Wind Energy最新文献_第8页

Prediction of aerodynamic performance of NREL offshore 5‐MW baseline wind turbine considering power loss at varying wind speeds 考虑不同风速下功率损失的NREL海上5 - MW基线风力涡轮机气动性能预测

IF 4.1 3区工程技术

Wind Energy Pub Date : 2023-03-01 DOI: 10.1002/we.2812

Yu-Hsien Lin, Hsuan‐Kuang Chen, K. Wu

引用次数: 0

On the wake deflection of vertical axis wind turbines by pitched blades 斜叶片对垂直轴风力机尾迹偏转的影响

IF 4.1 3区工程技术

Wind Energy Pub Date : 2023-02-22 DOI: 10.1002/we.2803

Ming Huang, A. Sciacchitano, C. Ferreira

引用次数: 3

Adding wind power to a wind‐rich grid: Evaluating secondary suitability metrics 向风力丰富的电网增加风能：评估二次适用性指标

IF 4.1 3区工程技术

Wind Energy Pub Date : 2023-02-17 DOI: 10.1002/we.2809

N. Pearre, Lukas Swan

引用次数: 0

A Bayesian hierarchical assessment of night shift working for offshore wind farms 海上风电场夜班工作的贝叶斯层次评估

IF 4.1 3区工程技术

Wind Energy Pub Date : 2023-02-14 DOI: 10.1002/we.2806

Fraser Anderson, D. McMillan, R. Dawid, D. García Cava

引用次数: 2

Bayesian uncertainty quantification framework for wake model calibration and validation with historical wind farm power data 基于历史风电场功率数据的尾流模型标定与验证的贝叶斯不确定性量化框架

IF 4.1 3区工程技术

Wind Energy Pub Date : 2023-02-14 DOI: 10.1002/we.2841

F. Aerts, L. Lanzilao, J. Meyers

{"title":"Bayesian uncertainty quantification framework for wake model calibration and validation with historical wind farm power data","authors":"F. Aerts, L. Lanzilao, J. Meyers","doi":"10.1002/we.2841","DOIUrl":"https://doi.org/10.1002/we.2841","url":null,"abstract":"The expected growth in wind energy capacity requires efficient and accurate models for wind farm layout optimization, control, and annual energy predictions. Although analytical wake models are widely used for these applications, several model components must be better understood to improve their accuracy. To this end, we propose a Bayesian uncertainty quantification framework for physics-guided data-driven model enhancement. The framework incorporates turbulence-related aleatoric uncertainty in historical wind farm data, epistemic uncertainty in the empirical parameters, and systematic uncertainty due to unmodelled physics. We apply the framework to the wake expansion parameterization in the Gaussian wake model and employ historical power data of the Westermost Rough offshore wind farm. We find that the framework successfully distinguishes the three sources of uncertainty in the joint posterior distribution of the parameters. On the one hand, the framework allows for wake model calibration by selecting the maximum a posteriori estimators for the empirical parameters. On the other hand, it facilitates model validation by separating the measurement error and the model error distribution. In addition, the model adequacy and the effect of unmodelled physics are assessable via the posterior parameter uncertainty and correlations. Consequently, we believe that the Bayesian uncertainty quantification framework can be used to calibrate and validate existing and upcoming physics-guided models.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47448055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Automatic testbed with a visual motion tracking system for airborne wind energy applications 带有视觉运动跟踪系统的自动测试平台，用于机载风能应用

IF 4.1 3区工程技术

Wind Energy Pub Date : 2023-02-13 DOI: 10.1002/we.2805

I. Castro-Fernández, F. DeLosRíos‐Navarrete, R. Borobia-Moreno, M. Fernández-Jiménez, H. García‐Cousillas, M. Zas‐Bustingorri, A. T. Ghobaissi, F. López‐Vega, K. Best, R. Cavallaro, G. Sanchez-Arriaga

引用次数: 0

Markus Gross' obituary 马库斯·格罗斯的讣告

IF 4.1 3区工程技术

Wind Energy Pub Date : 2023-02-07 DOI: 10.1002/we.2808

V. Magar

引用次数: 0

Can synoptic drivers explain rotor‐area wind conditions and predict energy output? 天气驱动因素能否解释转子区域的风况并预测能量输出？

IF 4.1 3区工程技术

Wind Energy Pub Date : 2023-02-03 DOI: 10.1002/we.2807

J. Coburn, K. Klink

引用次数: 1

Reliability of onshore wind turbines based on linking power curves to failure and maintenance records: A case study in central Spain 基于连接功率曲线与故障和维护记录的陆上风力涡轮机可靠性:西班牙中部的一个案例研究

IF 4.1 3区工程技术

Wind Energy Pub Date : 2023-01-26 DOI: 10.1002/we.2793

Andres J. Sanchez-Fernandez, J. Gónzalez-Sánchez, Íñigo Luna Rodríguez, Félix R. Rodríguez, Javier Sanchez‐Rivero

引用次数: 1

Data‐driven modelling of turbine wake interactions and flow resistance in large wind farms 大型风电场中涡轮尾流相互作用和流动阻力的数据驱动建模

IF 4.1 3区工程技术

Wind Energy Pub Date : 2023-01-04 DOI: 10.1002/we.2851

Andrew C. Kirby, François‐Xavier Briol, T. Dunstan, T. Nishino

{"title":"Data‐driven modelling of turbine wake interactions and flow resistance in large wind farms","authors":"Andrew C. Kirby, François‐Xavier Briol, T. Dunstan, T. Nishino","doi":"10.1002/we.2851","DOIUrl":"https://doi.org/10.1002/we.2851","url":null,"abstract":"Turbine wake and local blockage effects are known to alter wind farm power production in two different ways: (1) by changing the wind speed locally in front of each turbine; and (2) by changing the overall flow resistance in the farm and thus the so-called farm blockage effect. To better predict these effects with low computational costs, we develop data-driven emulators of the `local' or `internal' turbine thrust coefficient $C_T^*$ as a function of turbine layout. We train the model using a multi-fidelity Gaussian Process (GP) regression with a combination of low (engineering wake model) and high-fidelity (Large-Eddy Simulations) simulations of farms with different layouts and wind directions. A large set of low-fidelity data speeds up the learning process and the high-fidelity data ensures a high accuracy. The trained multi-fidelity GP model is shown to give more accurate predictions of $C_T^*$ compared to a standard (single-fidelity) GP regression applied only to a limited set of high-fidelity data. We also use the multi-fidelity GP model of $C_T^*$ with the two-scale momentum theory (Nishino &Dunstan 2020, J. Fluid Mech. 894, A2) to demonstrate that the model can be used to give fast and accurate predictions of large wind farm performance under various mesoscale atmospheric conditions. This new approach could be beneficial for improving annual energy production (AEP) calculations and farm optimisation in the future.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41491244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6