Decreasing cost of energy with advanced sensing in wind turbines
Wind energy is increasingly forming part of the world’s renewable energy mix, both for real-time power and for generation of hydrogen as a practical means of energy storage.
Consequently, understanding the failure modes of increasingly large wind turbine generators (WTG) and wind farms can yield significant reductions in operating costs for operators, as well as provide more consistent and continuous power to end-users.
Determining the reliability of WTG components is crucial to both driving improvement and calculating the levelised cost of energy (LCOE), both for an individual turbine and for wind farms as a whole.
Maintenance of off-shore WTGs is expensive, as downtime can be significant due to reliance on specialised crews, coupled with a need for fair weather conditions before inspection or repairs can proceed. This large and unpredictable cost is a significant pressure point for wind farm operators, as well as for the wind industry at large in proving its competitive advantages against other forms of energy production.
Bearings are often the cause of failure in a wind turbine drivetrain. Ricardo’s technology allows OEM and Tier 1 customers to better understand the loading of their bearings (either main bearings or gearbox bearings) and therefore correlate their bearing finite element models during development. This enables early validation of life models and therefore higher confidence in the predicted operations and maintenance costs.
Customers can also monitor their WTG drivelines during operation and schedule preventative maintenance rather than reactive maintenance: offering large operational cost savings.
Ricardo’s ultrasonic sensing technology SensorLifeTM has been further developed, and now allows the direct measurement of bearing raceway deflection and lubricant film thickness, both on test rigs and on WTGs in the field. This uses material properties in conjunction with finite element contact modelling and measured load to provide life predictions that are more accurate than incumbent methods reliant on ISO standards.