风力发电
支持向量机
涡轮机
空气动力学
方位角
计算机科学
风速
风洞
替代模型
人工神经网络
环境科学
海洋工程
人工智能
机器学习
工程类
气象学
航空航天工程
数学
物理
电气工程
几何学
出处
期刊:Energy
[Elsevier]
日期:2023-09-01
卷期号:278: 127940-127940
被引量:3
标识
DOI:10.1016/j.energy.2023.127940
摘要
Wind tunnel experiment is one of the most common research methods of wind turbines. However, the processing of experiment data towards Vertical Axis Wind Turbines (VAWT) are inefficient. In this work, a data-driven surrogated model based on Artificial Neural Networks (ANN), Support Vector Regression (SVR), Gaussian Process Regression (GPR) and Decision Tree Regression (DTR) is proposed to regress the experimental data of aerodynamic forces. Meanwhile, a novel U-typed Darrieus Wind Turbine (UDWT) and two H-typed VAWTs are manufactured in this work. A measurement system combining the magnetic remanence brake, six force sensor, and data processing subsystem is assembled to investigate the aerodynamic forces of wind turbines. A Spin-Down measurement procedure is adopted to collect experiment data in various operating conditions. Results showed that SVM- and GPR-based regression models feature the better fitting ability with R2 larger than 0.99, which can regress the experiment data accurately. ANN-based surrogated model can reproduce the fluctuation of experiment data because of the best learning ability. Aerodynamic forces of UDWT outperform H-type wind turbines at azimuth angle of 90° and 270°.
科研通智能强力驱动
Strongly Powered by AbleSci AI