transition leads to an acceleration of the flow downstream of it, which affects the wake recovery and wind farm power production. Different velocity deficits are

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Wei, Dong Ning, Xue Min Zhang, and Jian Min Ye. "Modelling Wind Farm with Doubly Fed Induction Generators Based on Characteristic Fusion." Applied Mechanics and Materials 494-495 (February 2014): 1820–24. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.1820.

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In this paper, a novel modelling approach based on characteristic fusion is proposed and used to build a static equivalent model of wind farm. Firstly, the modelling framework based on characteristic fusion is given. Secondly, the basic characteristics of wind farm including characteristic of wind turbine generator (WTG), wind speed spatial distribution and characteristic of wind farm are analyzed according to the framework. Then detailed modelling process is provided utilizing SVR as a fusion tool. This approach combines the advantages of both mechanism and non-mechanism methods with both sat

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Guo, Zhenhai, and Xia Xiao. "Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods." Abstract and Applied Analysis 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/941648.

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The accurate assessment of wind power potential requires not only the detailed knowledge of the local wind resource but also an equivalent power curve with good effect for a local wind farm. Although the probability distribution functions (pdfs) of the wind speed are commonly used, their seemingly good performance for distribution may not always translate into an accurate assessment of power generation. This paper contributes to the development of wind power assessment based on the wind speed simulation of weather research and forecasting (WRF) and two improved power curve modeling methods. Th

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Becker, Marcus, Maxime Lejeune, Philippe Chatelain, Dries Allaerts, Rafael Mudafort, and Jan-Willem van Wingerden. "A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies." Wind Energy Science 10, no. 6 (2025): 1055–75. https://doi.org/10.5194/wes-10-1055-2025.

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Abstract. Wind farm flow control (WFFC) is the discipline of manipulating the flow between wind turbines to achieve a farm-wide goal, like power maximization, power tracking or load mitigation. Specifically, steady-state control approaches have shown promising results in both theory and practice for power maximization. But how are they expected to perform in a dynamically changing environment? This paper presents an open-source wake modeling framework called OFF (abbreviated from the models OnWARDS, FLORIDyn and FLORIS). It allows the approximation of the performance of WFFC strategies in resp

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Storey, Rupert, and Rene Rauffus. "Mesoscale-coupled Large Eddy Simulation for Wind Resource Assessment." Journal of Physics: Conference Series 2767, no. 5 (2024): 052040. http://dx.doi.org/10.1088/1742-6596/2767/5/052040.

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Abstract Turbulence, a key driver of wind turbine loads, is central in the assessment of turbine suitability and performance, and consequently impacts the expected energy production of a wind farm. Conventional flow modeling methods for wind resource assessment (WRA) typically lack the ability to resolve turbulence due to key simplifications in their formulation. This work applies Large Eddy Simulation (LES) to address these limitations and deliver high-fidelity wind condition predictions at 24 wind farm sites. The model includes recently improved boundary conditions to downscale information f

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Basu, Sudipta Lal, Breiffni Fitzgerald, and Biswajit Basu. "Estimating the performance of wind turbines misaligned with respect to the wind direction using a simplified CFD based approach." Journal of Physics: Conference Series 2767, no. 2 (2024): 022039. http://dx.doi.org/10.1088/1742-6596/2767/2/022039.

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Abstract Wind direction in atmospheric boundary layer changes constantly. As such, the power extracted from the wind turbines in a wind farm is subjected to change. This change in wind direction impacts the wake pattern which in turn affects the power output of the downstream wind turbines. Empirical wind farm models are useful in such cases to assess the overall performance of the wind farms. However, the results provided by these models can be made more useful if fluid-structure interaction (FSI) effects are also considered. In the present work, we begin by looking at the potential flow mode

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Tao, Sichen, Sicheng Liu, Ruihan Zhao, Yifei Yang, Hiroyoshi Todo, and Haichuan Yang. "A State-of-the-Art Fractional Order-Driven Differential Evolution for Wind Farm Layout Optimization." Mathematics 13, no. 2 (2025): 282. https://doi.org/10.3390/math13020282.

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The wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency and mitigate energy losses caused by wake effects by optimizing the spatial layout of wind turbines. Although Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been widely used in WFLOP due to their discrete optimization characteristics, they still have limitations in global exploration capability and optimization depth. Meanwhile, the Differential Evolution algorithm (DE), known for its strong global optimization ability and excellent performance in handling complex nonlinea

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Tao, Sichen, Yifei Yang, Ruihan Zhao, Hiroyoshi Todo, and Zheng Tang. "Competitive Elimination Improved Differential Evolution for Wind Farm Layout Optimization Problems." Mathematics 12, no. 23 (2024): 3762. http://dx.doi.org/10.3390/math12233762.

