A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Wake position tracking using dynamic wake meandering model and rotor loads
The wake position is crucial feedback information for closed loop wind farm control. This paper presents a wake center position tracking approach based on turbine rotor loads, which is feasible to track the wake position while the wake is impinging on the downstream wind turbine. The rotor equivalent wind characteristics are estimated based on the blade root bending moments. Furthermore, a wake-wind model is developed to describe the relationship between rotor averaged wind characteristics and wake position, in which the velocity deficit profile of the wake is defined by the Gaussian model. An adaptive extended Kalman filter is proposed to estimate the lateral and vertical wake position states, in which wake dynamics is described by the dynamic wake meandering model. The wake meandering is driven by a colored noise with a covariance matrix that is gain scheduled according to the rotor averaged wind characteristic. Uncertainties of wake position detection are discussed in various combinations of the ambient atmospheric condition. Simulation results show that the proposed tracking method achieves good estimates in low ambient turbulence intensity, while, for a wind condition with high turbulence intensity, relatively longer time averaging needs to be applied to smooth out short-term fluctuations and highlight longer-term wake trends.
Wake position tracking using dynamic wake meandering model and rotor loads
The wake position is crucial feedback information for closed loop wind farm control. This paper presents a wake center position tracking approach based on turbine rotor loads, which is feasible to track the wake position while the wake is impinging on the downstream wind turbine. The rotor equivalent wind characteristics are estimated based on the blade root bending moments. Furthermore, a wake-wind model is developed to describe the relationship between rotor averaged wind characteristics and wake position, in which the velocity deficit profile of the wake is defined by the Gaussian model. An adaptive extended Kalman filter is proposed to estimate the lateral and vertical wake position states, in which wake dynamics is described by the dynamic wake meandering model. The wake meandering is driven by a colored noise with a covariance matrix that is gain scheduled according to the rotor averaged wind characteristic. Uncertainties of wake position detection are discussed in various combinations of the ambient atmospheric condition. Simulation results show that the proposed tracking method achieves good estimates in low ambient turbulence intensity, while, for a wind condition with high turbulence intensity, relatively longer time averaging needs to be applied to smooth out short-term fluctuations and highlight longer-term wake trends.
Wake position tracking using dynamic wake meandering model and rotor loads
Dong, Liang (author) / Lio, Wai Hou (author) / Meng, Fanzhong (author)
2021-03-01
14 pages
Article (Journal)
Electronic Resource
English
A statistical model for wake meandering behind wind turbines
| Elsevier | 2019
Low-frequency dynamic wake meandering: comparison of FAST.Farm and DIWA software tools
| BASE | 2021