Acoustic tomography of temperature and wind flow fields in a wind power plant

Wind power is one of the fastest growing forms of electric power in recent years. However, wind power is inherently subject to weather conditions and very often unstable. It is therefore highly desirable to predict wind speed as accurately and quickly as possible. Among various wind power prediction methods, Physics-based prediction is a relatively new method that requires substantial research work to overcome major difficulties in computational cost and so on. This paper describes a method using acoustic travel-time tomography to simultaneously reconstruct the flow and temperature in a wind field. An algorithm which uses spatial-temporal covariance functions of environment turbulence is used in the specific inverse problem. It allows incorporating tomographic data obtained at different times to estimate the state of the propagation medium. Numerical experiment is carried out in a 2D domain of a wind power plant, which obtains a detailed reconstructed result. It shows that acoustic travel-time tomography is applicable and may become a new approach in wind speed prediction.