No two blades are the same in terms of the unique signs and patterns of distress, damage and wear-and-tear wrought on these components during the operational lifetime of a wind turbine. Siemens Gamesa’s Morten Vindbjerg, Head of Product Lifecycle Management in the Service Business Unit, discusses how advances in imaging technology, artificial intelligence (AI) and big data are being harnessed in combination with broad operational knowledge to optimise the maintenance of blades.
What are some of the key issues, or challenges wind farm owners/operators face when it comes to maintenance and servicing of blade, which Siemens Gamesa is helping to address?
Blades by default of their placing, are the most challenging component to maintain on a wind turbine and, because they absorb kinetic energy and channel it to the power train, they also work the hardest. Without well-functioning blades, overall turbine performance will be affected, resulting in a reduction in annual energy production (AEP). Extrapolate across a wind farm and profitability can be impacted.
To effectively maintain blades requires their assessment, which needs to be fast and accurate. But then when a fault is identified, what do you do next?
We’re trying to take predictive maintenance a step further. We take images, using ground cameras or drones to effectively establish the severity of a problem, which is the baseline, and apply engineering analysis to forecast when it will need to be addressed or remedied in the future.
The trend is towards bigger and bigger capacity turbines with large rotors and longer blades to capture more wind resource. What are some of the challenges this presents from a blade maintenance and servicing perspective?
As blades grow in size it is important to maintain the same or lower blade tip speed as today, and so the approach in both onshore and offshore wind is to develop turbines that target relatively moderate tip speeds, which means slowing down rotation. As blades move more slowly leading-edge erosion is lessened. We work with a number of different solutions that address leading-edge erosion, some of which are applicable during manufacture and others that are more suitable for retrofitting. Every site has different factors at play that dictate solutions, such as how remote or accessible the site is. Cost is another factor.
In offshore wind the environment is very challenging. The job of a wind turbine technician has become increasingly skilled and complex. Using analysis techniques we’re able to time sequence maintenance jobs and so we can see where some jobs might be taking longer. This allows us to identify and figure out issues and what can be done to ensure technicians can do their maintenance activities in the safest, most efficient – and most cost-effective way.
From Siemens Gamesa’s perspective as a producer of blades, how are blades being designed and manufactured to ensure optimal service life, while reducing maintenance costs and simplify maintenance?
Feedback gained from aging turbines that are 15 years or older has enabled us to design out issues that occurred in older machines. The challenge we now face is balancing the client’s desire for low levelized cost of energy and maintenance costs. You can comply with ever-increasing cost pressures but not for a variable such as the weather. You have forecasts and can run modelling when designing your site but there will be outliers. As an OEM our focus is on providing a solution that is fast and accurate at assessing the current health of the blade.
There is a growing awareness among asset owners of the impact of poorly performing blades on AEP, which can result in a loss of 1-5% on annual energy production and we can have the solutions that can help them forecast when they should be applying a remedy. AEP and cashflow is almost a 1:1 relationship/impact. When asset owners are aware of that they become more interested in blade integrity management, and we can support them with any O&M need for inspections to blade repairs and upgrades.
Looking forward over the next few years, how does Siemens Gamesa see blade maintenance techniques and innovations evolving?
We’ve come a long way from the days of technicians looking at blades with binoculars, then high-res cameras, and manually recording, processing their observations. Our current solution, Visual Based Asset Integrity, uses advanced imaging hardware to take multiple pictures and rolling AI over the top allows us to stitch together images of blades, categorise faults and then apply statistical analysis to predict when an issue may occur in the future, which provides asset owners with the broadest range of solutions and approaches to address it.
In the future there will be a tendency towards more observation through image recording, to provide a kind of continuous visual inspection of the asset. This can then be applied against the statistical model and provide the most accurate maintenance approach. Perhaps a planned maintenance campaign has been based on assessing the rate of erosion predicated on a forecasted amount of rainfall, over a specific period. The reality may be that the site experiences more dry spells and less rain, so less erosion. This constant benchmarking is possible with imaging technology and the possibility of cloud-based computing to store all of this data. Again, innovative technology-based solutions need to be cost-optimised and that is the challenge OEMs need to rise to.
• At , we are committed to providing our customers with the best-in-class blade services and support team: more than 400 dedicated blade experts, as well as engineering and technical support for every step of the way. Our blade technology has already proved that it can be scaled and adapted to fit all of our customer's needs, as we continue working to ensure the everlasting durability of our blades