{"title":"Prediction of aerodynamic performance of NREL offshore 5‐MW baseline wind turbine considering power loss at varying wind speeds","authors":"Yu-Hsien Lin, Hsuan‐Kuang Chen, K. Wu","doi":"10.1002/we.2812","DOIUrl":"https://doi.org/10.1002/we.2812","url":null,"abstract":"","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41893983","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}
{"title":"On the wake deflection of vertical axis wind turbines by pitched blades","authors":"Ming Huang, A. Sciacchitano, C. Ferreira","doi":"10.1002/we.2803","DOIUrl":"https://doi.org/10.1002/we.2803","url":null,"abstract":"","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47308811","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}
Fraser Anderson, D. McMillan, R. Dawid, D. García Cava
{"title":"A Bayesian hierarchical assessment of night shift working for offshore wind farms","authors":"Fraser Anderson, D. McMillan, R. Dawid, D. García Cava","doi":"10.1002/we.2806","DOIUrl":"https://doi.org/10.1002/we.2806","url":null,"abstract":"","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":"49595854","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}
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.
风能容量的预期增长需要高效准确的风电场布局优化、控制和年度能源预测模型。虽然分析尾流模型广泛用于这些应用，但必须更好地理解几个模型组件以提高其准确性。为此，我们提出了一个贝叶斯不确定性量化框架，用于物理指导的数据驱动模型增强。该框架结合了历史风电场数据中与湍流相关的任意不确定性，经验参数中的认知不确定性，以及由于未建模物理而导致的系统不确定性。我们将该框架应用于高斯尾流模型的尾流扩展参数化，并采用了west most Rough海上风电场的历史功率数据。我们发现该框架成功地区分了参数联合后验分布中的三种不确定性来源。一方面，该框架允许通过选择经验参数的最大后验估计量来校准尾流模型。另一方面，通过分离测量误差和模型误差分布，便于模型验证。此外，模型的充分性和未建模物理的影响可通过后验参数不确定性和相关性来评估。因此，我们相信贝叶斯不确定性量化框架可以用来校准和验证现有的和即将到来的物理指导模型。
{"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}
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
{"title":"Automatic testbed with a visual motion tracking system for airborne wind energy applications","authors":"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","doi":"10.1002/we.2805","DOIUrl":"https://doi.org/10.1002/we.2805","url":null,"abstract":"","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46142772","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}
Andres J. Sanchez-Fernandez, J. Gónzalez-Sánchez, Íñigo Luna Rodríguez, Félix R. Rodríguez, Javier Sanchez‐Rivero
{"title":"Reliability of onshore wind turbines based on linking power curves to failure and maintenance records: A case study in central Spain","authors":"Andres J. Sanchez-Fernandez, J. Gónzalez-Sánchez, Íñigo Luna Rodríguez, Félix R. Rodríguez, Javier Sanchez‐Rivero","doi":"10.1002/we.2793","DOIUrl":"https://doi.org/10.1002/we.2793","url":null,"abstract":"","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43455679","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}
Andrew C. Kirby, François‐Xavier Briol, T. Dunstan, T. Nishino
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.
涡轮机尾流和局部阻塞效应以两种不同的方式改变风力发电场的发电量:(1)通过改变每个涡轮机前部的局部风速;(2)通过改变农场的整体流动阻力，从而产生所谓的农场阻塞效应。为了以较低的计算成本更好地预测这些影响，我们开发了数据驱动的模拟器，将“局部”或“内部”涡轮推力系数C_T^*$作为涡轮布局的函数。我们使用多保真高斯过程(GP)回归，结合低(工程尾流模型)和高保真(大涡模拟)模拟不同布局和风向的农场来训练模型。大量的低保真度数据加快了学习过程，高保真度数据保证了学习的准确性。与仅应用于有限的高保真数据集的标准(单保真)GP回归相比，经过训练的多保真GP模型显示出更准确的$C_T^*$预测。我们还使用了具有双尺度动量理论的多保真度GP模型(Nishino &Dunstan 2020, J. Fluid Mech. 894, A2)来证明该模型可以用于快速准确地预测各种中尺度大气条件下的大型风电场性能。这种新方法可能有助于改善未来的年能源产量(AEP)计算和农场优化。
{"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}
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