Why turbine monitoring isn’t enough
In the early days of wind energy, operators were forced to deal with catastrophic failures with little warning. Inspections performed every few months could catch some issues, some of the time – even today, Operations and Maintenance (O&M) costs account for, on average, 60% of windfarm OPEX.
Monitoring wind turbines is now standard practice – especially offshore. Thanks to advanced cost-effective monitoring hardware, such as ONYX Insight’s ecoCMS, it has even become economical to retrofit monitoring systems onto ageing assets, transforming older windfarms and making them data-rich.
The key to driving down operational expenditure and unlocking additional value from existing wind assets is not just gathering data, however. The principal benefits from monitoring come when operators use wind turbine data to underpin smart predictive maintenance strategies, allowing them to take a calculated view of their O&M activity, cutting total OPEX costs by up to 17%.
The costs of turbine downtime
The core goal of wind turbine O&M is maximising turbine uptime, or availability. This ensures that assets are fully functional during critical energy generation periods. The costs of failing to maximise turbine uptime can be considerable and will increase further with the trend for larger turbines.
The missed profit due to unavailability of a failed turbine can be up to $2,000 per day, based on a 4.2MW turbine. However, the logistical difficulties in some regions of offshore turbine repair mean it can take weeks or months to complete a repair in this environment.
These figures do not include the cost of the repairs, which can quickly mount due to the hazardous and labour-intensive conditions offshore. Offshore maintenance involves specialised crew transfer vessels, cranes, highly skilled technicians and engineers, as well as replacement parts. Sending personnel up wind turbines, whether onshore or offshore, is inherently risky, and should be minimised where possible.
Predictive maintenance – take back control of operations and maintenance budgets
To prevent these costly wind turbine failures, operators need to identify problems before they become more serious. The best way to do this is through predictive maintenance, which strikes a balance between the expensive ‘run to fail’ reactive model of O&M, and preventative maintenance that spends unnecessary money on regularly scheduled replacements.
Predictive maintenance uses advanced digital technologies to enable a more intelligent, strategic method of O&M. Using cost-effective sensors installed in wind turbines to monitor data-streams such as vibration, oil condition and temperature, predictive maintenance providers can train artificial intelligence/machine learning algorithms to spot trends in turbine monitoring data, which could indicate potential problems. Expert engineers then analyse these trends to diagnose issues months before failures occur, preventing further damage to components.
Three key benefits of predictive maintenance
Predictive maintenance has many benefits for wind turbine operators, but three of the most important are extended lead time for repair work, reduced risk of catastrophic failures, and increased operational flexibility.
Extend lead time on repairs by up to 18 months
More lead time on faults means that orders of new components can be placed well ahead of time, reducing costs. Advanced warning of potential problems also means that any repair and replacement work can be consolidated. There are fewer trips for personnel, lower crane hire costs, and less fuel used by vessels transporting personnel and equipment. Using predictive maintenance can deliver savings of up to 30% on a windfarm’s O&M budget.
Significantly reduce the risk of catastrophic failures
Instances where undetected failure of generator bearings result in generator shaft damage can cost an operator an additional $150,000 to repair, and potentially months of lost revenue. Similarly, undetected gearbox defects in parallel stage can result in complete gearbox replacement – something that could easily be avoided by using predictive analytics.
If separate components across a whole turbine are monitored holistically, the resulting data gathered can more effectively protect an asset from failure. Wider, more detailed, and more joined-up data sampling across both turbines themselves entire wind fleets can help to futureproof operations, dramatically slashing the risk of future downtime and catastrophic failures.
Manage turbine performance according to condition and maintenance windows
Using insights on asset health means operators can select an appropriate derating strategy to keep turbines online until a suitable weather window for repairs arrives. Replacing a wind turbine gearbox when the wind is still reduces lost revenue since the unit wouldn’t be generating energy at that time. With the flexibility to deal with problems strategically, O&M departments can maximise turbine uptime and ensure that logistical costs are minimised.
The bottom line – efficiency
Predictive maintenance can deliver an average reduction of three percent on the levelised cost of electricity – vital if wind energy is to maintain its reputation as a reliable and cost-effective source of electricity, particularly against the current backdrop of high energy prices and rising inflation. Investor faith in wind energy is strong. It is incumbent on operators to justify that faith and demonstrate that wind assets are run economically, carefully, and smartly.