A Combined Approach to Wind Profile Prediction
National Grid Electricity System Operator
National Grid TO Innovation Team
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Innovation Funding Incentive
Gas Transmission Networks
Wind profile (including speed and direction) prediction at different scales (short term,
mid-term and long-term) plays a crucial role for efficient operation of wind
turbines and wind power prediction. This problem can be approached in two
different ways: one is based on statistical signal processing techniques and both
linear and nonlinear (such as artificial neural networks) models can be employed
either separately or combined together for profile prediction; on the other hand,
wind / atmospheric flow analysis is a classical problem in Computational Fluid
Dynamics (CFD) in applied mathematics, which employs various numerical
methods and algorithms, although it is an extremely time-consuming
process with high computational complexity.
On the CFD side, in the simulation / prediction of the atmospheric flows on the
surface, one particular difficult regime is the case with stable stratification. Stable
stratification leads to internal gravity waves. The interaction between the waves
and turbulence remains a challenge for the modelling of turbulent atmospheric
flows. Among the various issues, an important one is how to accurately account
for the incoming / outgoing waves in the boundary conditions. If not properly
handled, artificial waves can be generated in the simulations, which could
destabilize the simulations.
On the other hand, the signal process methods developed in Electronic and Electrical Engineering (EEE) at Sheffield are particularly suitable for capturing the wave components in a noisy signal.
Therefore, the synergy between the two approaches can be particularly valuable
for the simulation / prediction of wind profile / atmospheric flows.
The aim of this project is to develop efficient and effective algorithms for wind
profile prediction based on synergies between the signal processing approach
and the computational fluid dynamics approach. One of the main deliverables
will be a PhD thesis which contains the source code and prediction methodology