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The wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency under different wind conditions by optimizing the spatial layout of wind turbines to fully mitigate energy losses caused by wake effects. Some high-performance continuous optimization methods, such as differential evolution (DE) variants, exhibit limited performance when directly applied due to WFLOP’s discrete nature. Therefore, metaheuristic algorithms with inherent discrete characteristics like genetic algorithms (GAs) and particle swarm optimization (PSO) have been extensively developed in

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Li, Shuyu, Rui Kang, Meilin Wen, and Tianpei Zu. "Belief Reliability Modeling Method for Wind Farms Considering Two-Directional Rotor Equivalent Wind Speed." Symmetry 16, no. 5 (2024): 614. http://dx.doi.org/10.3390/sym16050614.

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Compared to conventional energy sources, wind power is a clean energy source with high intermittence and uncertainty. As a system that converts wind energy into electricity, wind farms inevitably face severe reliability issues. In this paper, based on reliability theory, a new reliability modeling method for wind farms is proposed. Firstly, a belief reliability model for wind farms is constructed. Then, a power generation model based on two-directional rotor equivalent wind speed is established to represent the wind farm performance in the belief reliability model. Finally, several numerical s

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Ollier, Sarah J., and Simon J. Watson. "Modelling the impact of trapped lee waves on offshore wind farm power output." Wind Energy Science 8, no. 7 (2023): 1179–200. http://dx.doi.org/10.5194/wes-8-1179-2023.

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Abstract. Mesoscale meteorological phenomena, including atmospheric gravity waves (AGWs) and including trapped lee waves (TLWs), can result from flow over topography or coastal transition in the presence of stable atmospheric stratification, particularly with strong capping inversions. Satellite images show that topographically forced TLWs frequently occur around near-coastal offshore wind farms. Yet current understanding of how they interact with individual turbines and whole farm energy output is limited. This parametric study investigates the potential impact of TLWs on a UK near-coastal of

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Gomaa, Mohamed R., Hegazy Rezk, Ramadan J. Mustafa, and Mujahed Al-Dhaifallah. "Evaluating the Environmental Impacts and Energy Performance of a Wind Farm System Utilizing the Life-Cycle Assessment Method: A Practical Case Study." Energies 12, no. 17 (2019): 3263. http://dx.doi.org/10.3390/en12173263.

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The ever-increasing popularity of finding alternative forms of renewable energy has seen an increased interest and utilization of wind energy. The objective of this research therefore, is to evaluate the environmental impacts and energy performance of wind farms. This study was operationalized in Jordan using a life-cycle assessment (LCA) method. The environmental impact is evaluated through lifecycle emissions that include all emissions during various phases of the project. The energy performance is illustrated by the energy indicators. The latter is the energy payback ratio (EPR) and the ene

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Tiriolo, L., R. C. Torcasio, S. Montesanti, et al. "Forecasting wind power production from a wind farm using the RAMS model." Advances in Science and Research 12, no. 1 (2015): 37–44. http://dx.doi.org/10.5194/asr-12-37-2015.

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Abstract. The importance of wind power forecast is commonly recognized because it represents a useful tool for grid integration and facilitates the energy trading. This work considers an example of power forecast for a wind farm in the Apennines in Central Italy. The orography around the site is complex and the horizontal resolution of the wind forecast has an important role. To explore this point we compared the performance of two 48 h wind power forecasts using the winds predicted by the Regional Atmospheric Modeling System (RAMS) for the year 2011. The two forecasts differ only for the hori

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Ahyaten, Sabra, and Jalal El Bahaoui. "Modeling of Wind Turbines Based on DFIG Generator." Proceedings 63, no. 1 (2020): 16. http://dx.doi.org/10.3390/proceedings2020063016.

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The role of wind energy is so promising as a source of future energy all over the world. However, whether the unpredictable nature of wind speed fluctuations and the stability of the power systems be affected by a high penetration of wind power remains an unanswered question. Therefore, an accurate analysis study of the effectiveness and robustness performance of the doubly fed induction generator (DFIG) is one of the challenges in wind turbine applications. The present works tackle the issue of grid connection of DFIG modeling and the dynamic operation to evaluate the capabilities and their i

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Shapiro, Carl R., Johan Meyers, Charles Meneveau, and Dennice F. Gayme. "Wind farms providing secondary frequency regulation: evaluating the performance of model-based receding horizon control." Wind Energy Science 3, no. 1 (2018): 11–24. http://dx.doi.org/10.5194/wes-3-11-2018.

