作为传感器的风电场:从运行数据中学习和解释地形和植物诱导的流异质性

IF 3.6 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY
R. Braunbehrens, A. Vad, C. Bottasso
{"title":"作为传感器的风电场:从运行数据中学习和解释地形和植物诱导的流异质性","authors":"R. Braunbehrens, A. Vad, C. Bottasso","doi":"10.5194/wes-8-691-2023","DOIUrl":null,"url":null,"abstract":"Abstract. This paper describes a method to identify the heterogenous flow\ncharacteristics that develop within a wind farm in its interaction with the\natmospheric boundary layer. The whole farm is used as a distributed sensor,\nwhich gauges through its wind turbines the flow field developing within\nits boundaries. The proposed method is based on augmenting an engineering\nwake model with an unknown correction field, which results in a hybrid\n(grey-box) model. Operational SCADA (supervisory control and data acquisition) data are then used to simultaneously learn the parameters that describe the correction field and to tune the ones of the engineering wake model. The resulting monolithic maximum likelihood estimation is in general ill-conditioned because of the collinearity and low observability of the redundant parameters. This problem is solved by a singular value decomposition, which discards parameter combinations that are not identifiable given the informational content of the dataset and solves only for the identifiable ones. The farm-as-a-sensor approach is demonstrated on two wind plants with very\ndifferent characteristics: a relatively small onshore farm at a site with\nmoderate terrain complexity and a large offshore one in close proximity to\nthe coastline. In both cases, the data-driven correction and tuning of the\ngrey-box model results in much improved prediction capabilities. The\nidentified flow fields reveal the presence of significant terrain-induced\neffects in the onshore case and of large direction and ambient-condition-dependent intra-plant effects in the offshore one. Analysis of the coordinate transformation and mode shapes generated by the singular value decomposition help explain relevant characteristics of the solution, as well as couplings among modeling parameters. Computational fluid dynamics (CFD) simulations are used for confirming the plausibility of the identified flow fields.\n","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data\",\"authors\":\"R. Braunbehrens, A. Vad, C. Bottasso\",\"doi\":\"10.5194/wes-8-691-2023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract. This paper describes a method to identify the heterogenous flow\\ncharacteristics that develop within a wind farm in its interaction with the\\natmospheric boundary layer. The whole farm is used as a distributed sensor,\\nwhich gauges through its wind turbines the flow field developing within\\nits boundaries. The proposed method is based on augmenting an engineering\\nwake model with an unknown correction field, which results in a hybrid\\n(grey-box) model. Operational SCADA (supervisory control and data acquisition) data are then used to simultaneously learn the parameters that describe the correction field and to tune the ones of the engineering wake model. The resulting monolithic maximum likelihood estimation is in general ill-conditioned because of the collinearity and low observability of the redundant parameters. This problem is solved by a singular value decomposition, which discards parameter combinations that are not identifiable given the informational content of the dataset and solves only for the identifiable ones. The farm-as-a-sensor approach is demonstrated on two wind plants with very\\ndifferent characteristics: a relatively small onshore farm at a site with\\nmoderate terrain complexity and a large offshore one in close proximity to\\nthe coastline. In both cases, the data-driven correction and tuning of the\\ngrey-box model results in much improved prediction capabilities. The\\nidentified flow fields reveal the presence of significant terrain-induced\\neffects in the onshore case and of large direction and ambient-condition-dependent intra-plant effects in the offshore one. Analysis of the coordinate transformation and mode shapes generated by the singular value decomposition help explain relevant characteristics of the solution, as well as couplings among modeling parameters. Computational fluid dynamics (CFD) simulations are used for confirming the plausibility of the identified flow fields.\\n\",\"PeriodicalId\":46540,\"journal\":{\"name\":\"Wind Energy Science\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2023-05-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Wind Energy Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5194/wes-8-691-2023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wind Energy Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5194/wes-8-691-2023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 2

摘要

摘要本文描述了一种识别风电场与大气边界层相互作用中发展的非均质流动特性的方法。整个农场被用作分布式传感器,通过其风力涡轮机测量边界内发展的流场。该方法是基于一个未知修正场的工程尾流模型,从而得到一个混合(灰盒)模型。然后使用操作SCADA(监控和数据采集)数据同时学习描述校正场的参数并调整工程尾流模型的参数。由于冗余参数的共线性和低可观测性,所得到的整体最大似然估计通常是病态的。这个问题是通过奇异值分解来解决的,奇异值分解会丢弃给定数据集的信息内容无法识别的参数组合,只解决可识别的参数组合。这种“农场即传感器”的方法在两个特点截然不同的风电场上得到了验证:一个是相对较小的陆上风电场,位于地形中等复杂的地点,另一个是靠近海岸线的大型海上风电场。在这两种情况下,数据驱动的修正和灰盒模型的调优都大大提高了预测能力。所识别的流场表明,在陆上情况下存在显著的地形诱导效应,在海上情况下存在较大的方向和环境条件依赖的植物内效应。分析奇异值分解产生的坐标变换和模态振型,有助于解释解的相关特征,以及建模参数之间的耦合关系。计算流体力学(CFD)模拟用于验证识别流场的合理性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data
Abstract. This paper describes a method to identify the heterogenous flow characteristics that develop within a wind farm in its interaction with the atmospheric boundary layer. The whole farm is used as a distributed sensor, which gauges through its wind turbines the flow field developing within its boundaries. The proposed method is based on augmenting an engineering wake model with an unknown correction field, which results in a hybrid (grey-box) model. Operational SCADA (supervisory control and data acquisition) data are then used to simultaneously learn the parameters that describe the correction field and to tune the ones of the engineering wake model. The resulting monolithic maximum likelihood estimation is in general ill-conditioned because of the collinearity and low observability of the redundant parameters. This problem is solved by a singular value decomposition, which discards parameter combinations that are not identifiable given the informational content of the dataset and solves only for the identifiable ones. The farm-as-a-sensor approach is demonstrated on two wind plants with very different characteristics: a relatively small onshore farm at a site with moderate terrain complexity and a large offshore one in close proximity to the coastline. In both cases, the data-driven correction and tuning of the grey-box model results in much improved prediction capabilities. The identified flow fields reveal the presence of significant terrain-induced effects in the onshore case and of large direction and ambient-condition-dependent intra-plant effects in the offshore one. Analysis of the coordinate transformation and mode shapes generated by the singular value decomposition help explain relevant characteristics of the solution, as well as couplings among modeling parameters. Computational fluid dynamics (CFD) simulations are used for confirming the plausibility of the identified flow fields.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Wind Energy Science
Wind Energy Science GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY-
CiteScore
6.90
自引率
27.50%
发文量
115
审稿时长
28 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信