{"title":"基于SPOD-DNN的风电场尾流分析非侵入式降阶模型","authors":"Zhaoliang Guo, Li Xu, Guanhao Zhou, Kaijun Zhang","doi":"10.1177/0309524X231162638","DOIUrl":null,"url":null,"abstract":"Wind farm wake modeling is of great significance for wind turbine layout optimization design and yaw control strategy. In this work, we combine deep neural network (DNN) with spectral proper orthogonal decomposition (SPOD) to discover dynamic characteristics of wake under different inflow conditions. Then an assessment of the proposed SPOD-DNN surrogate modeling method of parameterized fluid is performed by comparing the predicted results. Meanwhile, we demonstrate the robustness of the SPOD-DNN through a comparison with POD-DNN, where SPOD produces fewer modes than POD but can achieve the same cumulative contribution rate and wake prediction accuracy. In the end, the method is developed to predict the wake of single wind turbine in untrained inflow condition and Wake of six wind turbines with different yaw angles. The results reveals that the model has good generalization performance and can robustly reconstruct the wake of multiple wind turbines in different directions.","PeriodicalId":51570,"journal":{"name":"Wind Engineering","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A non-intrusive reduced-order model for wind farm wake analysis based on SPOD-DNN\",\"authors\":\"Zhaoliang Guo, Li Xu, Guanhao Zhou, Kaijun Zhang\",\"doi\":\"10.1177/0309524X231162638\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Wind farm wake modeling is of great significance for wind turbine layout optimization design and yaw control strategy. In this work, we combine deep neural network (DNN) with spectral proper orthogonal decomposition (SPOD) to discover dynamic characteristics of wake under different inflow conditions. Then an assessment of the proposed SPOD-DNN surrogate modeling method of parameterized fluid is performed by comparing the predicted results. Meanwhile, we demonstrate the robustness of the SPOD-DNN through a comparison with POD-DNN, where SPOD produces fewer modes than POD but can achieve the same cumulative contribution rate and wake prediction accuracy. In the end, the method is developed to predict the wake of single wind turbine in untrained inflow condition and Wake of six wind turbines with different yaw angles. The results reveals that the model has good generalization performance and can robustly reconstruct the wake of multiple wind turbines in different directions.\",\"PeriodicalId\":51570,\"journal\":{\"name\":\"Wind Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2023-04-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Wind Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/0309524X231162638\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wind Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/0309524X231162638","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
A non-intrusive reduced-order model for wind farm wake analysis based on SPOD-DNN
Wind farm wake modeling is of great significance for wind turbine layout optimization design and yaw control strategy. In this work, we combine deep neural network (DNN) with spectral proper orthogonal decomposition (SPOD) to discover dynamic characteristics of wake under different inflow conditions. Then an assessment of the proposed SPOD-DNN surrogate modeling method of parameterized fluid is performed by comparing the predicted results. Meanwhile, we demonstrate the robustness of the SPOD-DNN through a comparison with POD-DNN, where SPOD produces fewer modes than POD but can achieve the same cumulative contribution rate and wake prediction accuracy. In the end, the method is developed to predict the wake of single wind turbine in untrained inflow condition and Wake of six wind turbines with different yaw angles. The results reveals that the model has good generalization performance and can robustly reconstruct the wake of multiple wind turbines in different directions.
期刊介绍:
Having been in continuous publication since 1977, Wind Engineering is the oldest and most authoritative English language journal devoted entirely to the technology of wind energy. Under the direction of a distinguished editor and editorial board, Wind Engineering appears bimonthly with fully refereed contributions from active figures in the field, book notices, and summaries of the more interesting papers from other sources. Papers are published in Wind Engineering on: the aerodynamics of rotors and blades; machine subsystems and components; design; test programmes; power generation and transmission; measuring and recording techniques; installations and applications; and economic, environmental and legal aspects.