Wake Properties Prediction by means of machine learning algorithms

Wind power has become the most successful renewable energy and represents the second-largest power generation capacity (175 GW) in Europe after natural gas. At the end of 2019, there was an installed accumulative wind power capacity in the UK of 23,513 MW of wind power, generating more than 50 TWh of energy.
Wake steering is a control strategy that can increase wind farm performance. There are multiple recent and ongoing studies, showing that yawing upwind turbines can deliver more power for the downwind turbines due to reduction of wake deficits. Similar effect can potentially be achieved using derating of upwind turbines, or by a combination of yaw control and derating.
In order to robustly apply wake steering or wind turbine derating, it is necessary to know whether the turbine is actually experiencing any detrimental wake effects and the relative location of the wake disturbance so that appropriate control action can be carried out.
There are multiple examples of how wake characteristics have been identified using nacelle-mounted lidars and other remote sensing technologies. The results are promising however they require additional devices (lidars) which increase cost and complexity if it were to be installed on each turbine. Further issues are relatively low data availability and the requirements for data processing. Furthermore it would be preferable for prediction can be carried out with standard sensor setups.
Wind inflow conditions such as wind shear and yaw misalignment have been successfully characterized with turbine-mounted sensors, e.g. strain gages, supplemented with accelerometer data and SCADA signals such as rpm and power. Since the wind profiles caused by wake deficits represent somewhat similar phenomena to wind shear and other variations of wind speed and turbulence over the rotor, it is expected that wake effects will cause similar variations in the load harmonics.
Machine learning has been successfully applied for various modelling and detection purposes in wind turbines, with the greatest focus by far being power output prediction and detection of faults. For wake properties prediction, the problem is similar to fault detection since patterns in the signal need to be identified and evaluated as to whether or not they are consistent with normal behavior. Machine Learning is, thus, expected to be the best approach for achieving automated wake properties prediction based on load signals.
The main goal of the project is to devise a method for wind turbine wake properties prediction based on the use of measurement signals from the turbine affected by the wake. The wake properties prediction capability will enable controlling individual wind turbines to maximize wind farm performance. This is achieved through the following specific objectives:
- Devise a Machine Learning-based wake properties prediction algorithm using numerical simulations of wake conditions and wind turbine dynamics
- Analyze feature importance so that relevant inputs are understood and included; additionally dimensionality may be reduced based on individual input contribution to a more accurate prediction. Furthermore understand which inputs are available across turbine fleet, and reflect on these configuration and evaluate in relation to considered models.Carry out field validation of the prediction method by comparison with other prediction capabilities such as nacelle-mounted lidars
- Suggest a control scheme to utilize the wake properties prediction information for improving wind farm performance