Research Interests

Education and Work Experience

Research Achievement

Dr. Haiying Sun is an ambitious and enthusiastic researcher aiming to carry out research in wind turbine wake effect. Her innovative work on the three-dimensional wind-turbine wake modelling has significantly advanced the field, providing valuable insights for optimizing wind farm layouts and improving energy yields. The wake model considers the spatial wind distribution, which helps to investigate and estimate the wake effect of wind farm in complex terrain. In order to further study the complex wake phenomena and validate the wake model, she designed and conducted two wind-field experiments in complex-terrain wind farms in Hebei Province (six months) and Shaanxi Province (one year) in China. The experimental data contribute to investigate the complicated wake characteristics, such as wake width, wake centerline, and wake interaction between wind turbines. The wake model has been improved by the experimental data and has been proven able to accurately describe the wake effect of hilly wind farms. Another major contribution of her previous research is the development of a wind turbine layout optimization method to minimize the energy loss of the entire wind farm caused by the wake effect. The optimization method is applicable to both onshore and offshore wind farms, and is more advanced than existing methods because it adopts the foundation repowering strategy for offshore wind turbines. The developed wake model has been integrated into the optimization method, which has further improved the performance of the method. This research has made substantial academic contributions and of significance for the wind energy industry.

Five selected projects:

• 01/2023-12/2025 National Natural Science Foundation of China: Experimental and theoretical investigation on the dynamic wake characteristics of floating wind turbines. (PI)

• 01/2024-12/2025 Guangzhou Science and Technology Bureau under Grant: Study on wake characteristics of offshore wind turbines. (PI)

• 01/2022-12/2023 The Fundamental Research Funds for the Central Universities: Study on the wake characteristics of yawed wind turbines. (PI)

• 04/2024-12/2024 Qingdao Leice Transient Technology Co., Ltd.: Offshore floating platform wind farm measurement and analysis services. (PI)

• 01/2024-04/2024 Power China Huadong Engineering Corporation Limited: Research on risk control and emergency rescue of offshore wind power construction. (PI)   

Five selected papers:

• Sun, H. *, Yang, H., & Gao, X. (2023). Investigation into wind turbine wake effect on complex terrain. Energy, 269, 126767. (Q1, IF= 9.0)

• Sun, H. *, & Yang, H. * (2023). Wind farm layout and hub height optimization with a novel wake model. Applied Energy, 348, 121554. (Q1, IF= 10.1)

• Sun, H. *, Qiu, C., Lu, L., Gao, X., Chen, J., & Yang, H. (2020). Wind turbine power modelling and optimization using artificial neural network with wind field experimental data. Applied Energy, 280, 115880. (Q1, IF= 10.1)

• Sun, H., Gao, X., & Yang, H. * (2020). A review of full-scale wind-field measurements of the wind-turbine wake effect and a measurement of the wake-interaction effect. Renewable and Sustainable Energy Reviews, 132, 110042. (Q1, IF= 16.3)

• Sun, H. *, & Yang, H. (2020). Numerical investigation of the average wind speed of a single wind turbine and development of a novel three-dimensional multiple wind turbine wake model. Renewable Energy, 147, 192-203. (Q1, IF= 9.0)

Editorial Board:

• Young Editorial Board Members of Applied Energy.   

• Review Editor of Frontiers in Energy Research.