  Various types of measurement techniques, such as Light Detection and Ranging
(LiDAR) devices, anemometers, and wind vanes, are extensively utilized in wind
energy to characterize the inflow. However, these methods typically gather data
at limited points within local wind fields, capturing only a fraction of the
wind field's characteristics at wind turbine sites, thus hindering detailed
wind field analysis. This study introduces a framework using Physics-informed
Neural Networks to assimilate diverse sensor data types. This includes
line-of-sight wind speed, velocity magnitude and direction, velocity
components, and pressure. Moreover, the parameterized Navier-Stokes equations
are integrated as physical constraints, ensuring that the neural networks
accurately represent atmospheric flow dynamics. The framework accounts for the
turbulent nature of atmospheric boundary layer flow by including artificial
eddy viscosity in the network outputs, enhancing the model's ability to learn
and accurately depict large-scale flow structures. The reconstructed flow field
and the effective wind speed are in good agreement with the actual data.
Furthermore, a transfer learning strategy is employed for the online deployment
of pre-trained PINN, which requires less time than that of the actual physical
flow. This capability allows the framework to reconstruct wind flow fields in
real time based on live data. In the demo cases, the maximum error between the
effective wind speed reconstructed online and the actual value at the wind
turbine site is only 3.7%. The proposed data assimilation framework provides a
universal tool for reconstructing spatiotemporal wind flow fields using various
measurement data. Additionally, it presents a viable approach for the online
assimilation of real-time measurements. To facilitate the utilization of wind
energy, our framework's source code is openly accessible.
