Wind turbines are placed in sites that will see different lifetime conditions compared to what they are designed against. This means that the lifetime of a wind farm may be extended depending on the damage profile it has experienced relative to what it was designed against. DamageProspector is a product that predicts the lifetime damage of a wind turbine relative to its design conditions (IEC) using the full set of SCADA data and machine learning techniques.
Power, rotational speed, torque, blade angles, yaw error, wind speed and turbulence, and shut-down events all affect the fatigue life of wind turbines, but classically only wind speed and turbulence intensity is considered in load simulations for lifetime extension. Furthermore, wind speed and turbulence intensity from SCADA data are inaccurate and cannot be trusted alone. What if we could use the full set of SCADA data to help us predict the actual damage incurred by a wind turbine - this is where DamageProspector comes in.
DamageProspector uses machine learning combined with high-frequency aeroelastic simulations to predict accumulated damage. This approach accounts for the unique load history of your turbines, allowing it’s fatigue age relative design assumptions to be defined. The calculated load time-series history provides opportunity for intelligent through-life management to avoid premature asset ageing or to sweat your asset in an informed way. Contact us for more information.