{"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":null,"pages":null},"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\":null,\"pages\":null},\"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}
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.