Lidar-assisted control (LAC) of wind turbines is a control concept that takes advantage of a nacelle-mounted lidar (a remote sensing device) to measure upstream wind speeds of a turbine to allow a preview of the incoming turbulence. Because the turbine will not be exposed to the identical turbulence as that measured by the lidar in advance, the simulation of a LAC system will be more realistic if wind evolution can be modeled in stochastic wind field simulations. Moreover, application of nacelle-mounted long-range scanning lidars is facing the challenge that the nacelle motion causes deviations in the measuring trajectories. Such lidars are very sensitive to even the slightest trajectory deviation due to its long measuring range. Motivated by these factors, Yiyin Chen’s research mainly focuses on three aspects:
Parameterization of wind evolution models with Doppler wind lidars using machine learning
Wind evolution refers to the evolution of turbulence structures over time, which is commonly quantified using magnitude-squared coherence of wind speeds. A two-parameter wind evolution model modified from a previous study is used to model the estimated coherence. Different machine learning algorithms are investigated for the modeling of wind evolution, including three variants of Gaussian process regression, regression tree, support vector machine regression, and shallow neural network. The investigation focuses on the simulation accuracy and the computational time of models, to provide some insights of the trade-off between these factors.
4D stochastic wind field simulation with integration of wind evolution models
The commonly used 3D stochastic wind field simulation method (Veers method) is extended to 4D to include the modeling of wind evolution along the wind direction. The most novel part is that we propose a two-step Cholesky decomposition approach for the factorization of the coherence matrices so that 4D wind fields can be generated by combining multiple statistically independent 3D wind fields. To enable better integration of the 4D method into the common workflow of wind turbine simulations, we implement the 4D method as the open-access tool evoTurb in combination with TurbSim and Mann turbulence generator.
Adaptive measuring trajectory of scanning lidars
To keep the target measuring trajectories of a lidar unaffected by its motion, we propose the concept of adaptive measuring trajectory to compensate the effect of the rotational motion of the lidar on its target measuring trajectories. The main idea is to add a feedforward control to the control system of the lidar scanning head to correct the deviations of the laser beam direction caused by the rotational motion of the lidar based on lidar motion measured by inertial measurement units. This concept can be understood as a sort of motion compensation on the measuring trajectory level.
- Chen, Yiyin, Wei Yu, Feng Guo und Po Wen Cheng. 2022. Adaptive measuring trajectory for scanning lidars: proof of concept. Journal of Physics: Conference Series 2265, Nr. 2. Journal of Physics: Conference Series (Mai): 022099. doi:10.1088/1742-6596/2265/2/022099, .
- Chen, Yiyin, Feng Guo, David Schlipf und Po Wen Cheng. 2022. Four-dimensional wind field generation for the aeroelastic simulation of wind turbines with lidars. Wind Energy Science 7, Nr. 2. Wind Energy Science: 539--558. doi:10.5194/wes-7-539-2022, .
- Chen, Yiyin, David Schlipf und Po Wen Cheng. 2021. Parameterization of wind evolution using lidar. Wind Energy Science 6, Nr. 1. Wind Energy Science: 61--91. doi:10.5194/wes-6-61-2021, .
- Chen, Yiyin und Po Wen Cheng. 2020. Comparison of different machine learning algorithms for prediction of wind evolution. Journal of Physics: Conference Series 1618. Journal of Physics: Conference Series: 062060. doi:10.1088/1742-6596/1618/6/062060, .
- von der Grün, M., P. Zamre, Y. Chen, T. Lutz, U. Voß und E. Krämer. 2020. Numerical study and LiDAR based validation of the wind field in urban sites. Journal of Physics: Conference Series 1618. Journal of Physics: Conference Series: 042009. doi:10.1088/1742-6596/1618/4/042009, .
- Chen, Yiyin. 2019. Parameterization of wind evolution model using lidar measurement. Wind Energy Science Conference 2019. Wind Energy Science Conference 2019. Cork. doi:10.5281/zenodo.3366119, .
|Since 01/2018||PhD candidate at SWE, University of Stuttgart|
|2015 - 2017||Master of Energy Engineering at University of Stuttgart|
|2016 - 2017||Internship, master thesis and Hiwi at Fraunhofer IEE (formerly IWES) in Kassel|
|2010 - 2014||Bachelor of Energy and Building Technology at Tongji University in Shanghai (2010-2013) and at TH Nuremburg (2013-2014)|