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Abstract. This paper is an extended version of our paper presented at the 2016 TORQUE conference (Shapiro et al., 2016). We investigate the use of wind farms to provide secondary frequency regulation for a power grid using a model-based receding horizon control framework. In order to enable real-time implementation, the control actions are computed based on a time-varying one-dimensional wake model. This model describes wake advection and wake interactions, both of which play an important role in wind farm power production. In order to test the control strategy, it is implemented in a large-ed

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Yang, Jia, and Wen Jun Qi. "Effect of Extreme Temperature on the Performance of Wind Turbine Blade." Key Engineering Materials 522 (August 2012): 457–61. http://dx.doi.org/10.4028/www.scientific.net/kem.522.457.

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According to the Dabancheng wing farm weather conditions from 1971 to 2000, and its extreme temperature conditions, the performance change of 750KW wind turbine blade with them was researched in the article. Import UG Modeling to ANSYS, then achieve the static structural analysis and the heat-structure interaction analysis respectively. The results show that comparing aerodynamic-temperature loads with aerodynamic loads, the stress, strain and displacement of the blade is increased by 13.89%, 10.29% and 0.20%. Therefore, temperature changes have a certain impact on the blade performance. In th

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Liew, Jaime, Kirby Heck, and Michael F. Howland. "Enhanced Modeling of Joint Yaw and Axial Induction Control Using Blade Element Momentum Methods." Journal of Physics: Conference Series 2767, no. 3 (2024): 032018. http://dx.doi.org/10.1088/1742-6596/2767/3/032018.

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Abstract Wind turbine control via concurrent yaw misalignment and axial induction control has demonstrated potential for improving wind farm power output and mitigating structural loads. However, the complex aerodynamic interplay between these two effects requires deeper investigation. This study presents a modified blade element momentum (BEM) model that matches rotor-averaged quantities to an actuator disk model of yawed rotor induction, enabling analysis of joint yaw-induction control using realistic turbine control inputs. The BEM approach reveals that common torque control strategies such

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Turner, Richard, Xiaogu Zheng, Neil Gordon, et al. "Creating Synthetic Wind Speed Time Series for 15 New Zealand Wind Farms." Journal of Applied Meteorology and Climatology 50, no. 12 (2011): 2394–409. http://dx.doi.org/10.1175/2011jamc2668.1.

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AbstractWind data at time scales from 10 min to 1 h are an important input for modeling the performance of wind farms and their impact on many countries’ national electricity systems. Planners need long-term realistic (i.e., meteorologically spatially and temporally consistent) wind-farm data for projects studying how best to integrate wind power into the national electricity grid. In New Zealand, wind data recorded at wind farms are confidential for commercial reasons, however, and publicly available wind data records are for sites that are often not representative of or are distant from wind

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Dallas, Scott, Adam Stock, and Edward Hart. "Control-oriented modelling of wind direction variability." Wind Energy Science 9, no. 4 (2024): 841–67. http://dx.doi.org/10.5194/wes-9-841-2024.

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Abstract. Wind direction variability significantly affects the performance and lifetime of wind turbines and wind farms. Accurately modelling wind direction variability and understanding the effects of yaw misalignment are critical towards designing better wind turbine yaw and wind farm flow controllers. This review focuses on control-oriented modelling of wind direction variability, which is an approach that aims to capture the dynamics of wind direction variability for improving controller performance over a complete set of farm flow scenarios, performing iterative controller development and

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Andersen, Søren Juhl, Simon-Philippe Breton, Björn Witha, Stefan Ivanell, and Jens Nørkær Sørensen. "Global trends in the performance of large wind farms based on high-fidelity simulations." Wind Energy Science 5, no. 4 (2020): 1689–703. http://dx.doi.org/10.5194/wes-5-1689-2020.

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Abstract. A total of 18 high-fidelity simulations of large wind farms have been performed by three different institutions using various inflow conditions and simulation setups. The setups differ in how the atmospheric turbulence, wind shear and wind turbine rotors are modeled, encompassing a wide range of commonly used modeling methods within the large eddy simulation (LES) framework. Various turbine spacings, atmospheric turbulence intensity levels and incoming wind velocities are considered. The work performed is part of the International Energy Agency (IEA) wind task Wakebench and is a cont

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Li, Kang Yi, Chao Lu, Xiao Zhen Zhang, and Miao Yu. "Dynamic Modeling and Simulation of Doubly-Fed VSCF Wind Generator Based on PSCAD/EMTDC." Advanced Materials Research 608-609 (December 2012): 748–54. http://dx.doi.org/10.4028/www.scientific.net/amr.608-609.748.

