Today wind power represents 4.4% of the total generated power. By 2030, this is to increase up to 20%. The challenges for wind turbine manufacturers are wide-ranging: the aerodynamic performance of the blades, reduce weight, keep noise and vibration levels under control, ensure a durable design and improve its overall system performance.
The gearbox is the most critical part of the wind turbine. Either you send a technician up the turbine and do a manual check, or you attach sensors to the gearbox and monitor the results remotely on a computer. Both approaches work to anticipate failures and allow turbine owners to schedule for repairs. Obviously, this comes at a price. A high price. Can’t this be done more cost-effective?
Predicting the remaining useful lifetime of each wind turbine gearbox
Winergy, a global key provider for wind energy in Germany, teamed up with the Simcenter Engineering experts of Siemens PLM Software to estimate the remaining useful lifetime (RUL) of a complete wind park. Let’s be a bit more specific: 78 wind turbines – 35 SCADA channels – historical data stored over 4 years.
Neural Networks The neural network was fed with information from different SCADA channels on the gearbox in combination with service data. Gearbox temperatures were defined as the most representative signals for a possible failure. Next, the neural network was trained on how a turbine reacts in healthy and faulty conditions. Winergy and Simcenter experts used the technique to accurately predict and detect failures early on.
Digital Twin A digital twin makes the bridge between a virtual representation and the physical product. It helps to understand and predict product performance characteristics. Wind turbine modeling was combined with physical validation measurements in 1 turbine to validate the digital twin model. The digital twin model is fed with historic loads extracted from the SCADA in order to predict the remaining useful lifetime of the bearings and gear teeth in each gearbox.
This combined approach limits the need for physical prototypes, reduces development time, and improves the quality of the finalized product.