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In order to build an aggregated wind farm model to research on the effect large scale wind power has on the transient performance of the power system, firstly, it is necessary to build an effective WTGs model. Based on the electro-magnetic transient simulation platform PSCAD/EMTDC, this paper built models of all the parts of Doubly-fed type VSCF wind generator, including wind model, wind turbine model, rotation system model, detailed converters model, and emphatically expounded the implementation process of the control strategy based on the stator flux vector oriented principle. Then through t

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Kaboli, Seyed Amir, and Reyhaneh Nazmabadi. "Techno-economic analysis and modelling of the feasibility of wind energy in Kuwait." Renewable Energy and Environmental Sustainability 7 (2022): 9. http://dx.doi.org/10.1051/rees/2021056.

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There continues to be significant attention and investment in wind power generation, which can supply a high percentage of the global demand for renewable energy if harvested efficiently. The research study is based on techno-economic analysis of the feasibility of implementing wind power generation in Kuwait with a power generation capacity of 105 MW based on 50 wind turbines, which has a major requirement for clean energy. The study focused on three main areas of analysis and numerical modeling using the RETScreen software tool. The first area involved evaluating the performance and efficacy

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Anagnostopoulos, S., and MD Piggott. "Offshore wind farm wake modelling using deep feed forward neural networks for active yaw control and layout optimisation." Journal of Physics: Conference Series 2151, no. 1 (2022): 012011. http://dx.doi.org/10.1088/1742-6596/2151/1/012011.

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Abstract Offshore wind farm modelling has been an area of rapidly increasing interest over the last two decades, with numerous analytical as well as computational-based approaches developed, in an attempt to produce designs that improve wind farm efficiency in power production. This work presents a Machine Learning (ML) framework for the rapid modelling of wind farm flow fields, using a Deep Neural Network (DNN) neural network architecture, trained here on approximate turbine wake fields, calculated on the state-of-the-art wind farm modelling software FLORIS. The constructed neural model is ca

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Shao, Zhenzhou, Ying Wu, Li Li, Shuang Han, and Yongqian Liu. "Multiple Wind Turbine Wakes Modeling Considering the Faster Wake Recovery in Overlapped Wakes." Energies 12, no. 4 (2019): 680. http://dx.doi.org/10.3390/en12040680.

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In a wind farm some wind turbines may be affected by multiple upwind wakes. The commonly used approach in engineering to simulate the interaction effect of different wakes is to combine the single analytical wake model and the interaction model. The higher turbulence level and shear stress profile generated by upwind turbines in the superposed area leads to faster wake recovery. The existing interaction models are all analytical models based on some simple assumptions of superposition, which cannot characterize this phenomenon. Therefore, in this study, a mixing coefficient is introduced into

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Ibrahim, Omar M. A. M., Shigeo Yoshida, Masahiro Hamasaki, and Ao Takada. "Wind Turbine Wake Modeling in Accelerating Wind Field: A Preliminary Study on a Two-Dimensional Hill." Fluids 4, no. 3 (2019): 153. http://dx.doi.org/10.3390/fluids4030153.

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Complex terrain can influence wind turbine wakes and wind speed profiles in a wind farm. Consequently, predicting the performance of wind turbines and energy production over complex terrain is more difficult than it is over flat terrain. In this preliminary study, an engineering wake model, that considers acceleration on a two-dimensional hill, was developed based on the momentum theory. The model consists of the wake width and wake wind speed. The equation to calculate the rotor thrust, which is calculated by the wake wind speed profiles, was also formulated. Then, a wind-tunnel test was perf

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Moens, Maud, Maxime Lejeune, and Philippe Chatelain. "An advanced farm flow estimator for the real-time evaluation of the potential wind power of a down-regulated wind farm." Journal of Physics: Conference Series 2767, no. 3 (2024): 032044. http://dx.doi.org/10.1088/1742-6596/2767/3/032044.

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Abstract This work aims at verifying the predictions of OnWaRDS, an open-source wake modelling framework that captures the main features of the wake dynamics, including its meandering, in ancillary services scenarios. OnWaRDS brings together Lagrangian flow modeling and flow sensing and runs in parallel with a wind farm environment (here Large-Eddy simulations coupled to an Actuator Disk model (LES-AD)) in order to use the available rotor states to predict the flow field. The performances of OnWaRDS are first assessed when it runs synchronously with the LES-AD of a down-regulated wind farm and

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